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Generative AI companies — both existing enterprises that are adding generative AI to their solution stacks and new generative AI startups — are popping up everywhere and quickly.

But what makes generative AI companies so different from other AI and ML companies? What are they offering that creates enough demand and buzz to earn funding from the top venture capital firms? In this guide, we’ll cover the top generative AI companies, their products and use cases, as well as a deep dive into what generative AI is and why it’s growing in popularity.

Also see: Generative AI Startups 

Also see: 100+ Top AI Companies

What Is Generative AI?

Generative AI is a kind of artificial intelligence that uses data analytics training sets, natural language processing, neural networks, and deep learning to create new and original content.

Generative AI content can be created for personal or business use and can take the form of text, images, video, audio, synthetic data, and object models. The most prominent instances of generative AI today are generative language modeling, writing, and imagery tools, such as ChatGPT and Stable Diffusion.

Learn more: What is Generative AI?

Generative AI Companies: Table of Contents

Best Generative AI Companies: Comparison Table

Company	Differentiator	Focus area(s)	Company valuation/Enterprise value	Products
OpenAI	Best Overall	Language modeling Content generation and editing	Private company; estimated at around $29 billion.	GPT-3 GPT-4 ChatGPT Plus DALL-E Whisper InstructGPT
Hugging Face	Best for Community-Driven AI Development	Generative AI forums Open-source AI model development	Private company; estimated at around $2 billion.	BLOOM Expert Acceleration Program Private Hub Inference Endpoints AutoTrain Hardware
Alphabet (Google)	Best for Scalability	Generative AI model development and deployment Application development Content generation	$1.72 trillion.	Generative AI support in Vertex AI Generative AI App Builder Generative AI in Workspace Bard
Microsoft	Best for Business Operations and Productivity	Content generation Generative AI-powered search Code development and support Cybersecurity	$2.25 trillion.	Microsoft 365 Copilot Microsoft Security Copilot GitHub Copilot AI-Enhanced Bing and Edge
Cohere	Best for Natural Language Processing	Natural language processing and modeling Enterprise conversational AI	Private company; estimated at around $6 billion.	Embed Neural Search Summarize Generate Classify
Anthropic	Best for Customizable Content Generation	Content generation and editing Text classification and summarization Customer support	Private company; estimated at around $5 billion.
Jasper	Best for Marketers	Content generation Language modeling	Private company; estimated at around $1.5 billion.
Glean	Best for Employee User Experience	Cognitive enterprise search Knowledge management	Private company; estimated at around $1 billion.	Glean Search Glean Knowledge Management Glean Work Hub
Synthesis AI	Best Variety of Generative AI Use Cases	Synthetic data, image, and video generation 3D human modeling Automotive/pedestrian monitoring Cybersecurity	Private company; funding of $21.5 million+.	Synthesis Humans Synthesis Scenarios Data Visualizer
Stability AI	Best Foundational Model for Other Generative AI Solutions	Content generation and editing Open-source AI app development	Private company; estimated at around $1 billion.	Stable Diffusion 2.0 Stable Diffusion Reimagine DreamStudio Photoshop Plugin Blender Plugin Platform API
Lightricks	Best for Personal and Creative Use	Content generation and editing Art, image, and video editing	Private company; estimated at around $1.8 billion.	Facetune Photoleap Videoleap Popular Pays Filtertune Beatleap Motionleap Artleap
Inflection AI	Best Future-Looking Vision	Human-to-computer communication in plain language Voice search	Private company; current investments around $225 million, with a $675 million funding round expected soon.	No products publicly offered yet.

Leading Generative AI Companies

OpenAI

Best Overall

If you’ve lately heard talk about generative AI, chances are OpenAI and its products, like ChatGPT, came up in the conversation. OpenAI is the most successful generative AI companies to date, worth an estimated $29 billion and backed by major tech companies like Microsoft.

Beyond its currently-free content generation solution, ChatGPT, and image generation solution, DALL-E, OpenAI also offers its API and different models to support companies in their generative AI development efforts. GPT-4, chat models, instruct models, fine-tuning models, audio models, image models, and embedding models can all be customized for a usage fee to meet individual business needs.

Pros

• A well-funded firm with a good variety of generative AI solutions.
• Governed by a nonprofit with a capped-profit model.
• General availability of the OpenAI API.

Cons

• Occasionally generates inaccurate or even offensive content.
• Real-time news and data are not quickly incorporated into ChatGPT’s knowledge base; for example, ChatGPT is not able to accurately say what today’s most popular generative AI companies are.
• Certain models can become extremely expensive to use, depending on your usage requirements.

On a related topic: The AI Market: An Overview

Hugging Face

Best for Community-Driven AI Development

Hugging Face is a community-driven developer forum for AI and ML model development initiatives. Its wide variety of prediction models and datasets makes it possible for organizations to custom-build their own generative AI solutions and other AI toolsets.

AWS recently became a Hugging Face partner and now offers Hugging Face products directly to its customers. A number of other companies work on Hugging Face to optimize existing AI models and develop new ones from scratch. Although the forum is designed with developers and programmers in mind, certain Hugging Face solutions, like AutoTrain, require little to no coding.

Pros

• Open-source, collaborative development environment.
• Partnership with AWS.
• Embeddable generative AI technology for affordable scalability.

Cons

• Its developer-facing format makes it less friendly to non-technical users.
• Limited governance over third-party development tools, like Stable Diffusion.
• Limited customer support availability.

On a related topic: The Future of Artificial Intelligence 

Alphabet (Google)

Best for Scalability

Although most outlets would consider Microsoft ahead of Google in the generative AI race today, Google is building a foundation that looks promising for its future generative AI plans. Google is working on office suite, search, and text-based generative AI tools, much like Microsoft, but the company is actually focused on building a cloud ecosystem that operates with generative AI support integrated throughout. For example, a small group of customers is currently able to test generative AI functionality in Vertex AI and Generative AI App Builder.

What’s more, the company is developing AI with scalability and ethics both at the forefront. Its AI Principles were established in 2017 to guide AI development, and Google regularly releases reports about how they’re putting those principles to work in their latest releases and product updates.

Pros

• The generative AI lab, DeepMind, is an innovative player in the space and a subsidiary of Alphabet.
• Google takes a very comprehensive and transparent approach to AI ethics.
• Google Cloud infrastructure for AI is optimized for both cost and high performance.

Cons

• The company’s initial hesitancy to roll out generative AI could keep it behind other players for some time.
• On the flip side, Google’s race to keep up with Microsoft and other players could lead to the company rolling out tools that have not been thoroughly tested and vetted.
• Many of Google’s programs are only available through the Trusted Tester Program currently.

And: ChatGPT vs. Google Bard: Generative AI Comparison

Microsoft

Best for Business Operations and Productivity

Microsoft is one of the most dynamic leaders in generative AI today, developing many of its own generative AI tools while supporting and funding new technologies from OpenAI. Bing, the Microsoft-owned search engine, has recently been transformed and become the first major search engine to incorporate generative AI functions via chatbot. Microsoft also recently released generative AI content features across Microsoft 365 products.

Among Microsoft’s most exciting AI innovations is Copilot, a GPT-4-powered assistive tool that is now integrated into several Microsoft applications. These are the main instances of Copilot that are available today:

• Microsoft 365 Copilot: Assistive content generation in Microsoft 365 apps, like Word and Excel.
• Microsoft Dynamics 365 Copilot: The world’s first generative AI solution for CRM and ERP.
• Microsoft Security Copilot: A cybersecurity and incident response solution that is now in preview.

Pros

• Its relationship with OpenAI gives Microsoft access to build a number of tools on GPT-4 and other emerging OpenAI solutions.
• The company has already built intuitive generative AI tools into its office suite products and is beginning to dip into other areas like cybersecurity.
• Users can take advantage of contextualized, generative AI search for free in Bing.

Cons

• Much of the AI ethics and society workforce at Microsoft was recently laid off, though Microsoft still has an Office of Responsible AI.
• Microsoft is perhaps moving too fast — in efforts to beat its competition to cutting-edge generative AI products — and is potentially not considering the implications of its new releases.
• Many of Microsoft’s generative AI developments are based on OpenAI products; with OpenAI’s own success and hopes for growth, it’s difficult to say if this will impact Microsoft’s own scalability over time.

Also see: Cloud and AI Combined: Revolutionizing Tech

Cohere

Best for Natural Language Processing

Cohere offers a variety of high-powered natural language processing tools for text retrieval, classification, and generation. Its approach to large language models is comprehensive, not only giving users the ability to generate new content but also to search and summarize large sets of pre-written content. With a user-friendly API, app integrations, and quickstart guides, Cohere makes it possible and encourages companies to customize Cohere products to meet their own requirements.

Cohere’s main products are Neural Search, Summarize, Generate, Classify, and Embed. Here’s what each of these tools can do:

• Neural Search: Semantic text search for documents in 100+ languages.
• Summarize: Document, email, and article summarization.
• Generate: Content generation with a focus on marketing and sales use cases.
• Classify: Text classification for customer support, sentiment analysis, etc.
• Embed: Build your own text analysis applications with semantic search, topic modeling, recommendations, and multilingual options.

Pros

• Language models can be customized to specific enterprise requirements; resources for developers are extensive and the API is user-friendly.
• Models can operate in public, private, and/or hybrid cloud environments.
• Works with 100+ languages.

Cons

• Pricing can get expensive, especially if you need dedicated models, support channels, or extensive customizations.
• Somewhat limited integrations once you get past Cohere’s highly functional API.
• NLP development technology, like Cohere products, is notoriously difficult to understand and develop; customization of Cohere tools will require high levels of expertise.

For more information: AI vs. ML 

Anthropic

Best for Customizable Content Generation

Anthropic is a leading generative AI startup that believes quality and safety should take precedence over quantity and speed. Its team is made up of AI researchers and engineers but also policy experts, business leaders, and stakeholders from across government, academic, nonprofit, and industrial backgrounds.

Anthropic’s flagship product is Claude, an AI assistant that focuses on high-quality content generation, summarization, and explanations. Claude is highly customizable and can be used for workflow automation, natural conversation, text processing, and Q&A. These are some of the ways different industries and enterprise teams currently use Claude.

• Customer service: Friendly customer service conversations, with the ability to hand off tasks to human reps when necessary.
• Legal: Legal document analysis and summarization.
• Administrative office tasks: Email and document content summarization and categorization.
• Sales: Virtual sales representative with customizable personality, tone, and behaviors.

Although the company’s only public-facing solution is Claude, Anthropic is developing other large-scale AI systems behind the scenes and is focused on safety and problem-solving.  Here is its extensive library of AI research.

Pros

• Transparent, extensive research is available for public reading.
• Claude is designed to be useful while avoiding harmful content outputs.
• Generally focused on safe and steerable product development.

Cons

• Limited public-facing scope; Claude mostly provides assistance through text generation, classification, and summarization.
• Anthropic’s focus on extensive safety and performance testing leads to slower product rollouts; only Claude is available to the public.
• Claude-v1 can get expensive for larger tasks; however, Claude Instant is a more affordable option for these kinds of cases.

On a related subject: Algorithms and AI

Jasper

Best for Marketers

Jasper is a generative AI favorite for marketers and content creators, offering features to support blog and email writing, SEO optimization, and art and ad imagery creation. It’s easy to use and access, with both Chrome and Microsoft Edge extensions and the recent launch of in-line experiences.

Jasper has always had a business bent with its focus on marketer-style content, but in February 2023, the company took it to a new level with its announcement of Jasper for Business. This suite of business enhancements includes Jasper Brand Voice, which allows customers to train Jasper on their brand’s specific tone, style, and language. The company now also offers Jasper API to help marketers integrate Jasper into their pre-existing tool stacks and custom CMS builds.

Pros

• Focus on branding is a unique differentiator among top generative AI competitors.
• Intuitive AI Engine curates model selection for different job requests.
• Easy-to-use interface, especially with browser extensions and in-line cursors.

Cons

• Pricing can get expensive, depending on how many words per month you’re generating.
• Limited user counts outside of the highest-tier pricing plan.
• Jasper Brand Voice features are only available in the highest-tier pricing plan.

For more information, also see: Best Machine Learning Platforms

Glean

Best for Employee User Experience

Glean offers generative AI-powered internal search for workplace apps and ecosystems. Companies across different industries and backgrounds use Glean to make it easier for employees to search for company knowledge and contextualize that information to their roles.

The way Glean is designed, each company has its own dynamic knowledge graph that learns and adapts to specific people, interactions, and content requests. Through this approach, everyone from your engineering team to your sales team can use Glean to find the information they need more quickly and easily. Other key features that make this a highly usable tool include:

• Verified answers: Save and verify answers to frequently asked questions.
• Curated collections: The ability for individual teams to collect and organize documents and links that are most relevant for their team; ideal for onboarding.
• GoLinks: Short links that can be created and saved for your most commonly used resources.

Pros

• Despite its connection to all kinds of enterprise apps and databases, Glean’s tooling respects and enforces security permissions on all fronts.
• The user interface is clean and easy to understand.
• Semantic understanding supports more personalized search results and AI-generated answers.

Cons

• Though it’s advertised as an enterprise support tool, Glean functionality doesn’t stretch much beyond cognitive enterprise search and knowledge storage.
• Glean operates with a smaller support and development team than many other players on this list.
• Limited transparency for product pricing.

For more information, also see: Top Robotics Startup

Synthesis AI

Best Variety of Generative AI Use Cases

Synthesis AI is one of the smallest companies on this list if you look strictly at enterprise value. However, it’s one of the biggest and most promising when you consider the variety of products and solutions the company already offers its customers.

Synthesis AI focuses mostly on synthetic data, image, and video generation for computer vision; the applications of these synthetic creations are numerous. Some of the use cases and applications currently being fulfilled by Synthesis AI’s Humans and Scenarios products include the following:

• Identify verification.
• Avatar creation.
• Driver monitoring.
• Pedestrian detection.
• AR, VR, and XR.
• Teleconferencing.
• Cybersecurity.
• Virtual try-on.

Pros

• Effective video, data, and image labeling, particularly for humans.
• Focus on AI ethics and diversity.
• Capable of photorealistic image generation.

Cons

• Limited visibility into the company’s profitability and stability metrics.
• Limited outside funding rounds at this point in time.
• Human-centric data focus limits its relevance for text and non-human image generation.

On a related topic: Top Natural Language Processing Companies

Stability AI

Best Foundational Model for Other Generative AI Solutions

Stability AI is the engine that powers many of the latest and greatest generative AI solutions. The company’s deep learning model, Stable Diffusion, offers open-source code — primarily via GitHub and Hugging Face — that several other companies have opted to build off of for image and video generation. The company also offers an extensive API library that third-party users can take advantage of, as well as a Discord community where users can discuss how they use Stable Diffusion technology.

The company has faced controversy for its image-sourcing practices, and there’s also talk of profitability problems in the organization, but it’s difficult to deny the influence of a company with over 140,000 members in its open-source research hubs and a slew of customers (some even mentioned on this list).

Pros

• Open-source accessibility makes Stability AI tools highly customizable for experienced developers.
• Strong open-source communities in Discord, GitHub, and Hugging Face.
• A variety of relevant plugins and APIs are available to users.

Cons

• Costs for DreamStudio and API usage can go up quickly, depending on how many credits you need.
• High spending on foundational technology has hurt profitability for the company.

For more information, also see: History of AI

Lightricks

Best for Personal and Creative Use

Lightricks first came into the spotlight with its mobile photo editing app, Facetune, in 2013. It has since developed many different image and video editing solutions, as well as content generation solutions.

Lightricks made the shift into generative AI when it launched a text-to-image generator within its apps in 2022. This new feature makes it possible for users to create custom art and other images through their own or pre-written prompts. From there, they can edit and splice their imagery into animation and 3D motion creations.

Pros

• Consumer-friendly tool.
• Users can easily create animated videos and art.
• Users can edit both photos and AI-generated imagery.

Cons

• Little relevance to business use cases.
• Generated images do not appear photorealistic, which might not work for some use cases.

For more information, also see: Top Robotics Startups

Inflection AI

Best Future-Looking Vision

Inflection AI looks a little different than the other top generative AI companies on this list because they have not yet released a product. However, their vision and the pedigree of their founders and investors make it a compelling company to watch over the next several months and years.

The founders of Inflection AI are:

• Mustafa Suleyman: Co-founder of DeepMind, former Head of Applied AI at DeepMind, former VP of AI products and AI policy at Google, and venture partner at Greylock Partners.
• Reid Hoffman: Co-founder of Linkedin, former executive vice president at PayPal, and venture partner at Greylock Partners.
• Karén Simonyan: Chief Scientist at Inflection AI and former Principal Scientist at DeepMind.

These leaders and their small team are working to make human-to-computer communication possible through plain language. In a similar vein, they’re also working on advanced voice search capabilities.

If the company’s next funding round of $675 million comes through, the company will have received nearly $1 billion in funding; investors already believe in this team’s potential despite their lack of product at this time.

Pros

• Founded by technologists who used to be leaders/founders at DeepMind, Google, and LinkedIn.
• Focused on teaching machines to understand human language for improved human-machine interaction.
• Promises communication tools that will be accessible to people who are not familiar with computer and programming languages.

Cons

• Limited clarity on when the first product(s) will be launched.
• Limited visibility into what product(s) the company is building.
• The company is incredibly new and still in the process of building its team; it’s currently a team of fewer than 30 people.

For more information, also see: Cloud and AI Combined: Revolutionizing Tech 

Generative AI Companies: FAQ

Why Is Generative AI Growing In Popularity?

Generative AI is growing in popularity because it simplifies different types of tasks and democratizes access to high-value AI-created content. Although creatives in particular feel threatened by the existence of tools like ChatGPT and DALL-E, business owners, leaders, and private consumers alike are enjoying the opportunity to create compelling content through simple queries.

Generative AI also makes it possible for app and model developers to create better experiences in areas like code development, gaming, AR/VR/XR, and customer service.

How Does Generative AI Work?

Generative AI technology is typically designed with neural network algorithms that mimic the design and behavior of a human brain. With that setup, generative AI models are given massive training datasets to analyze and use as their knowledge base when generating new content.

The amount and variety of training data that go into these neural networks make it so generative AI tools can effectively learn data patterns and contextual relationships, then apply that knowledge to the content they create. The success of a generative AI solution is based heavily on the quantity, quality, variety, and neutrality of the training data it’s fed.

Who Is Investing in Generative AI?

Big tech companies like Microsoft, Google, and AWS are investing in generative AI startups and technology. For example, Microsoft is one of the biggest investors in OpenAI. Many of these companies are working to develop their own generative AI tools and operations as well.

Methodology for Generative AI Companies

The top generative AI companies in this list were selected based on a number of factors: current valuation, popularity with users, variety and relevance of products and use cases, ethicality, and potential to scale. Any known customer reviews and/or controversies were also noted throughout the review process. Pricing information was also incorporated into these rankings, where applicable.

Also see: What is Artificial Intelligence?

Bottom Line: Generative AI Companies

Generative AI companies offer compelling AI technology not only to technical users and developers but to the everyday consumer. The companies in this list have put forth some of the most interesting generative AI tools and use cases to date and are worth watching if you’re keeping an eye on the future of AI technology.

Some of these companies have had a meteoric launch, releasing several different products and generating millions of dollars in funding. On the other hand, a few of these organizations have taken a slower and steadier approach, first focusing on their idea and the ethics and safety behind development before going all in on a product launch. In all of these cases, the top generative AI companies are creating solutions that have the potential to scale with business and private user expectations in the long run.

On a related topic: Top Natural Language Processing Companies