Generative AI Tools and Platforms: Models, Use Cases, Types & How to Choose the Right One

Generative AI tools and platforms are a constantly growing set of software applications and cloud-based services that empower enterprises and individuals to harness the creative power of artificial intelligence. These tools and platforms build, customize, deploy, and integrate generative AI models into existing systems for more efficient development. Foundation generative AI models themselves – whether used as is or fine-tuned to a specific use case – include large language models (LLMs), diffusion image generators, video generators, music generators, and multimodal models.

The most popular generative AI tools are content generation tools (ChatGPT, Jasper AI, and PanelsAI), image-generation tools (Midjourney, DALL-E, Stable Diffusion), code-generation tools (Copilot), music generation tools (Jukebox), and platform-level services from major cloud providers such as Google Vertex AI, AWS Bedrock, and Azure OpenAI Service. The generative AI platforms from hyperscalers typically provide a full suite of foundation models as well as easy fine-tuning, deployment, and scaling in the cloud. They do this either by adding purpose-built tools on top of their platforms, or through plugins or APIs. Preferably, all of these methods.

There are multiple mechanisms for building and deploying these tools. There are open-source, closed-source, and proprietary model approaches. The foundation model itself can be built from source code, via APIs and SDKs, or plug-and-play integrations.

Generative AI tools and platforms are being rapidly deployed across a wide spectrum of industries and use cases. Deutsche Telekom uses AI-powered chatbots like Frag Magenta to deliver personalized marketing messages and manage customer inquiries around the clock, resolving over a third of issues instantaneously. Bloomberg’s Terminal now features AI-generated earnings-call and news summaries, presenting key takeaways in bullet form to help users digest information faster. Google is leveraging AI-driven real-time shopper journey optimization to reduce cart abandonment retailers lose an estimated $2 trillion globally due to search abandonment, and AI interventions are helping recapture sales by proactively engaging customers. Applications of generative AI now span sectors including telecommunications, finance, e-commerce, and more.

• Entertainment: Virtual characters, personalized content recommendations
• Healthcare: Medical image synthesis, drug discovery, patient data augmentation
• Manufacturing: CAD and product design, digital twins, supply chain optimization
• Retail: Inventory demand prediction, personalized product recommendation
• Financial services: Algorithmic trading, fraud detection, marketing compliance
• Digital marketing and webinars: Enhanced SEO, copywriting, video asset creation, campaign testing

No single tool or platform will be a perfect fit for every use case. If you require deep customization, rapid time-to-market, and third-party services, open-source generative AI tools might be best. If you prioritize ease of use, scalability, and ongoing support, proprietary cloud-based tools are better. APIs give granular control over model operations, whereas SDKs enable tighter integration. Factors worth considering include use case complexity, available technical expertise, budget, scalability needs, security and compliance requirements, as well as integration and workflow compatibility. Vendors for sophisticated deployments must be able to demonstrate a robust set of open APIs, integrations with major third-party systems, and hybrid edge processing support.

Even the most advanced AI platforms require connective tissue often in the form of APIs and automation layers to function meaningfully inside an organization. See real-world integration patterns and cross-stack coordination in our analysis on AI system integration with enterprise workflows.

What Are Generative AI Tools and Platforms?

Generative AI tools and platforms are software solutions that make it easier to develop, deploy, or use generative artificial intelligence (AI) products. These tools include everything from simple text or image generators to entire developer toolkits and platforms used by engineers to fine-tune new models.

Most generative AI platforms focus on consumerizing existing models, making it easy for everyday users to interact with generative artificial intelligence. On the other end of the spectrum, large software platforms make it easier for everyday users to achieve their own objectives through fine-tuning or training large models.

To better understand generative AI platforms and their use cases, one must first understand the core technology. Generative AI, often abbreviated as GenAI, refers to systems that can generate new, realistic content (text, images, music, etc.) based on training data, user instructions, and prompts. The content these tools create is often similar to the dataset they were trained on but new and unique in its details.

Generative AI uses large training sets and deep learning to analyze and understand elements of prior works, which are later assembled to create new works. Most generative AI systems start with generative model neural networks. These are deep-learning-bridge models that map pieces of data appropriately to each other after studying large datasets.

The generative AI platform landscape has become a crowded space. These are some of the earliest and best-known players in the space.

Since generative AI networks are complex datasets, leveraging generative AI can be a complex task with multiple steps including:

• Training and refining generative models: Training models requires significant computational resources and fine-tuning them for higher mastery over specific datasets can require weeks or even months of work for any single model.
• Testing and deploying generative models: Most generative AI models must be tested to determine biases, accuracy, and network hallucinations.
• Providing users and developers access to generative models: Users must have access to a network via web interfaces (APIs), iOS and Android software development kits (SDKs), and directly on user dashboards by being integrated into software.

To help developers and businesses leverage the benefits of generative artificial intelligence, a new software solutions landscape has emerged. These include narrow and specialized generative AI tools as well as more robust and complex generative AI platforms.

If you’re just starting out with generative AI, understanding the foundational models and how they evolved is essential. Our complete guide to Generative AI offers a beginner-friendly overview of architectures, applications, and future trends.

Tools vs Platforms: Key Differences

A simple generative AI tool is a straightforward software application designed to perform a focused, specific task, such as generating text or images using generative models. It provides a user-friendly interface, often in the form of a web or desktop application, through which users can interact with the generative AI model without needing to have any technical expertise. These tools are typically limited in scope and customization options and lack advanced features such as extensibility, APIs, and hosting capabilities.

Some of the key genAI models, such as Stable Diffusion, started out as standalone downloadable command-line executable OR as early browser-based GUIs. However, the majority of users often come in contact with models when using them as one feature or module in a tool, as with the Magic Write feature in the Canva graphic design tool, or Magic Eraser in Adobe Photoshop.

A generative AI tool’s simplicity makes it a good option for casual or non-technical users who want to quickly and easily generate content using AI models without needing to have any technical expertise. However, its limitations may make it unsuitable for more complex or customized use cases, especially in larger enterprises with more complex and critical needs. This is where the versatility of AI platforms and tech stacks shine.

A generative AI platform today offers much more than just individual models it’s a comprehensive infrastructure that supports end-to-end AI development, deployment, and management. Modern platforms integrate large language models like GPT‑4.1, multimodal systems such as GPT‑4 turbo, and enterprise-focused offerings like IBM watsonx. They provide tools for model hosting, fine-tuning with private data, multi-modal input handling, and built-in governance and workflow automation. These platforms empower developers and businesses to build, deploy, and scale AI applications securely and responsibly, ensuring seamless integration across diverse enterprise needs.

Core Functions of AI Tools and Platforms

Though there is a wide variety of generative AI platforms and tools, there are certain core functions that every platform or tool will perform to be useful to users and enterprises. These are the most important ones.

• Model access and deployment: Tools enable users to access pre-trained AI models from leading providers, fine-tune them for specific use cases, and deploy them into production. This eliminates the need to train models from scratch like was done with earlier discriminative AI.
• Model fine-tuning and customization: Tools now provide user-friendly interfaces for users to further train base models with their own data. This improves accuracy and applicability for unique domains versus using generic models.
• Inference hosting: Tools host fine-tuned models for serving production inference requests, handling scaling, security, versioning, and resource management.
• Data integration: Tools make it easier to import and prepare training/supplementary data from databases, documents, images, videos, or text, compared to doing this natively on models. Data prepping is one of the most laborious and time-consuming aspects of data science and model training.
• API integrations: Tools provide APIs and SDKs so apps, websites, and workflows programmatically utilize the AI generative capabilities from platforms.
• Multi-modal support: Advanced AI tools accommodate several content formats including text, images, audio, and video, instead of focusing only on one medium.
• User interfaces: Tools make AI more accessible to non-technical users via intuitive graphical interfaces and natural language input/output.
• Performance optimizations: Platforms optimize the underlying AI model pipelines for latency, throughput, and scaling for enterprise-grade usage.
• Monitoring and governance: Leading platforms provide dashboards, logs, and alerts to monitor usage, accuracy, costs, as well as control access, versions, and data provenance.

EY experts highlight that realizing the full potential of generative AI requires building an integrated enterprise platform. Such a platform enables different business personas structured by function and responsibility to select, onboard, fine-tune models under governance guidelines, and manage the AI lifecycle end-to-end. This approach ensures compliance with trustworthy AI principles and creates a foundation for scalable adoption across the organization.

Types of Generative AI Tools and Platforms

Types of generative AI platforms and tools are classified in numerous ways. Modality (type of content supported) and access model (open-source versus commercial) are the two most important ways of breaking up the market into useful categories.

Types of generative AI tools and platforms based on modality include the following:

• Text generation, handled by large language models, are used in chatbots, autocomplete, summarization, translation, captioning, and social media content. OpenAI GPT-3/4/turbo, BERT (Bidirectional Encoder Representations from Transformers), LaMDA, Jurassic-1, Cohere, Meta’s Llama, and Claude are leading models. ChatGPT’s web interface, Jasper AI’s copywriting interface, and Midjourney’s Discord prompt interface are examples of tools built around platforms.

• Image generation models produce digital art, photorealistic images, graphics, drawings, and more from a text prompt or image. Leading generative AI image models include OpenAI’s DALL-E, Google’s Imagen, Stability AI’s Stable Diffusion, Midjourney, and Adobe Firefly. Simple user interfaces like Stable Diffusion’s DreamStudio or Adobe Firefly’s web interface are examples of gen AI tools built on platforms.

• Video generation models allow users to create simple generic videos or custom animations with a combination of text prompts and visual prompts. Dreamix, DeepMind’s DreamerV3, DeepBrain, Kaiber, Pika, and Nvidia’s recent expansion of Picasso are notable video models, though every major image model platform is working on their own video models and tools.

• Music and audio generation with AI uses natural language processing and generative techniques to create actual music and audio. Audiogen, Coditayos, Google’s MusicLM, Stability AI’s Stable Audio, and Suno are all key music models. Endel’s soundscapes for productivity, focus, meditation and sleep, StemRoller’s music unmixing and remixing, and AVIA’s animated media streaming platform are examples of tools built around models.

Types of generative AI tools and platforms based on access model include:

• Open-source generative AI tools are available to all users, with the source code made available under a permissive license (meaning specific end-uses of the model are not restricted) and generally as freely available weights (actual files of the trained model’s parameters). While some open-source model providers gate model access behind an application process and approval, these are still considered open-source models as long as the code is available (which allows models to be forked and independently run and improved).

• Commercial, proprietary, and enterprise generative AI platforms have one or more of the following characteristics: designed to monetize access in some way, only code is available (no weights), or some explicit usage or modification restriction is part of the licensing requirements. Some might support open models but typically make customers pay for API access. CLAUDE is open-source code but currently has no weights, making it a commercial model until weights are available. Models with content guardrails that explicitly block “inappropriate” uses are considered proprietary AI.

• API-based models are those hosted on platforms that allow access to models from within various tools, allowing them to call upon and integrate AI functionality without needing to develop it themselves.

Open-Source Generative AI Tools

Open-source generative AI tools are software solutions that are designed to produce creative content and execute various functions, and also have their foundational source code freely available for developers and users to access, modify, and/or distribute. Unlike proprietary tools that restrict access to their underlying code, open-source tools promote knowledge sharing, experimentation, and collaboration among diverse user communities, often fostering innovative advancements.

Some of the most frequently used open-source generative AI tools include libraries and frameworks maintained by Hugging Face, Stability AI, OpenLLM, Nomic Atlas, IBM Products, and OpenAI, among others.

• Hugging Face is an open-source platform that enables natural language processing (NLP) and other machine learning tasks using transformer-based models. It provides libraries like Transformers, Datasets, and Tokenizers, that contain pre-trained generative AI models such as BERT and GPT-2 among others. The models can easily be fine-tuned for various NLP tasks like text classification, question answering, and language translation. Hugging Face is a framework for building and deploying custom NLP models which allows developers to quickly build state-of-the-art NLP applications using transformers.
• Stability AI leverages open-source generative AI to “provide the foundation to activate humanity’s potential.” It strives to democratize AI development by making it as widely available as possible and therefore provides multiple open source products, including Stable Diffusion, a text-to-image model, and StableLM, a versatile language model alternative to GPT-based proprietary options.
• OpenLLM is a framework for quickly deploying popular large-language models (LLM), like Meta’s Llama 2, through an intuitive web interface. OpenLLM is built by the startup Bentoml and is available on the Linux Foundation’s AI & Data Foundation. It’s aimed at making it easier for more companies to safely host, serve, update, and manage generative AI models.
• IBM has consistently supported open-source AI development through its Watson services and active participation in the Linux Foundation’s LF AI & Data Foundation. The company has contributed projects like Docling, Data Prep Kit, and BeeAI, which are now hosted under LF AI & Data Foundation. In addition, IBM supports AI solution development tools such as Elyra—an open-source extension for JupyterLab—and SystemDS, a scalable machine learning system managed through the Apache Software Foundation. These contributions demonstrate IBM’s ongoing commitment to open, collaborative AI innovation.
• OpenAI offers open-source models, including CLIP for vision-language learning, Whisper for automatic speech recognition, and Triton for GPU programming. These models are designed to lower current limitations of current systems and encourage the adoption of generative AI by a wider base of software developers.

Open-source generative AI tools enjoy certain advantages that relate to streamlining or controlling costs.

• There is no licensing fee: Some proprietary generative AI tools’ licensing fees can be cost-prohibitive for smaller organizations.
• Lower cost of experimentation: Open-source tools are suitable for experimental endeavors as they don’t carry high licensing costs. Companies use them to test the waters with AI projects with minimal cost and risk, before deciding to migrate to proprietary tools.
• Customization: The ability to modify tools to tailor them to specific project requirements”

Closed-Source and Enterprise Tools

Closed-source generative AI tools refer to proprietary software that generates artificial content – such as text, images, videos, code, etc. – using AI models and algorithms. The source code underlying these tools is not shared publicly, and users can only access the tool’s features and functionalities through a commercial license or subscription.

These tools can offer several advantages over open-source options. Firstly, they are built and maintained by dedicated development teams, ensuring better reliability and stability. Most importantly, enterprise platforms come with customer-usable APIs to carry out functions such as fine-tuning models. Closed-source or commercial generative AI tools also usually have dedicated support teams, comprehensive documentation, and integration with existing software workflows to meet requirements for larger organizations. They often feature templates, wizards, preset interfaces, and dynamic in-app guidance, to make it easier for non-technical users to get started with generative AI.

Some of the most popular closed-source and enterprise generative AI tools include Jasper, Copy.ai, Writesonic, OpenAI’s ChatGPT, Runway, Jasper Art, Copy.ai, HyperWrite, Grammarly, Khanmigo, and Rytr.

Here are some of the closed-source generative AI tools that are used today, though there are many more options:

1. Jasper (Text, Image): Jasper is an AI-powered content creation platform for businesses that want to create high-quality marketing content faster, with a focus on brand voice. Jasper’s key features include templates for a wide range of use cases, a library of fine-tuned templates and prompts, and a browser extension for content generation in other tools. Jasper leverages a mix of proprietary and third-party LLMs.
2. Copy.ai (Text): Copy.ai is a generative AI platform that enables businesses to customize, automate, and share AI-powered workflows across teams or applications. Copy.ai’s main features include the ability to create and deploy workflows within minutes, connect to external data sources and applications, and collaborate with team members in editable workspace projects. Copy.ai leverages multiple LLMs in the backend.
3. Writesonic (Text, Image, Video): Writesonic is an AI-powered content creation platform that enables brands to create SEO-optimized, plagiarism-free marketing copy. Writesonic’s main features include the ability to optimize content for organic SEO and generate citations for verified information, bulk content creation, and a chatbot builder. Writesonic leverages GPT-4 in the backend.
4. PanelsAI: is a generative AI platform that helps users write high-quality content faster using multiple models like OpenAI, Anthropic, Xai and Google’s (Gemini 1.5 etc) at one platform for tasks such as content planning, campaign creation, and brainstorming.
5. Jasper Art: Jasper Art is image generation software launched in September 2022 by Jasper AI. This standalone generative AI tool uses models like Stable Diffusion and DALL-E 2 you can switch between and which allow users to generate original images based on textual prompts. Jasper Art can help create marketing materials such as blog posts, social media posts, or website imagery. Jasper is now understood to include GPT-4 models as well.
6. RunwayML: RunwayML is a video editing suite of tools driven by Runway’s own large foundation models. The models include Gen-1, which can create new video using an existing video and some prompts as a basis. Gen-2 is used to generate new video content directly from prompts, while Text to Image and Image to Image models create photographs or computer art based on text prompts or input images. Other tools create videos from images or tracks of motion, as well as tools for inputting and editing sound, removing backgrounds, and sharing or publishing content.

APIs and SDKs as AI Tools

APIs (Application Programming Interfaces) and SDKs (Software Development Kits) play crucial roles in making generative AI very plug-and-play, even simplifying use to a few prompts and a few clicks of the mouse for many language models. This has opened up customization and integration by non-technical users.

APIs can technically be considered tools in their own right, and they act more like a traditional AI tool in terms of allowing workflow creation. They enable quick access to powerful generative models. Developers can incorporate sophisticated image, text, audio, or video generation into their own applications or services without needing to develop these models themselves. This democratizes access to cutting-edge technologies for a broader range of businesses and projects.

APIs like OpenAI, Anthropic Claude, and Gemini provide endpoints that make it possible for non-technical users to input data and receive outputs without knowing any programming language. This user-friendly approach empowers individuals without technical expertise to engage with AI models and utilize their capabilities for tasks such as natural language generation or content creation. Importantly, APIs often offer customization features. Such as fine-tuning parameters or exploring model options. Customization enables businesses or individual users to tailor the AI models to specific needs, enhancing the precision and relevance of generated outputs for specific use cases.

These APIs typically provide prompt engineering guides and sample code that serve as valuable resources for developers. These guides assist in designing effective prompts – the instructions given to the AI model – ensuring that users elicit the desired responses from the models. This guidance streamlines the development process and enhances the overall user experience.

They also enable rapid iteration. Developers can experiment with different prompts, settings, and parameters to achieve desired outcomes quickly. This flexibility accelerates the development cycle, allowing for quick adjustments based on user feedback or changing needs.

APIs and generative AI tools have a very tight relation because APIs serve as the gateway for tools to interact with generative models and provide a bridge between the underlying generative models and the end-users who leverage these tools.

• Tools utilize APIs to extend their functionalities and access advanced AI capabilities without reinventing the wheel. This symbiotic relationship enables tools to leverage generative APIs for tasks such as natural language processing, image generation, or audio synthesis.
• APIs act as the standardized interface through which tools communicate with generative models, ensuring interoperable and seamless integration. This consistent connection enhances the efficiency of incorporating AI capabilities into various applications, expanding the reach and usability of generative AI.
• APIs offer customization options, and tools can leverage these settings to tailor the AI models to specific use cases.
• This relationship facilitates scalability. As APIs often support cloud-based deployment models that allow tools to scale their operations seamlessly, catering to increased user interactions without worrying about the backend infrastructure.
• The continuous evolution of generative AI is reflected in updates to APIs. Tools integrating these APIs benefit from the latest advancements in AI.

AI SDKs for Developers

Software development kits (SDKs) play a crucial role in helping developers create, customize, and integrate generative AI tools into projects with more power and flexibility than APIs. SDKs are often open-source, come with a suite of integrated APIs for controlling their functionality, and have comprehensive documentation. For these reasons, they allow extensive flexibility for applications with complex requirements, though require some coding work for this.

Hugging Face Inference Endpoints: Highly praised by technical users, these are best for experts and larger businesses with high-performance requirements. They have interfaces targeted to developers, documentation rich with technical detail, and low-level customization options to suit niche use cases. However, they are one of the more difficult tours on the market and users report that they are prone to slowdowns. Hugging Face Inference Endpoints supports a wide range of models including hundreds of LLMs, TTS, speech-to-text, image generation, object detection, and many more. Hugging Face Inference Endpoints can be hosted on cloud clusters by AWS, Azure, or Google Cloud Platform.

TensorFlow APIs: TensorFlow provides highly customizable APIs for newcomers up to experts in building, training, fine-tuning, and managing machine learning models for generative AI. They enable developers to create custom Python programs or work with generative AI models within the TensorFlow ecosystem, leveraging its extensive set of machine learning and deep learning libraries, tools, and frameworks.

AWS Bedrock SDKs: Provide interfaces for developers to interact with the Bedrock platform more programmatically than through the UI. This enables the building of custom applications, automation of workflows, and leveraging Bedrock’s capabilities in machine learning, deep learning, and AI services.

Top Generative AI Platforms and the Models They Support

OpenAI Platform. now offers access to a full suite of state-of-the-art models and tools, including GPT-4.1 (with mini and nano variants), multimodal GPT-4o, and the advanced reasoning agents o3 and o4-mini. It supports language, code, vision, and audio use cases—such as GPT-4.1 for coding and long-context tasks, GPT-4o for multimodal applications, and o3 for deep reasoning workflows. The API also includes tools like DALL·E 3 for image generation and Whisper for speech transcription. These services enable developers to build and deploy customized AI applications without training models from scratch. Additionally, OpenAI provides visual prompt design and application builders through tools like the ChatGPT plugin builder and Functions API, allowing non-API users to create AI-enhanced experiences without managing model endpoints.

• Accessing OpenAI APIs: Developers can use REST APIs with authentication keys and SDKs in Python, Node.js, and Go to connect to the foundational models. There are still waiting lists for new users of certain models. Pricing is pay-per-request, calculated by token.
• Key Use Cases: OpenAI’s flagship APIs are broadly used for content generation, document processing, marketing copywriting, conversational chatbots, data extraction, artistic image creation, enhancing voice assistants, and more. Their open-ended capabilities and services make them some of the best alternatives to Bard (now Gemini). Key direct competitors are big cloud platforms like Google Gemini and Anthropic Claude, and many open-source models can provide similar capabilities.
• Pricing: Both the API and tools are free to use up to the monthly usage limits, then pay-per-use fees are charged determined by prompt volume and usage.

PanelsAI. is a unified generative AI workspace designed for writers, marketers, and digital teams to interact with multiple leading LLMs through a single, centralized interface. The platform enables seamless prompting, content creation, and editing using models like GPT-4, Claude, and Gemini without needing separate accounts or API management.

• Accessing PanelsAI: is accessible via a clean web-based interface with no-code interaction. Users can select any model to launch chats without needing technical skills. The platform abstracts model complexity behind intuitive interfaces, offering model flexibility for different use cases without requiring switching between platforms.
• Key Use Cases: PanelsAI is widely used for content marketing, ad copy generation, SEO blogging, email automation, social media writing, product research, and creative brainstorming. It’s also gaining adoption among teams looking to assign writing tasks or manage AI output as part of a content production pipeline. Users can generate content across multiple formats in one place without juggling multiple tools or subscriptions.
• Pricing: PanelsAI offers a just 1$ trial with limited usage and while others use a pay-per-use pricing model that charges customers based on the amount of time spent utilizing the service, the number of generated outputs, or the volume of data processed.

Google Vertex AI for Generative AI. While Vertex AI has long provided MLOps tools for building, training, and deploying models, as well as easier access to various Google Foundation Models, Vertex AI now enables developers to access, evaluate, and fine-tune the latest Gemini (formerly Bard) and PaLM 2 models for next-generation generative AI use cases. Cloud storage, machine learning engines, model customization, and interactive interfaces round out the service.

• Accessing Gemini AI: Gemini and other Google models can now be accessed both in the Vertex AI interface and via APIs, alongside the longstanding extensibility service offering multiple SDKs and no-code integration tools.
• Key Use Cases: Common use cases focus on optimizing enterprise AI workflows for better search, language app development, and data analytics. Third-party models enhance application development and NLP applications, while model fine-tuning enables customization for unique business objectives. While OpenAI is Gemini’s primary direct competitor, Anthropic’s Claude and lesser known models like Mistral are strong competitors at a purely LLM level.
• Pricing: There is a free tier with monthly usage limits beyond which pay-per-use fees kick in based on usage.

AWS Bedrock. Bedrock is Amazon’s managed platform for safe and scalable generative AI application development. Bedrock connects to multiple models from leading providers, including Amazon Titan, Stability AI, AI21 Labs, Anthropic Claude, and Meta’s Llama. The models can be deployed alongside powerful workflow tools to streamline prompt engineering, customization, fine-tuning, and hallucination detection.

• Accessing Bedrock: Bedrock is accessible via a web interface, AWS console or CLIs, REST APIs, or via Python, TypeScript, JavaScript, and Go SDKs.
• Key Use Cases: Largest enterprise users most commonly use Bedrock for customer service chatbots, document analysis, content marketing, and seamless backend workflows for complex architected LLM frameworks. Increased focus on safe deployment and stringent security procedures leads to comparatively higher trust and increased adoption. Multi-model APIs have significant cost and flexibility benefits as well.
• Pricing: Pricing is determined by which models are used, how often, the extent of integrations, and which end-user-facing tools are used.

ChatGPT. ChatGPT is unique among generative AI tools for its breadth of users given its provision of human-like conversational and writing capabilities to a broad population of non-technical users. While OpenAI exists behind ChatGPT, the application has attracted a base of key power users interested in integrating GPT models into a collaborative workspace leveraging features such as web browsing, multimodal outputs like voice interaction capabilities, and custom GPTs for more tailored outputs.

• Accessing ChatGPT: ChatGPT is accessible via the web, mobile app, API, and browser extension.
• Key Use Cases: ChatGPT is heavily used for content creation, code and technical support, data processing collaborations, research, and knowledge management. Its chatbot interface enabling intricate, dynamic conversations remains a distinctive highlight that differentiates it from other competitors such as Bard or Jasper. Many downstream custom applications are focused on enhancing productivity and decision-making by summarizing documents/chats and organizing action items and next steps.
• Pricing: ChatGPT is free for basic use with GPT-3, with a paid $20 per month tier that gives access to GPT-4 and added features. API access is metered.

Hugging Face Hub. Hugging Face Hub is a central repository of multiple natural language processing (NLP) models designed for language understanding and generation. As an application, the Hub provides a collaborative platform for sharing and discussing the latest developments in language models.

• Accessing Hugging Face AI: Hugging Face AI can be accessed via SDKs that leverage common languages such as Python, JavaScript, and Swift. APIs and a command line interface assist with large-scale deployment, testing and integration of models from the Hub.
• Key Use Cases: For community members, they find Hugging Face Hub to be a central hub to collaborate, conduct NLP research, and deploy language-specific models. It supports workflow process optimization, prompt engineering, increased scalability, and model fine-tuning for downstream users. Its collection of the best open-source large language models that can be easily evaluated and deployed is a unique characteristic that differentiates it from competitors.
• Pricing: Pricing is determined by factors such as subscription plans (based on storage space, cloud machine hours, bandwidth, and so on), deployment architectures (SaaS or Self-hosted), and custom bug-fix support.

Stability AI Platforms and APIs. Stability AI is a platform provider for cutting-edge, open-source generative AI tools and applications for generating images, video, audio, and language. Support and community tools for developers and artists to understand and use the tools is an extra layer of offering Stability brings into the gen AI space.

• Accessing Stability AI: Stability AI’s web interface offers a user-friendly platform for exploring and utilizing various models, while APIs and SDKs make it easier for downstream developers to integrate Stable models into their own applications. The platform supports over 10 programming languages.
• Key Use Cases: In addition to content creation, code generation, image creation, and workflow automation, the platform has seen increasing adoption for synthetic video, speech and music applications. Distinctiveness is a core focus, as Stability AI intends to provide both the fastest and most capable image, speech, audio, and video generation models.
• Pricing: Stability AI offers generative AI services such as models and art generators based on various pricing systems. Some are free, while others use a pay-per-use pricing model that charges customers based on the amount of time spent utilizing the service, the number of generated outputs, or the volume of data processed.

These are just the largest platforms, and each has its own multi-model collection of tools in various categories. Choosing which is right for you is a function of your needs, the cost, and expertise required.

OpenAI API and Platform

The OpenAI Platform is a cloud-based machine learning solution that enables developers to utilize advanced language models for various applications. OpenAI was founded in December 2015 by Sam Altman, Elon Musk, and other leaders including Greg Brockman and Ilya Sutskever with the mission of building safe and beneficial artificial general intelligence (AGI). While the organization initially pledged to share research, code, and models openly, its shift to a capped-profit structure and more guarded release policies in recent years has drawn scrutiny over its transparency, OpenAI is a clear leader in the generative AI platform space with over a thousand professional and hobbyist-related jobs now referencing it on Indeed.

The first Transformer-based GPT model was released in June 2018 as a deep learning natural language processing model that leverages a transformer-based architecture designed to solve a wide variety of natural language tasks. The following image is a visual representation of OpenAI’s modular, rotating approach to fine-tuning models using reward prompting and reinforcement learning from human feedback that is at the core of their GPT model architecture and frequent update process.

OpenAI’s second version, GPT-2, released in February 2019, improved performance, scalability, and data efficiency for quality responses across a wide variety of tasks. GPT-2 has 1.5 billion parameters and was initially refused a full release by OpenAI due to “concerns about malicious applications of the technology” but later released as open source. GPT-3 did not have such a delay and debuted in June 2020 as a new class of very large, multidisciplinary language models capable of solving novel tasks. GPT-3 has 175 billion parameters, making it one of the largest language models in existence at the time. GPT-3.5 was released in March 2022 with improved conversation and multitasking abilities. The fourth generation, GPT-4, was released in March 2023 with increased accuracy, task range, and other capabilities. The GPT-4 Turbo model was released in November 2023.

Access to all of these GPT models, as well as other OpenAI models such as DALL-E, is available through their app or API. The OpenAI API allows users to deploy trained models such as GPT embeddings and customized models, evaluate model performance and monitor production or inference-level metrics. People familiar with OpenAI’s ChatGPT frontend product may be less familiar with this API and production-level toolchain. Models can be fine-tuned using the OpenAI API or Command Line Interface (CLI) and there are preset tasks such as sentiment classification, topic classification, NER tagging, and text moderation.

GPT-powered chatbots are frequently used online, making up the bulk of available product options on a website like there’s often significant skepticism about the reliability of the answers provided by these products due to their inability to provide citations. Some have partially solved this issue by making a retrieval-augmented generation (RAG) or LLM fusion hybrid models to connect to search engines, provided citations from references, or retrieved information from their own trained datasets. However, this often introduces limitations of their own such as slow response times, hallucinated citations, and difficulty discerning what exactly the model is trained on.

OpenAI’s Embedding models are commonly used because of their versatility and ability to catch even complicated semantic patterns. The embeddings represent the meaning and relationships of natural language, code, and other forms of data. This lets them accomplish complex machine learning tasks related to semantic search, clustering, topic modeling, and data visualization. OpenAI Embedding models range in cost and size from ada-002 at $0.0001 per one thousand tokens up to davinci-002 at $0.0200 per thousand tokens.

The Whisper API provides automatic speech recognition (ASR) capabilities, available through open-source or OpenAI fine-tuned models. These models are used to generate srt or vtt files that have segmented transcriptions of speech and can be imported into video or audio editing software to automatically create subtitles. The following image is one-capable case where a video file can have Whisper generated speech recognition, which is then enhanced via additional layers such as speaker diarization, punctuation, and emotional sentiment analysis.

Managing multiple tools and models often requires a centralized control layer. Learn how centralized APIs improve governance, scalability, and integration across AI platforms.

Google Vertex AI for GenAI

Vertex AI is the cloud-based platform by Google that enables an organization to develop, deploy, and maintain generative artificial intelligence (AI) solutions across a wide variety of use cases. Vertex AI can be run on the Google Cloud Platform or within customer-managed cloud or on-premise environments. It also provides an efficient API, enabling developers to easily consume and make use of the Google AI models (such as Gemini or PaLM).

Vertex AI’s main strength is its support for Google’s own powerful foundation models. This includes the following:

• Gemini. Google’s new flagship multimodal and multilingual foundation model, available in three versions (Nano, Pro, and Ultra) and several possible sizes each. The industrial-scale Gemini Ultra model features a 1 million token context window and advanced reasoning capabilities, with the equally powerful Gemini 1.5 Pro variant now available in preview on Vertex AI.
• PaLM (Pathways Language Model). PaLM 2 is famous for running Google’s automated search results and powering features like “help me write” in Gmail, and “immersive view” in Google Maps. Developers can choose among three versions: PaLM API, Text, and Chat-Bison.
• Imagen. Google’s Imagen models are an increasingly important part of the image generation landscape, with five powerful model variants and usage options.
• Chirp. Chirp is Google’s new voice model, currently available on Vertex AI only in English.

Vertex AI automatically analyzes training data, selects appropriate features, fine-tunes hyperparameters of ML models, and selects models based on the problem and dataset characteristics.

The platform supports a wide variety of advanced machine learning tasks including creating, training, and deploying supervised and unsupervised machine learning models. Vertex AI also offers key data processing functionality facilitating the preparation of input data and exploring of features. It provides model evaluation and validation and online learning to allow AI models to re-train themselves with changing data. It provides one of the best MLOps transforms to enable the end-to-end engineering of AI pipelines. And finally, it offers document analysis, transforming images such as scanned forms into natural language.

This video from Google demonstrates how to fine-tune Vertex AI models with Retrieval Augmented Generation.

AWS Bedrock and Hugging Face Hub

Hugging Face AI platform comes in the form of the Hugging Face Hub, which is a repository intended for public community sharing as well as private collaboration for machine learning models and other generative AI assets. Hugging Face Hub is accessible via web interfaces, Python APIs, and CUDA APIs. As of June 2023, Hugging Face Hub has 245,000 models available which cumulatively have been downloaded more than 3 billion times.

AWS Bedrock is Amazon’s collection of managed cloud-based AI tools and models, including Foundation Models (FMs) from Anthropic, Stability AI, and internal Amazon models. All of the models in Bedrock can be accessed as managed services. It provides tools for users to customize models using their own data and fine-tuning, and provides security, and operational monitoring. The Bedrock team is integrated with the core of Amazon AWS to leverage its global cloud infrastructure for full scaling of models and services.

Stability AI Platforms and APIs

Stability AI is an open-source generative AI tool and company utilizing public datasets and novel AI methods in offering their open-source products. The products have the “ability to generate high-quality, photorealistic images, videos, animations, 3D models, and music from text and visual inputs.” They have been used by more than 200,000 developers and created more than 170 million images. Stable Diffusion models are the foundation on which they build, and they are designed via a mix of proprietary methods, open-source, and external vendor approaches, with a focus on being accessible to the broadest set of users from very simple no-code consumer apps to detailed custom training via the Stability SDK.

Key Stability AI models include:

• Stable Diffusion: stability AI’s diffusion model was created to generate art images from text prompts. It is open-source code and was developed as an alternative to proprietary diffusion methods like those of OpenAI used to create DALL-E. They have image and animation versions of the model, and the tool ultimately became the foundation of their entire business.
• Stable Video Diffusion: this product is video generation software with open-source code that enables users to perform image-to video tasks and create 2-second sample videos based on a single image. This video generation capability of familiar media and sophisticated tool brings the high quality of text-to-image to rapid and useful video creation.
• DreamStudio integration: Dream Studio is an AI development studio with more than a million users in more than 50 countries according to Stability. It enables their generative AI models to be run either via the cloud or locally, and they support multiple programming languages.

Today Stability AI does not have tools for all modalities such as audio, though the multi-modal aspects of their platforms support downstream pipelines which, for example, turn text or video into audio.

Stability AI’s success in product development is member-driven and goal-derived. They factor in the short- and long-term goals of their users, and combine that with today’s best AI approaches to maximize useful outcomes. This is reflected in their basic approach in which their product is open source and capable in a wide range of use cases but still easily accessible – even to new users. As a result, since its release in 2022 Stable Diffusion has become the most popular and widely used of the image diffusion models both open- and closed-source. Stability AI, founded in 2019 in London, has been an early and strong advocate of responsible AI development in which the interests of all stakeholders are protected and considered. Their models are open source, efforts for copyright protection have been enabled, copyright infringement addressed, and a host of other issues related to responsible AI confronted head-on.

Generative AI tools rely heavily on evolving neural architectures. To understand how these models actually work behind the scenes, explore our article on Generative AI Neural Networks.

Common Use Cases for Generative AI Tools

AI tools are increasingly used in multiple industries, with generative AI in particular rapidly expanding the workflows it can perform. Common use cases for generative tools include text/copywriting, image/video generation and editing, as well as for audio/voice/music creation and editing. The areas of deployment are rapidly expanding as generative AI technology improves in speed and capability, and as the underlying computational resources to power the models are expanded.

Key industries and workflows where generative AI tools are increasingly deployed include the following:

• Media, Marketing, and Communications: Generative AI is used in media, marketing, and communications for content creation, including articles, news stories, blog entries, immersive corporate videos, and advertising content. Marketing material is delivered across multiple channels and styles and includes not only images and videos, but also podcast scripts, presentations, and social media posts. Deloitte Digital’s 2023 study surveyed approximately 650 senior leaders in marketing roles across diverse industries, finding that 26% were already using generative AI for content creation and another 45% planned to adopt it by the end of 2024. While the sample focused on marketing professionals rather than a broad group of 900 respondents, these figures reflect significant momentum in GenAI adoption for creative workflows.
• Healthcare: From early-stage research and drug development to customer care and documentation, AI tools are already transforming the healthcare industry. Generative AI tools such as PharmaLAMDA are helping to identify targets and develop new molecules of drugs, accelerate clinical trials, and optimize manufacturing and commercialization. The chart below shows typical sources of value which generative AI is expected to bring to the healthcare industry.
• Finance: Generative AI tools in finance are increasingly powering personalized decision-making and product development at every step of the financial journey from banking to investing and insurance. AI-based rapid research and content curation are leading to the automation of much of the editorial/web/database content across the industry. They’re also being used for fraud detection, risk assessment, and quantitative financial analysis.

  McKinsey’s research estimates that generative AI could contribute between $2.6 trillion and $4.4 trillion annually across 63 use cases they analyzed equivalent to adding roughly the entire GDP of the United Kingdom in 2021. This range reflects a 15% to 40% increase in the impact of all AI technologies when generative AI is included alongside traditional methods.

• Design, Film, and Animation: Hollywood has long used some form of AI to generate entertainment content such as crowds. The emergence of generative AI is likely to accelerate that trend. Automated editing of stunts and voice-overs, customized scriptwriting, and marketing are some of the areas where generative AI is expected to play an increasingly large role.
• Software Development: AI tools for developers can generate code and act as coding copilots, automate the development of test cases, troubleshoot errors, automate test case development, and much more. This enables software developers to improve their productivity, write code faster and with fewer errors, thereby accelerating their time-to-market.
• Retail: New product innovation, operational optimization, supply chain management, and customer service are being increasingly enabled by generative AI tools. For example, H&M is using generative AI to do synthetic data generation for planning purposes while Levi’s is using AI-generated models to supplement human models for improved personalization.
• Other Uses: In the legal field, analysts expect that generative AI will optimize legal review and knowledge management while increasing overall productivity. In the industrial sector, generative AI-powered virtual or augmented reality “avatars” will augment customer and field technical support while generative AI tools will optimize workflow management.

Text, Content, and Copywriting

Text, content, and copywriting tools are new software and cloud-based products that automate or assist in creating marketing and other content to reduce costs, increase productivity, and increase content personalization. This is often done using large language models to create high-quality first drafts or finished content with minimal human input. Natural language processing (NLP) algorithms analyze input prompts, which can be as short as ten or twenty words, to help generate anything from headlines to long articles. Typical outputs include social media posts, email newsletters, advertising copy, blogs, documentation, and even books.

More than a dozen well-known text, content, and copywriting tools are being used today, They all avoid plagiarism and ensure that each piece they generate is fresh and unique. Even though each item is generated from the same basic prompt, these tools purposely create different outputs with each run always. This graphic from LLM application researchers Jay Alammar and Marek Rei (a well-known machine learning researchers, educators, and visual communicators) summarizes the relationships and order of use of a small selection of such tools.

Some typical users of these tools in the market today.

• Jasper: A content creation tool offering pre-made templates for various marketing needs and customization options for generated content. It includes a knowledge base feature with links to articles, FAQs, and other helpful resources.
• Copy.ai: Suited for beginners or those who require basic marketing content. Easy to use and provides a range of content templates and customization options.
• ChatGPT: Serious content writing tool with more customization options than Copy.ai, but it has a steeper learning curve and requires technical expertise. Generates high-quality content and features SEO optimization, plagiarism checking, and automated editing.
• Google Bard: Answer questions in a human-like conversational style by researching on the internet and linking to sources. Still in the experimental stage, so it is only available to a small number of users right now.

Image and Video Generation

Image and video generation has exploded in popularity since 2022 as a use case for generative AI due to the power, simplicity, and low cost of generative tools to produce engaging imagery. Visual content is increasingly important in business settings where it can improve conversion rates, advertising recall, and engagement.

Static images can be generated from tools or APIs such as Adobe Firefly, Microsoft Bing Image Creator, Stability AI’s Stable Diffusion, or Midjourney. A new industry of AI-generated “stock” imagery has emerged, and hot competition between the leading models is rapidly increasing output quality.

These images can be edited with a large array of generative AI tools and features, ranging from industry favorites like Adobe Photoshop’s beta version of AI-powered Generative Fill, to earlier leaders like Canva, to specialized tools like Deepswap for face swapping, or specialized APIs from Stability or OpenAI that modify images beyond the capabilities of Photoshop.

Videos are entering the generative process either by being fully generated by text prompts, by being extended from existing content, or by editing existing video content.

Runway ML and Pika Labs are two of the most prominent leaders in full video generation. They enable inputs measures again time (seconds), and users can fine-tune the results with further prompts. The field is rapidly becoming crowded and competitive, with even some text-generating models like Anthropic’s Claude beginning to experiment with video creation.

Major generative AI models such as Google Gemini, GPT-4o, Meta Llama 3, Mistral, and others all have video capabilities, most via APIs or SDKs. Chatbots, music generators, and instructional videos are some of the most popular use cases for generative video creation. For more information on video generation platforms, see this review of the key criteria for video software tools.

Video upscaling and editing tools include Synthesia, Reface, and Kapwing, which can generate or manipulate short-form video content but are not able to generate high-quality, long-form video content comparable to that produced by major film and television studios.

Audio, Voice, and Music Creation

Audio, voice, and music creation refers to the broader field of using generative AI to make sound, create speech performance using unnatural voices, and compose artificial music tracks. AI-generated music refers specifically to techniques and processes that enable computers to produce music in an automated or semi-automated way. Tools for creating AI-generated music do just this. AI-generated music is often confused with (and implicated in) controversial discussions about AI-generated audio deepfakes. An AI-generated audio deepfake refers to an artificially created audio file that closely resembles the speech, voice, and even certain expressions of a real person.

There are two primary techniques that such tools use. First, they use symbolic AI, a process by which computers manipulate symbols and strings of text (such as “note on C4”) using specific rules and heuristic logic in order to create new works. Second, they use machine-learning processes to train on data and learn how to make new music that closely mirrors that data. The screenshot below shows the interface that arises after a user enters a prompt to create a 30-second audio piece in Suno, the leading AI-generated music platform.

There are four major uses of generative audio in the market today, but each has different requirements.

1. Background music (musac): The digital age has greatly increased the demand for background music for videos, podcasts, gnmes, and social media content. As such, falls in copyright protections and the increased popularity of open licensing often limit the musical options available to individuals and organizations. AI-generated music helps by allowing people to generate truly custom musical works at the speed of sound. As such, there is growing demand for AI-generated background music.

2. Music mixing (Suno, Bandlab): Western music can be described in terms of rhythm, harmony, and melody. AI holds the promise to help people mix, transform, and vary these three building blocks of music. For example, AI tools should soon be able to adjust to the bpm (beat per minute) of the on-screen action, shift a key change, and morph between musical styles.

3. Composing melody and/or harmony (Stability AI’s Harmonai): Human composers write by hand pieces for orchestra that blend multiple musical lines and ideas together in a way our brains find pleasing. Harmonai approaches this through open-source generative models that can create new pieces on the basis of its library of prompts and samples.

4. Human text-to-music generation (Suno, StableAudio, Udio): As with text, human users have embraced the prompt method of producing new music. The final product varies wildly by tool, but human users can fine-tune works created in these new AI platforms, or the platforms can create fully complete songs.

AI-generated music faces powerful resistance from existing musicians and copyright holders over two key concerns: AI-produced copies of real human voices (audio deepfakes) as a new form of identity theft, and AI generation of audio tracks based on or identical to (infringing on) copyrighted music data.

One recent high-profile controversy was the outcry over an AI-generated song in 2023 that imitated the voices of rappers Drake and the Weeknd and managed to gain significant online attention before the original copyright holder Universal Music Group had it taken down.

Another example involves the debate over AI-generated music tracks that can either clone, imitate, or otherwise infringe upon copyrighted musical works. There are thousands of examples of generative AI music platforms encountering copyright claims and takedown requests in the wild.

In April 2022, Sony Music Group chose to halt progress on certain AI-driven music generation projects. The company expressed concerns that generative AI models are trained on copyrighted material in ways that cannot be reliably monitored or controlled. Later media reports from 2024 described how Sony formally notified more than 700 AI developers and music-streaming platforms that its catalog should not be used to train AI systems due to copyright concerns.

The rise of AI-generated deepfake content featuring Taylor Swift has amplified those concerns. In late January 2024, explicit deepfake images and videos of her face circulated across platforms like X, TikTok, and Instagram. One viral post alone received tens of millions of views—prompting Swift’s team to publicly warn fans about the misleading and unauthorized content creation. This episode highlighted the urgency around enforcing copyright protections and regulating synthetic media.

Despite these negatives, there are positives. One of the biggest benefits of generative AI for music is in the accessibility and inclusiveness opened up for musically untrained creators. Generative AI tools and platforms make it easier for them to create original music without expensive studio hours or a lengthy licensing process. Another upside is that it saves time by automating simpler and more basic iterations (for example, creating a jingle that might take a human musician 5 seconds, an AI tool might generate thousands in the same period of time).

The best AI tools to create music are Suno AI, harmonai, iZotope RX, AIVA, Soundful, and Bandlab. Audiolab and Udio are also widely used, though they need to deliver on audio copyright notice features. Eleven Labs excels at the text-to-speech side of audio generation.

How to Select the Right Generative AI Tool or Platform?

Select the right Generative AI tool or platform by focusing on four main factors: output quality, scalability, cost, and development factors like compatibility, timeline, and licensing. Within these high-level criteria, AI engineers who specialize in machine learning and work in natural language processing or natural language generation will take a much deeper look at factors like latency, inference content, throughput, data needs, and security.

BCG and experts from leading organizations have published frameworks to help businesses evaluate generative AI platforms. For example, BCG consultants—such as Alan Iny, Luc de Brabandere, and colleagues—offer an innovation-centered roadmap, while engineers at NVIDIA present technical deployment frameworks. These resources cover key evaluation factors like model performance, governance, integration, and scalability.

Here are eight more detailed Generative AI tool selection criteria:

• Output quality: Assess output quality against desired media type, features, accuracy, complexity, and realism.
• Cost: Analyze the sufficient size of training and inference data, cloud services, hosting, compute systems, and customer support.
• Scalability: Evaluate scalability, including cost-per-time-unit and performance benchmarks against real-world workloads.
• Development timeline: Select a tool or platform based on typical testing, deployment, and optimization timelines.
• Dev Resources: Analyze if an organization has the required skills to successfully develop a tool or platform.
• Compatibility: Assess compatibility for integration into other platforms, and provide security and user privacy compliance.
• Licensing: Understand the tool’s licensing to avoid intellectual property, or copyright infringement issues, or regulatory hurdles.
• Customization: Evaluate the tool’s ability to customize the output data based on the needs.

This generative AI tools decision chart is a useful bottom-line forumla for selecting among the various options in the market.

Not all platforms are equal. If you’re seeking objective performance insights, our breakdown of AI platform evaluation criteria covers ROI, usability, latency, and vendor capabilities.

Factors to Consider When Choosing a Tool

For both large-scale enterprise-level needs and fast and easy early trial testing, choosing a generative AI tool or platform is a multi-layered decision requiring consideration of factors all along the process from initial assessment to planned deployment and scaling. These are key factors to consider when choosing a generative AI tool:

1. How well is the tool designed for ease of use and productivity?
2. How extensible and customizable is the product?
3. Does it have strong scalability and performance requirements?
4. Can the tool be integrated easily with existing systems and data sources?
5. Does the tool support the specific AI use cases and modalities I need?
6. Is it available at a cost-effective price for different usage levels?
7. Are trust and safety, including data protection and compliance, ensured?
8. How simple, accessible, and predictable are the terms of use?
9. Does the vendor have a good reputation?
10. How strong is customer support, documentation, and training?

The questions below can be asked to better clarify each of these points for your organization’s needs.

1. How well is the tool designed for ease of use and productivity? With so many options available, especially for model-centric open-source tools, evaluating a tool’s user interface (UI) and user experience (UX) design is a quick way to make an initial shortlist of options. In multi-step processes such as training data, storing model weights, or codifying deployment for example, some tools may require more manual effort than others. A good sign of usable, well-designed products is the presence of visual documentation along with textual material such as videos, diagrams, and interactive tutorials.
2. How extensible and customizable is the product? Customization and extensibility is critical for choosing a scalable generative AI tool. Opt for tools that allow customization for proprietary data and workflows, adaptability to changing use cases, and future integration with other existing tools.
3. Does it have strong scalability and performance requirements? Evaluate the scalability metrics they present, such as supported throughput and concurrency. Assess performance by testing out their learning speed against sample tasks or comparing benchmarks against competitors.
4. Can the tool be integrated easily with existing systems and data sources? Consider whether integration requires technical expertise or licenses and whether your current development environment or platforms can support the tool.
5. Does the tool support the specific AI use cases and modalities I need? Different generative AI tools like Claude, Chinchilla, or DreamStudio, have unique strengths and weaknesses. For example, LLM-based models might be more appropriate for data-intensive tasks while GAN-based models might be better for creative tasks. A critical evaluation metric is whether the tool is a good fit for your specific use case.
6. Is it available at a cost-effective price for different usage levels? Scalability and cost-effectiveness should be balanced, with careful budget analysis of licensing, operational, and tooling costs. Consider whether the vendor offers support to enterprise customers and whether training and onboarding of staff is supported from a cost-effectiveness perspective.
7. Are trust and safety, including data protection and compliance, ensured? Ensure the tool has robust trust and safety protocols in place, with data protection regulations and compliance requirements like GDPR or HIPAA built into the tool’s architecture thought process.
8. How simple, accessible, and predictable are the terms of use? Consider the complexity and transparency of licensing contracts, and ensure that the tool does not involve legal or contractual penalties that are too risky or unpredictable.
9. Does the vendor have a good reputation? Research the vendor’s track record and user feedback, through popular open-source and enterprise software review sites and through published benchmarks.
10. How strong is customer support, documentation, and training? Evaluate whether the vendor provides adequate support for documentation and learning materials. Whether or not the product provides a comprehensive manual or an active user community is an important predictor of whether it will be robust, trustworthy, and future-proofed. Evaluating the vendor’s customer service policies is important as well.

This is not a comprehensive list. As generative AI tools continue to proliferate, evaluation criteria will get updated through community consensus, feedback, regulatory evolution, and new tools being developed.

When to Use an Open-Source vs Proprietary Tool

The choice between using an open-source vs a proprietary tool often comes down to factors such as flexibility, cost, and ease of onboarding and usability.

Open-source generative AI tools provide strong flexibility and control for teams who want to iterate the models, pipeline, and interfaces to match their needs if they have the technical resources to accomplish this. They can also be useful when there are security or privacy concerns, as the full code base can be audited for safety.

In a piece of analytical guidance, Accenture in its industry report Unlocking the Generative AI Revolution in Enterprise notes that “for many, adoption challenges linked to new content types and a general lack of understanding about Generative AI will cause them to initially turn to third-party content.” However, “the introduction of more open models and associated skilled communities will accelerate and democratize adoption.” This suggests that while proprietary solutions are a good starting point, open-source models have a powerful role to play as users grow more comfortable utilizing generative systems.

Proprietary generative AI tools are typically faster and easier to set up, requiring a commitment of only minutes or hours in most cases. They usually have more advanced and thorough documentation, as well as legally binding SLAs that guarantee support. Enterprise-grade SaaS AI tools are particularly valuable as they offer regulatory compliance such as SOC 2 or GDPR, while remaining easy and affordable to use off-the-shelf.

The decision between open-source and proprietary tools is often determined by the need for flexibility and technical resources to support deployment, vs the need for immediacy and a guarantee of security and compliance.

Important Questions to Ask Vendors

Buyers should ask critical questions to ensure a vendor can meet their needs for use cases, integration, security, scale, support, cost, model selection, and business continuity. These are some of the most important questions to ask:

• Does the platform provide access to a wide selection of generative AI models and customization to support niche use cases?
• What data input and output options are available for training and deployment to my precise needs?
• How do you guarantee the privacy and security of sensitive user data during storage, training and inference?
• Can I integrate the generative AI models into my existing tech stack? What are the key tools for this?
• What level of technical support is offered to me as a unique customer?
• What is the typical downtime around generative AI model servers? How often are the models unavailable?
• What customizability and fine-tuning options are available?
• How does your billing model work? How do you measure the value and costs?

The best Generative AI vendors will be able to clearly answer these questions for every customer and use case, and will even have documentation for buyers to read and understand at their own pace.

When and Why Should You Use a Platform Instead of a Tool?

You should use a platform instead of a tool when you need broader scale, multiple integrations, and long-term needs like ownership and flexibility. Generative AI platforms are better suited for building fully integrated, complex, mission-critical, or large-scale applications because they offer more customization, broader capabilities, infrastructure, and cost-effectiveness than standalone tools.

AI tools are simpler, task-specific, easy to use, cost-effective, and ideal for small-scale or non-critical projects, according to developer advisers at Google and Appen. But their customization and scalability are limited. Standalone tools may not provide the level of control and ownership over the application or data that organizations require. Standalone tools may be sufficient for short-term projects, but in the long run, they may not be as flexible or cost-effective as building on a platform.

According to Gartner, platforms provide a more comprehensive AI solution, including development, deployment, and management, than standalone tools. Hugging Face explains that AI platforms are often more scalable, reliable, and secure than standalone tools, which can be especially important for mission-critical applications.

Ultimately, the decision to use an AI platform or a set of standalone tools depends on the complexity and scale of the application, as well as the level of customization and control required. It’s important to carefully evaluate the available options and choose the solution that best fits the specific requirements of the application.

How Are APIs Different from SDKs in Generative AI?

APIs and SDKs are both important enablers of building generative AI applications, but they differ in scope. APIs (application programming interfaces) allow different software systems to communicate and interact with one another. In the context of generative AI, models are hosted on cloud servers which allow generative AI tools to access and call functionalities like text or image generation. The AI model itself runs on the platform vendor’s remote servers. APIs often provide sample code but not full software tools.

SDKs (software development kits) go beyond merely defining API interfaces and endpoints by providing developers with a complete “toolkit” that includes code libraries, documentation, user interface templates, debugging tools, and more. Beyond making API calls to hosted models, some SDKs enable downloading and building directly on top of open-source models for custom training, deployment, and use of generative AI tools. They typically bundle example projects, prebuilt user interface components, and testing utilities to help accelerate app development for specific platforms. Those platforms can be made available for generative AI tools that facilitate prompt engineering and automate tasks.

APIs provide immediate hosted capabilities – you pass your input to the API and get the output with minimal coding needed. They are preferred for rapid API consumption. SDKs provide the foundation to build fully customized AI applications, fine-tune model behavior, integrate advanced UI features, and potentially host the models locally. SDKs have a higher learning curve but are preferred for long-term customization, especially in local environments.

What Makes a Platform Enterprise-Ready?

A platform is enterprise-ready if it provides security, reliability, scalability, adaptability, and a strong set of technical and usability services. Security is critical for enterprises in highly regulated sectors such as healthcare, banking, and government that need to comply with a variety of industry laws. Data residency and privacy capabilities are vital, as are customizable access and permission controls.

Multi-region deployment support is important for globally distributed organizations while cost control functionality and strong vendor support are key factors for enterprise readiness. Typical enterprise AI platforms must offer service-level agreements (SLA), compliance certifications (ISO 27001, SOC2, FedRAMP, HIPAA, and others), scalability, support tiers, customization options, and billing flexibility.

• SLAs. Guarantees for uptime, reliability, and support. Outlines what happens if the vendor fails to meet the promised standards.
• Compliance certifications. Documents such as SOC 2, ISO 27001, or HIPAA that verify the platform is following established global best practices for data protection, security, and privacy.
• Scalability. The ability to add or remove users, compute, and other resources easily in both directions as needs change.
• Support tiers. A choice of support levels (e.g. business hours and 24/7 for customers spread out around the world). usually with extra services such as priority troubleshooting and support as you select higher paid tiers.
• Customization options. The ability to adapt a product or platform to the particular needs of an organization. The most secure and high-performance option for substantial customization is direct engineering and coding of the product if permitted by the vendor. For more accessible and less costly customization, low-code or no-code tools are preferred, with appropriate security and governance guardrails in place for less-skilled internal users.
• Billing flexibility. The ability to add or remove users, compute, and other resources easily in both directions as needs change.

Where Are Generative AI Tools Most Effective Today?

Open source generative AI tools are most effective where companies have an experienced development team and are seeking data privacy, security, and customizability or utility extensions. Closed and paid AI software is best for companies with less experienced teams who need quick productivity gains in popular areas like marketing automation or creative content pipelines with fewer compliance or data privacy needs.

Generative AI tools can transform work, improving productivity so companies can do more with limited resources. The tools can help improve customer experiences, strengthen marketing campaigns, and streamline workflows.

• Marketing automation: AI can generate personalized marketing messages, automate email sequencing, and produce engaging social media content. For instance, the AI-powered marketing platform Persado has shown a 41% average lift in conversions vs brand messaging.
• Research and engineering: AI can assist researchers in designing experiments and analyzing data, aiding engineers to develop new products and processes. Google’s AlphaFold has been used by scientists around the world to solve difficult R&D problems with protein folding.
• Creative content pipelines: AI can help copywriters and designers create engaging content quickly by generating headlines, blog posts, images, and videos. The content team at NVIDIA saw its productivity multiply as it used generative AI to accelerate both idea generation and copywriting.
• Customer service: AI-powered chatbots and virtual assistants answer customer questions, resolve problems, and process orders continuously freeing human agents to focus on more complex tasks. Juniper Research’s data is largely based on older forecasts. Their 2018 report projected global cost savings of approximately $11 billion annually by 2023 up from $6 billion in 2018 based on chatbot adoption in retail, banking, and healthcare More recent data on savings beyond 2023 isn’t publicly available, although research indicates continued growth in chatbot use through 2026. Overall, chatbots remain a key tool for reducing costs and improving customer service, but be sure to rely on the latest data for current planning and ROI estimates.
• Other establishment use cases for generative AI tools: Advances in AI models are enabling tools to perform increasingly complex tasks across business functions, including code generation, summarization of legal content, and technical content. As technology matures, AI is expected to handle more diverse and technical tasks.