Generative AI Infrastructure

Empowering Tomorrow's Innovations with Generative AI Infrastructure

Overview

Generative AI (GenAI) infrastructure covers the underlying technology and complex architecture used to develop, train, deploy, and monitor GenAI models.

Companies in this industry play a pivotal role in enabling the development and deployment of GenAI applications across various domains, including natural language processing, computer vision, and creative content generation. It encompasses diverse products and tools that support the lifecycle of GenAI models, including hardware, data storage, AI model management (development, training, deployment, monitoring), and prompt engineering.

* Note: Additional sections (such as market sizing, and incumbents) can be provided on request.

Industry Updates

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Market Mapping


Established startups compete with incumbents, while newer firms provide specialized offerings

Prominent growth-stage AI players, such as Scale AI and DataRobot, also provide integrated offerings for enterprise users that compete with incumbent solutions. Firms such as SambaNova Systems, Cerberus Systems, and Graphcore have raised significant funding to compete in the hardware infrastructure space. Another growing trend has been the entry of data infrastructure providers, such as Databricks, Snowflake, and Datasaur, leveraging their data expertise to introduce LLM and GenAI development solutions.

Startups established in the last few years have gravitated toward providing specialized solutions and services in a specific part of the GenAI value chain, such as prompt engineering, model monitoring, and data storage and retrieval.

More recently, there have been growing concerns over the safety of AI models due to data breaches, biases and inaccuracies, and impending regulations over copyright and the use of AI in sensitive areas. This has led to the emergence of specialized AI safety and governance tools, where firms offer solutions to develop and implement governance policies while improving the security of their models.

Incumbents
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Integrated LLMOps solutions
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Hardware infrastructure
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Model development and training
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Data storage and retrieval
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Model deployment and integration
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Model monitoring
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AI security and governance
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Prompt engineering
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Semiconductor
Semiconductor
Semiconductor
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Semiconductor
Semiconductor

The Disruptors


Funding History

Competitive Analysis


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Notable Investors


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Overview

Behind the Scenes with ChatGPT, DALL-E, and Bard 

Since the launch of OpenAI’s ChatGPT a year ago, “generative AI” (GenAI) is fast becoming a household term. 2023 has seen a sharp uptick in the usage of GenAI-powered solutions; one may even say we are spoiled for choice with the number of options currently available in the market. GenAI is now each person’s personal assistant, helping us with menial and creative tasks, such as answering questions (ChatGPT, Bard), writing (Jasper, Copy.ai), and creating images (DALL.E, Midjourney). GenAI has transcended technical usage, providing something for everyone, much like the emergence of the internet. 
Because of this boom, a slew of firms have quickly jumped into the GenAI market with different use cases and solutions that can be applied across various industries, hoping to make them “firsts.” GenAI is now here to stay as it becomes increasingly embedded into our everyday lives, which is reason enough for companies to use it in their products and services and to augment their business processes. 

What is GenAI Infrastructure?

GenAI Infrastructure (GenAI Infra) includes a range of tools and platforms used to develop, train, deploy, and monitor large language models (LLMs) as well as GenAI models and applications. After covering this industry for over 12 months, we have identified a vast support system under eight broad segments as follows.  

Key types of GenAI Infra solutions

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