벡터 데이터베이스 분야 에이전트형 AI 애플리케이션 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the agentic AI applications market size in the vector database market is expected to grow from USD 0.46 billion in 2025 to USD 0.57 billion in 2026, and is forecast to reach USD 1.73 billion by 2031 at a 24.86% CAGR over 2026-2031.

This report is Segmented by Deployment Mode (Cloud-Managed, and More), Vector DB Type (Purpose-Built Vector Databases, and More), Application (Conversational AI and Retrieval-Augmented Generation, and More), End-User Industry (IT and Telecom, BFSI, Healthcare and Life Sciences, Retail and E-Commerce, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Agentic AI Applications In Vector Database Market Trends and Insights

Proliferation Of Large Language Models Driving High-Dimensional Retrieval

Agentic AI applications in the vector database market are benefiting from the shift of large language models from experimentation to production infrastructure. Each enterprise pipeline that combines LLM inference with live internal data requires a vector index that can handle large embedding volumes with low, stable retrieval latency. The pressure is increasing because many organizations are no longer standardizing on a single model and are instead supporting several models that operate in different embedding spaces. That forces them to maintain multiple indexes and raises storage, orchestration, and compute demand inside the same environment. MongoDB introduced 5 Voyage 4 embedding models in January 2026, including a multimodal option with video capability, and integrated them into Atlas Vector Search to reduce dependence on external embedding calls. That kind of integration shows how the agentic AI applications in the vector database market are expanding alongside model diversity rather than around a single retrieval pattern.

Rise of Agentic AI Architectures Necessitating Persistent Memory Stores

The agentic AI applications in the vector database market are also being pushed forward by agentic systems that plan, retrieve, reason, and act across multiple sessions. These workloads differ from static RAG because they can execute thousands of vector lookups for one task and also write new memory back into the system while the task is still active. Qdrant stated in April 2026 that production agent loops generate multi-thousand queries per workflow, while traditional RAG workloads remain much lighter. Enterprises also expect full observability, audit trails, and access controls because every agent action must be explainable to internal governance teams. Amazon Bedrock AgentCore reached general availability in October 2025, introducing persistent memory, semantic retrieval, and native OpenTelemetry observability, raising the baseline for enterprise deployments. As a result, the agentic AI applications in the vector database market are favoring providers that combine performance with governance rather than solely on retrieval speed.

High Compute And Storage Costs For Billion-Scale Indexes

Agentic AI applications in the vector database market face a real cost ceiling as deployments move from millions of vectors to billions. HNSW indexes remain memory-intensive, and a 1-billion-vector dataset with 1,536 dimensions requires substantial RAM before quantization is even applied. That pushes managed cloud spending to levels that can weaken the business case for mid-market users and for enterprises testing multiple agent workflows simultaneously. Qdrant highlights binary quantization that cuts memory use by 32x while preserving more than 95% recall, but the tradeoff still depends on workload design and tolerance for retrieval drift. The pressure is sharper for agent memory systems because frequent writes increase rebuild frequency and infrastructure load compared with static retrieval systems. Tencent Cloud said its enterprise vector database handled more than 850 billion daily retrieval requests across internal Tencent businesses in 2025, which shows how scale efficiency remains concentrated among the largest operators.

Other drivers and restraints analyzed in the detailed report include:

1. Cloud Providers Embedding Native Vector Capabilities Into AI Stacks
2. Open-Source Vector Engines Lowering Total Cost of Ownership
3. Lack of Standardized Benchmarks And Interoperability

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Within the agentic AI applications market for vector databases, cloud-managed deployments held a 62.31% share in 2025, as buyers favored elasticity, managed availability, and low infrastructure overhead. Cloud-managed services shorten deployment time for AI teams by handling indexing, scaling, failover, and routine maintenance within the platform. The model also fits enterprise buying behavior because vector retrieval is increasingly bundled into broader AI subscriptions rather than purchased as a separate system. Amazon Bedrock AgentCore reinforced that pattern in 2025 by combining persistent memory and semantic retrieval inside a managed service stack.

In the agentic AI applications market for vector databases, self-hosted deployments remain relevant in healthcare, government, and heavily regulated enterprise environments where residency and control remain central. Hybrid deployments are projected to expand at a 24.81% CAGR through 2031 as organizations seek cloud-like operations without losing control of the execution environment. Zilliz positioned directly into that demand with BYOC-I and BYOC Azure options that let customers keep the engine inside their own tenant while retaining vendor support and managed updates. That makes hybrid less of a compromise and more of a default architecture for the agentic AI applications in the vector database market across multi-region enterprises.

Within the vector database market, purpose-built vector databases captured 55.73% of the agentic AI applications share in 2025 because they are designed from the ground up for high-dimensional similarity search. Their value is strongest where latency targets are tight, index sizes are large, and retrieval quality must remain stable under production load. Qdrant reported p50 query latency of 3 milliseconds and p99 latency of 14 milliseconds for 1 million vectors at 768 dimensions, which illustrates why purpose-built engines remain attractive for core workloads. Vector-enabled relational and document stores still matter because they let enterprises add semantic retrieval to existing application databases without introducing another infrastructure layer.

In the agentic AI applications market for vector databases, embedded and edge vector stores are forecast to grow at a 28.33% CAGR through 2031 as AI inference moves closer to the point of action. Qdrant launched Qdrant Edge in July 2025 as an in-process vector library for mobile devices, robots, and resource-constrained hardware. Actian followed in April 2026 with VectorAI DB, aimed at environments ranging from Raspberry Pi systems to enterprise edge servers. This segment is gaining ground in the agentic AI applications market for the vector databases because local search reduces latency, supports offline execution, and meets data minimization requirements.

Geography Analysis

Within the agentic AI applications market for vector databases, North America held a 41.11% share in 2025 and remained the leading regional revenue base, as enterprise AI deployments moved into production earlier than in most other regions. The United States led regional spending through large rollouts across financial services, healthcare, and enterprise software, while Canada added support through its research clusters and startup ecosystem. Mexico also contributed through the expansion of nearshore technology services and the wider use of AI-enabled customer engagement platforms in regional delivery centers. The region's regulatory demands are also shaping product design, and managed vendors have already added healthcare-focused compliance features to support U.S. adoption.

Within the agentic AI applications in the vector database market, Asia-Pacific is the fastest-growing region, and the market size in the region is projected to grow at a 23.97% CAGR through 2031. China is a major demand center because domestic AI infrastructure investment is growing, and Tencent Cloud said its enterprise vector database handled more than 850 billion daily retrieval requests across internal Tencent businesses in 2025. Japan is building demand through large-enterprise knowledge management and compliance retrieval use cases that carry high contract value even when deployment counts remain lower. India is supporting growth through its large developer base and IT services sector, which is increasingly evaluating vector platforms for public and enterprise RAG programs. South Korea is strengthening the region's role in embedded deployment because manufacturers are using agentic AI for quality control and supply chain workflows that depend on local vector stores.

Europe has a distinct role in the agentic AI applications market for vector databases because GDPR and the EU AI Act are pushing buyers toward resident infrastructure and stronger governance features. Zilliz made BYOC Azure with customer-managed encryption keys generally available in March 2026, directly addressing those sovereignty requirements within customer-controlled environments. South America remains smaller, with Brazil as the main node, as cloud investment expands across the region. Th Middle East and Africa are gaining momentum through sovereign AI programs, and the Stargate UAE project in Abu Dhabi is building a 1-gigawatt compute base,e with an initial 200 MW phase expected to be operational in Q2 2026.

1. Pinecone Systems Inc.
2. Zilliz Inc.
3. Semi Technologies B.V. (Weaviate)
4. Elastic N.V.
5. Redis Ltd.
6. Chroma Inc.
7. Qdrant GmbH
8. Oracle Corporation
9. MongoDB Inc.
10. Neo4j, Inc.
11. Microsoft Corporation
12. Amazon Web Services, Inc.
13. Google LLC
14. IBM Corporation
15. Databricks Inc.
16. Snowflake Inc.
17. Couchbase, Inc.
18. Alibaba Cloud (Alibaba Group)
19. Tencent Cloud
20. Vespa.ai

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Proliferation of Large Language Models Driving High-Dimensional Retrieval
  • 4.2.2 Rise of Agentic AI Architectures Necessitating Persistent Memory Stores
  • 4.2.3 Cloud Providers Embedding Native Vector Capabilities into AI Stacks
  • 4.2.4 Open-Source Vector Engines Lowering Total Cost of Ownership
  • 4.2.5 Edge AI Adoption Spurring Demand for Embedded Vector Stores
  • 4.2.6 Venture Capital Influx Accelerating Product Innovation
• 4.3 Market Restraints
  • 4.3.1 Lack of Standardized Benchmarks and Interoperability
  • 4.3.2 High Compute and Storage Costs for Billion-Scale Indexes
  • 4.3.3 Data-Sovereignty Regulations Restricting Cross-Border Vector Sharing
  • 4.3.4 Scarcity of Skilled Vector Similarity Engineers
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intenssity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment Mode
  • 5.1.1 Cloud-Managed
  • 5.1.2 Self-Hosted
  • 5.1.3 Hybrid
• 5.2 By Vector Database Type
  • 5.2.1 Purpose-Built Vector Databases
  • 5.2.2 Vector-Enabled Relational/Document Stores
  • 5.2.3 Embedded/Edge Vector Stores
• 5.3 By Application
  • 5.3.1 Conversational AI and Retrieval-Augmented Generation
  • 5.3.2 Autonomous Agents and Workflow Orchestration
  • 5.3.3 Semantic Search and Recommendation
  • 5.3.4 Fraud Detection and Anomaly Analytics
  • 5.3.5 Bioinformatics and Scientific Computing
• 5.4 By End-User Industry
  • 5.4.1 IT and Telecom
  • 5.4.2 BFSI
  • 5.4.3 Healthcare and Life Sciences
  • 5.4.4 Retail and E-Commerce
  • 5.4.5 Media and Entertainment
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 United Arab Emirates
      • 5.5.5.1.2 Saudi Arabia
      • 5.5.5.1.3 Rest of Middle East
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Egypt
      • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Pinecone Systems Inc.
  • 6.4.2 Zilliz Inc.
  • 6.4.3 Semi Technologies B.V. (Weaviate)
  • 6.4.4 Elastic N.V.
  • 6.4.5 Redis Ltd.
  • 6.4.6 Chroma Inc.
  • 6.4.7 Qdrant GmbH
  • 6.4.8 Oracle Corporation
  • 6.4.9 MongoDB Inc.
  • 6.4.10 Neo4j, Inc.
  • 6.4.11 Microsoft Corporation
  • 6.4.12 Amazon Web Services, Inc.
  • 6.4.13 Google LLC
  • 6.4.14 IBM Corporation
  • 6.4.15 Databricks Inc.
  • 6.4.16 Snowflake Inc.
  • 6.4.17 Couchbase, Inc.
  • 6.4.18 Alibaba Cloud (Alibaba Group)
  • 6.4.19 Tencent Cloud
  • 6.4.20 Vespa.ai

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment