How big is the retrieval augmented generation market today, and what are its future growth expectations?
The retrieval augmented generation market size has grown exponentially in recent years. It will grow from $1.50 billion in 2024 to $2.13 billion in 2025 at a compound annual growth rate (CAGR) of 41.6%. The growth in the historic period can be attributed to enterprise adoption, demand for accurate AI content, rise in data generation, enhancement of AI model accuracy, regulatory compliance needs, and adoption of RAG in content creation and recommendation systems.
The retrieval augmented generation market size is expected to see exponential growth in the next few years. It will grow to $8.45 billion in 2029 at a compound annual growth rate (CAGR) of 41.2%. The growth in the forecast period can be attributed to retail and e-commerce personalization, increasing usage in document retrieval and summarization, contributions from the open-source community, and global digital transformation. Major trends in the forecast period include advanced technologies, the emergence of AI-powered recommender systems, advancements in natural language processing (NLP), strategic partnerships and collaborations, and research and development initiatives.
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What have been the primary factors driving the retrieval augmented generation market’s growth?
The growing demand for data security is expected to propel the growth of the retrieval-augmented generation market going forward. Data security refers to the practice of safeguarding digital information against theft, corruption, illegal access, and disclosure at every stage of its lifespan. The growing demand for data security is due to increasing cyber threats, data breaches, regulatory compliance requirements, and the need to protect sensitive personal, financial, and business information in an increasingly digital world. Retrieval-augmented generation (RAG) enhances data security by enabling AI systems to securely access and retrieve relevant information from trusted sources in real time, ensuring that sensitive data is processed and used only when necessary, minimizing exposure to potential breaches while maintaining privacy and compliance standards. For instance, in April 2024, according to the Gov.UK, a UK-based government website providing a single point of access to HM Government services, in 2023, the UK cybersecurity sector grew by 8% with nearly 2,000 firms, and its revenue reached £10.5 billion, reflecting a 3% increase from 2022. Therefore, the growing demand for data security will drive the growth of the retrieval-augmented generation market.
What are the key segments within the retrieval augmented generation market?
The retrieval augmented generation market covered in this report is segmented –
1) By Data Type: Structured Data, Unstructured Data
2) By Function: Document Retrieval, Response Generation, Summarization And Reporting, Recommendation Engines
3) By Deployment: Cloud, On-Premises
4) By Application: Knowledge Management, Customer Support And Chatbots, Legal And Compliance, Marketing And Sales, Research And Development, Content Generation
5) By End-Use: Healthcare, Financial Services, Retail And E-Commerce, Information Technology And Telecommunications, Education, Media And Entertainment, Other End-Users
Subsegments:
1) By Structured Data: Relational Databases, Knowledge Graphs, Enterprise Data Warehouses, Metadata Repositories, Linked Data
2) By Unstructured Data: Text Documents, Audio And Video Transcripts, Web Content And Blogs, Emails And Customer Support Logs, Social Media Posts
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Which key players are shaping the retrieval augmented generation market?
Major companies operating in the retrieval augmented generation market are Apple Inc., Google LLC, Samsung Electronics Co. Ltd., Microsoft Corporation, Meta Platforms Inc., Dell Technologies Inc., Amazon Web Services Inc., Intel Corporation, Accenture Plc, IBM Corporation, Cisco Systems Inc., Qualcomm Incorporated, Oracle Corporation, SAP SE, Hewlett Packard Enterprise (HPE), NVIDIA Corporation, Salesforce Inc., Ericsson AB, Adobe Inc., Alibaba Cloud, OpenAI LLC, Informatica Inc., Databricks Inc., Clarifai Inc.
How will emerging trends drive the retrieval augmented generation market throughout the forecast period?
Major companies operating in the retrieval-augmented generation market are focusing on advanced technologies, such as decentralized AI operating systems, to enhance their offerings and improve operational efficiencies. A decentralized AI operating system refers to an AI-driven system where decision-making, data processing, and control are distributed across multiple nodes or devices rather than being managed by a central server or authority. For instance, in July 2024, Core42 – G42, a UAE-based provider of digital transformation, launched OnDemand AI Operating System (AIOS) to streamline the deployment of AI applications, providing a comprehensive solution for developers, engineers, enterprises, and startups. It offers unique features such as the flexible deployment of both open-source and proprietary AI models, including integration with Hugging Face. It features a plugins and marketplace ecosystem for developers to create, deploy, and sell AI tools. Additionally, AIOS supports multi-step Retrieval-Augmented Generation (RAG) for complex API integrations and provides a low-code solution, making AI development accessible with minimal setup.
How do regional factors impact the retrieval augmented generation market, and which region is the largest contributor?
North America was the largest region in the retrieval augmented generation market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the retrieval augmented generation market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Retrieval Augmented Generation Market Report 2025 Offer?
The retrieval augmented generation market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Retrieval-augmented generation refers to the technique that improves the capabilities of large language models (LLMs) by using external information retrieval techniques. This method enables LLMs to use authoritative information repositories, boosting the accuracy and relevancy of their generated responses.
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