Vector database usage on the Databricks platform grew 377% in a single year across a base of 10,000 customers (Databricks, State of Data + AI 2024). Analysts put the 2025 market at $2.58 billion to $3.02 billion and expect growth of 23.6% to 27.5% a year, while Gartner’s database forecast names vector databases the fastest-growing database segment, at a 75.3% CAGR. Retrieval-augmented generation (RAG) is now the main enterprise use: Menlo Ventures found it in 51% of generative AI implementations, up from 31% a year earlier (see our generative AI statistics for the wider spending picture). We aggregated data from Gartner, MarketsandMarkets, Fortune Business Insights, Menlo Ventures, Retool, Databricks, DB-Engines, Stack Overflow, AWS, and vendor announcements from Pinecone, Qdrant and Zilliz.
TL;DR
- The global vector database market is projected to grow from $2.65 billion in 2025 to $8.95 billion in 2030, a 27.5% CAGR (MarketsandMarkets 2025)
- Gartner expects the total DBMS market to grow 18.4% to $161 billion in 2026, with vector databases growing fastest at a 75.3% CAGR (Gartner DBMS Forecast 2025 Update)
- Asia Pacific is the fastest-growing region, expanding at a 30.2% CAGR to $2.46 billion by 2030 (MarketsandMarkets 2025)
- 63.6% of AI practitioners surveyed used a vector database in 2024, up from 20% in 2023 (Retool State of AI H1 2024)
- RAG appeared in 51% of enterprise generative AI implementations in 2024, up from 31% (Menlo Ventures 2024)
- Vector database usage grew 377% year over year among Databricks customers (Databricks State of Data + AI 2024)
- Elasticsearch scores 95.50 on the DB-Engines vector DBMS ranking, more than 10 times the score of Pinecone, the top purpose-built vector database (9.07) (DB-Engines, September 2026)
- 17.9% of developers who build AI agents use pgvector, against 11.2% for Pinecone (Stack Overflow Developer Survey 2025)
- On 1.4 billion vectors, Pinecone reports 5,700 queries per second with a 26 ms median latency (Pinecone, Dedicated Read Nodes 2025)
- Amazon S3 Vectors stored 40 billion vectors in its first four months and claims up to 90% lower cost than specialized vector databases (AWS 2025)
- Qdrant raised a $50 million Series B in March 2026, bringing its total funding to $87.8 million (Qdrant 2026)
- 63% of organizations lack, or are unsure they have, the right data management practices for AI (Gartner 2025)
1. Market Size and Growth Forecasts
Analysts disagree on the size of the market today but agree on how fast it is growing. Their 2025 estimates differ by about $440 million ($2.58 billion against $3.02 billion), mostly because they define the category differently, yet every forecast lands between 23.6% and 27.5% a year. MarketsandMarkets projects the market will more than triple, to $8.95 billion, by 2030.
The category is still small next to the database market as a whole. The highest 2026 estimate, $3.73 billion from The Business Research Company, is about 2.3% of Gartner’s $161 billion DBMS forecast for the same year (3.73 / 161 = 0.023), and these two estimates use different scopes. A 75.3% segment CAGR inside a market growing 18.4% suggests vector search is becoming a standard feature of databases, not a niche product. The figures come from the MarketsandMarkets Vector Database Market report, the Fortune Business Insights vector database analysis and the Business Research Company global market report.
| Metric | Value | Source |
|---|---|---|
| Global vector database market, 2024 to 2025 | $2,006.2 million to $2,652.1 million | MarketsandMarkets Vector Database Market Report 2025 |
| Global market, 2026 (highest estimate) | $3.73 billion | The Business Research Company Vector Database Global Market Report 2026 |
| Global market forecast, 2030 | $8,945.7 million (27.5% CAGR, 2025-2030) | MarketsandMarkets Vector Database Market Report 2025 |
| Global vector database market, 2025 (alternate estimate) | $2.58 billion | Fortune Business Insights Vector Database Market 2026 |
| Global market forecast, 2034 | $17.91 billion, up from $3.2 billion in 2026 (24% CAGR) | Fortune Business Insights Vector Database Market 2026 |
| Global market, 2025 to 2030 (third estimate) | $3.02 billion to $8.71 billion (23.6% CAGR, 2026-2030) | The Business Research Company Vector Database Global Market Report 2026 |
| Total DBMS market, 2026 | $161 billion (+18.4%) | Gartner Forecast: Database Management Systems, Worldwide, 2023-2029, 2025 Update |
| Vector database CAGR within the DBMS forecast | 75.3% | Gartner Forecast: Database Management Systems, Worldwide, 2023-2029, 2025 Update |
Context note: The Business Research Company is the most bullish of the three firms on the near term. MarketsandMarkets has the steepest growth curve. Gartner’s full DBMS forecast sits behind a paywall, so the two Gartner rows come from the summary of Gartner’s DBMS forecast.
2. Regional and Industry Breakdown
North America is the largest market, but it is not the fastest-growing one. Asia Pacific is projected to grow at 30.2% a year, which would bring it level with Europe at about $2.4 billion by 2030. By then the market will be spread more evenly across regions, which matters for vendors deciding where to open new cloud regions.
By industry, banking and retail together account for nearly half of spending. Retail and e-commerce is also the fastest-growing vertical, because product search and recommendations were early production uses of embeddings. Regional figures are from the MarketsandMarkets regional breakdown. Industry and technology shares are from Fortune Business Insights.
| Metric | Value | Source |
|---|---|---|
| North America revenue share, 2025 | 36.6% (MarketsandMarkets) vs 41% (Fortune Business Insights) | MarketsandMarkets 2025; Fortune Business Insights 2026 |
| US market, 2025 to 2030 | $874.0 million to $2,662.2 million (25.0% CAGR) | MarketsandMarkets Vector Database Market Report 2025 |
| Asia Pacific market, 2025 to 2030 | $658.2 million to $2,460.1 million (30.2% CAGR, fastest) | MarketsandMarkets Vector Database Market Report 2025 |
| Europe market, 2025 to 2030 | $676.8 million to $2,397.4 million (28.8% CAGR) | MarketsandMarkets Vector Database Market Report 2025 |
| Share by component | Solutions 68%, services 32% | Fortune Business Insights Vector Database Market 2026 |
| Share of spending by vertical | BFSI 23%, retail and e-commerce 22%, IT and ITeS 18%, healthcare 14% | Fortune Business Insights Vector Database Market 2026 |
| Retail and e-commerce growth rate | 33.8% CAGR (fastest vertical) | MarketsandMarkets Vector Database Market Report 2025 |
| Share by technology | Natural language processing 46%, computer vision 31%, recommendation systems 23% | Fortune Business Insights Vector Database Market 2026 |
Outlier note: The same report sizes the UK ($160.4 million to $559.8 million, a 28.4% CAGR), the Middle East and Africa ($228.0 million to $787.2 million, a 28.1% CAGR) and Latin America ($117.2 million to $313.1 million, a 21.7% CAGR, the slowest region). In its forecast, MEA grows faster than North America.
3. Enterprise Adoption and RAG Usage
Within one survey cycle, vector databases went from something most AI teams had not tried to something most of them use. In Retool’s survey of about 730 practitioners, use rose from 20% to 63.6% in one year. Databricks’ telemetry shows the same pattern in production: category usage grew 377% year over year and 186% in the roughly three months after its own vector search entered public preview (Databricks State of Data + AI).
RAG is the main reason for this growth. In the Menlo Ventures 2024 enterprise report, RAG appeared in 51% of implementations while fine-tuning appeared in only 9%. Most enterprises are choosing to retrieve their own data at query time rather than retrain models on it, and retrieval runs on a vector index. Our enterprise AI adoption statistics and RAG statistics cover the wider deployment picture.
| Metric | Value | Source |
|---|---|---|
| Practitioners using a vector database | 63.6% in 2024 (up from 20% in 2023) | Retool State of AI H1 2024 |
| Companies with 5,000+ employees using vector DBs or RAG to customize models | About 33% (23.2% across all respondents) | Retool State of AI H1 2024 |
| Enterprise GenAI implementations using RAG | 51% in 2024 (up from 31% in 2023) | Menlo Ventures, 2024 State of Generative AI in the Enterprise |
| Production models using fine-tuning | 9% | Menlo Ventures, 2024 State of Generative AI in the Enterprise |
| Year-over-year growth in vector database usage | 377% | Databricks State of Data + AI 2024 |
| Category growth after Databricks vector search preview | 186% in just over three months | Databricks State of Data + AI 2024 |
| Predicted enterprise vector database usage by 2026 | 30% | Gartner prediction, as cited in Zilliz Milvus release, December 2025 |
| Enterprise generative AI spend, 2025 | $37 billion (3.2x 2024) | Menlo Ventures, 2025 State of Generative AI in the Enterprise |
Recency flag: The Retool, Menlo 2024 and Databricks figures are the most recent available survey and telemetry data on vector adoption specifically: Retool H1 2024, Menlo Ventures 2024, Databricks 2024. Menlo’s 2025 report (495 respondents, November 2025) ranks RAG second after prompt design but does not publish a new RAG percentage.
4. Popularity Rankings and Developer Usage
The most popular vector database is a search engine that added vectors later. Elasticsearch scores 95.50 on the DB-Engines vector DBMS ranking, more than 10 times the score of Pinecone (9.07), the top purpose-built vector database. Couchbase (9.43) also ranks above Pinecone, at third. Many enterprises turn on vector search in a system they already run before they buy a dedicated product. The DB-Engines vector DBMS ranking tracks 26 systems.
Developer surveys show the same thing. In the Stack Overflow Developer Survey 2025, Redis is the top storage layer among developers who build AI agents (42.9%), and pgvector (17.9%) beats Pinecone (11.2%). Among enterprises, Menlo Ventures found the purpose-built and general-purpose databases nearly tied. For the agent side of these numbers, see our AI agents statistics.
| Metric | Value | Source |
|---|---|---|
| Vector DBMS tracked | 26 systems | DB-Engines Vector DBMS Ranking, September 2026 |
| Elasticsearch popularity score | 95.50 (#1) | DB-Engines Vector DBMS Ranking, September 2026 |
| OpenSearch popularity score | 22.11 (#2) | DB-Engines Vector DBMS Ranking, September 2026 |
| Pinecone popularity score | 9.07 (#4, top purpose-built) | DB-Engines Vector DBMS Ranking, September 2026 |
| Milvus / Qdrant / Weaviate scores | 6.85 / 6.82 / 4.74 | DB-Engines Vector DBMS Ranking, September 2026 |
| AI-agent developers using Redis for data storage | 42.9% | Stack Overflow Developer Survey 2025 |
| AI-agent developers using ChromaDB / pgvector / Pinecone | 19.7% / 17.9% / 11.2% | Stack Overflow Developer Survey 2025 |
| Enterprise vector database share: Pinecone / Postgres / MongoDB | 18% / 15% / 14% | Menlo Ventures, 2024 State of Generative AI in the Enterprise |
Methodology note: DB-Engines measures popularity (search interest, job postings, Q&A activity and social mentions), not installations or revenue. The Stack Overflow question was asked only of the 3,398 respondents who use AI agents. In the same pool, Qdrant scored 8.2%, Milvus 5.2%, Weaviate 4.5% and LanceDB 4.4%.
5. Performance and Scale Benchmarks
Billion-vector search now routinely returns results in under 100 ms. The more relevant question for buyers is the cost of reaching that speed. Pinecone reports a production e-commerce workload of about 1.4 billion vectors that serves 5,700 queries per second with a 26 ms median and a 60 ms P99 latency (Pinecone Dedicated Read Nodes announcement). Larger indexes can still be slower: a 480-million-vector workload in the same release had a 170 ms P99.
Cost is where challengers attack the specialist vendors. Timescale reported that self-hosted Postgres with pgvectorscale beat Pinecone’s storage-optimized tier on 50 million embeddings at about a quarter of the monthly cost (Timescale benchmark release). Every number below is vendor-run, so treat it as a claim made against a competitor.
| Metric | Value | Source |
|---|---|---|
| 135M-vector media search workload | 600 QPS, P50 45 ms, P99 96 ms (load test: 2,200 QPS, P99 99 ms) | Pinecone, Dedicated Read Nodes, December 2025 |
| 480M-vector workload | 380 QPS, P50 80 ms, P99 170 ms | Pinecone, Dedicated Read Nodes, December 2025 |
| 1.4B-vector e-commerce recommendations | 5,700 QPS, P50 26 ms, P99 60 ms | Pinecone, Dedicated Read Nodes, December 2025 |
| pgvectorscale vs Pinecone s1, 50M Cohere embeddings (768 dims) | 28x lower p95 latency, 16x higher throughput at 99% recall | Timescale benchmark, June 2024 |
| Monthly cost in the same test | About $835 self-hosted vs $3,241 (Pinecone s1) and $3,889 (Pinecone p2) | Timescale benchmark, June 2024 |
| Amazon S3 Vectors query latency | About 100 ms (frequent), under 1 second (infrequent) | AWS, S3 Vectors GA, December 2025 |
| Amazon S3 Vectors capacity | 2 billion vectors per index, 20 trillion per bucket (40x preview) | AWS, S3 Vectors GA, December 2025 |
| RaBitQ 1-bit quantization memory reduction | 72% | Zilliz, Milvus release, December 2025 |
Context note: The open-source VectorDBBench project now supports more than 25 database clients and datasets of up to 100 million vectors. Zilliz maintains the project, and Zilliz sells Milvus.
6. Funding, Vendors, and Hyperscaler Entry
Funding has slowed. The specialists raised money in 2023 and are now growing on that capital, while the hyperscalers are building vector storage into their existing services. AWS customers created more than 250,000 vector indexes and loaded 40 billion vectors into Amazon S3 Vectors in just over four months (AWS S3 Vectors GA post). That is the kind of adoption specialist vendors need years to reach.
Pinecone, Qdrant and Weaviate have disclosed about $293.5 million in combined funding (138 + 87.8 + 67.7). That is about 11% of the most conservative 2025 market estimate (293.5 / 2,580 = 0.114), which suggests these companies run on lean capital. In September 2025 Pinecone moved its founder to chief scientist and appointed a new CEO, citing more than 5,000 customers (Pinecone leadership announcement). Qdrant’s Series B announcement is the largest new round in the category this year.
| Metric | Value | Source |
|---|---|---|
| Pinecone Series B | $100 million at a $750 million valuation (April 2023) | Pinecone press release, 2023 |
| Pinecone total funding and customers | $138 million raised; 5,000+ customers | Pinecone press release, September 2025 |
| Qdrant Series B | $50 million (March 2026); $87.8 million raised in total | Qdrant Series B announcement, 2026 |
| Qdrant open-source traction | 250 million downloads; 29,000 GitHub stars | Qdrant Series B announcement, 2026 |
| Weaviate Series B | $50 million (April 2023); $67.7 million raised in total | Tech.eu, reporting Weaviate’s announcement, 2023 |
| LanceDB Series A | $30 million (June 2025) | LanceDB Series A announcement, 2025 |
| Milvus adoption | 40,000+ GitHub stars; 10,000+ enterprise teams in production | Zilliz Milvus press release, December 2025 |
| Amazon S3 Vectors early usage | 250,000+ indexes, 40 billion vectors, 1 billion+ queries; up to 90% lower cost vs specialized vector DBs | AWS, S3 Vectors GA, December 2025 |
Recency flag: Pinecone’s and Weaviate’s most recent disclosed priced rounds date from April 2023. Sources: the Pinecone Series B release and the Tech.eu report on Weaviate. The LanceDB round is from the LanceDB Series A post, and the Milvus figures are from the Zilliz Milvus release.
7. Challenges and Barriers to Adoption
The database itself is rarely what holds enterprises back. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that are not supported by AI-ready data (Gartner, Lack of AI-Ready Data Puts AI Projects at Risk). In that release Gartner recommends adding vector stores, chunking, embedding and RAG integration to existing data management practices rather than building a separate stack.
Governance makes the biggest measurable difference. Databricks found that companies using AI governance tools put 12 times as many AI projects into production. Teams choosing a vector database care most about performance benchmarks, but almost as many rely on community reviews or their own proofs of concept.
| Metric | Value | Source |
|---|---|---|
| Organizations lacking, or unsure they have, AI-ready data management practices | 63% | Gartner survey, Q3 2024 (published February 2025) |
| AI projects without AI-ready data expected to be abandoned through 2026 | 60% | Gartner, February 2025 |
| Organizations that estimate their data is not AI-ready | 57% | Gartner Hype Cycle for Artificial Intelligence 2025 |
| GenAI projects predicted to be abandoned after proof of concept by end of 2025 | 30% | Gartner, July 2024 |
| Top vector DB selection methods | Performance benchmarks 40%, user reviews 39.3%, proofs of concept 38% | Retool State of AI H1 2024 |
| AI projects reaching production with governance tools | 12x more | Databricks State of AI Agents 2026 |
| AI systems reaching production with evaluation tools | 6x more | Databricks State of AI Agents 2026 |
| Respondents who believe their AI governance program is insufficient | 40% | Economist Impact survey 2024, cited in Databricks State of AI Agents 2026 |
Context note: In the 2025 Hype Cycle, Gartner places AI-ready data and AI agents at the Peak of Inflated Expectations (Gartner Hype Cycle for AI 2025). The Databricks State of AI Agents report analyzes more than 20,000 organizations.
Summary: Vector Database Enterprise Adoption by the Numbers
| Metric | Value | Source |
|---|---|---|
| Global market, 2025 | $2.58 billion to $3.02 billion (three estimates) | Fortune Business Insights 2026; MarketsandMarkets 2025; The Business Research Company 2026 |
| Global market forecast, 2030 | $8.95 billion (27.5% CAGR) | MarketsandMarkets 2025 |
| Global market forecast, 2034 | $17.91 billion (24% CAGR) | Fortune Business Insights 2026 |
| Vector DB CAGR within the DBMS market | 75.3% | Gartner DBMS Forecast, 2025 Update |
| Total DBMS market, 2026 | $161 billion (+18.4%) | Gartner DBMS Forecast, 2025 Update |
| Fastest-growing region | Asia Pacific, 30.2% CAGR | MarketsandMarkets 2025 |
| North America revenue share, 2025 | 36.6% to 41% | MarketsandMarkets 2025; Fortune Business Insights 2026 |
| Largest vertical | BFSI, 23% | Fortune Business Insights 2026 |
| Practitioners using vector DBs | 63.6% (from 20%) | Retool State of AI H1 2024 |
| Enterprise GenAI implementations using RAG | 51% (from 31%) | Menlo Ventures 2024 |
| Vector database usage growth | 377% year over year | Databricks State of Data + AI 2024 |
| DB-Engines top vector DBMS | Elasticsearch, 95.50 | DB-Engines, September 2026 |
| Top purpose-built vector DB on DB-Engines | Pinecone, 9.07 | DB-Engines, September 2026 |
| AI-agent developers using pgvector | 17.9% | Stack Overflow Developer Survey 2025 |
| Billion-scale production throughput | 5,700 QPS at P50 26 ms (1.4B vectors) | Pinecone 2025 |
| Amazon S3 Vectors early ingest | 40 billion vectors in about four months | AWS 2025 |
| Qdrant total funding | $87.8 million | Qdrant 2026 |
| Milvus production adoption | 10,000+ enterprise teams | Zilliz 2025 |
| Organizations without AI-ready data practices | 63% | Gartner 2025 |
| AI projects expected to be abandoned without AI-ready data | 60% through 2026 | Gartner 2025 |
Methodology and Sources
We collected about 75 raw data points through targeted searches and kept the figures we could trace to a report, filing, survey, platform telemetry or company announcement. We left out figures that appear only on aggregator blogs.
- MarketsandMarkets, Vector Database Market Report 2025-2030 (published December 2025)
- Fortune Business Insights, Vector Database Market 2026-2034 (updated September 2026)
- The Business Research Company, Vector Database Global Market Report 2026
- Gartner, Forecast: Database Management Systems, Worldwide, 2023-2029, 2025 Update
- Gartner, Lack of AI-Ready Data Puts AI Projects at Risk (February 2025)
- Gartner, Hype Cycle Identifies Top AI Innovations in 2025 (August 2025)
- Gartner, 30% of Generative AI Projects Will Be Abandoned After Proof of Concept (July 2024)
- Menlo Ventures, 2024 State of Generative AI in the Enterprise (600 US IT decision-makers)
- Menlo Ventures, 2025 State of Generative AI in the Enterprise (495 respondents)
- Retool, State of AI H1 2024 (about 730 respondents)
- Databricks, State of Data + AI 2024 (10,000 customers)
- Databricks, State of AI Agents 2026 (20,000+ organizations)
- DB-Engines, Vector DBMS Ranking (September 2026)
- Stack Overflow, Developer Survey 2025: AI
- Pinecone, Dedicated Read Nodes (December 2025)
- Pinecone, Series B press release (April 2023) and leadership announcement (September 2025)
- Timescale, pgvectorscale benchmark release (June 2024)
- AWS, Amazon S3 Vectors general availability (December 2025)
- Qdrant, Series B announcement (March 2026)
- Tech.eu, Weaviate Series B (April 2023)
- LanceDB, Series A announcement (June 2025)
- Zilliz, Milvus 40,000 GitHub stars release (December 2025)
- Zilliz, VectorDBBench repository
- Data watch: The three market-size estimates for 2025 conflict ($2.58 billion Fortune Business Insights, $2.65 billion MarketsandMarkets, $3.02 billion The Business Research Company) because each firm defines the category differently, and their North America shares also differ (36.6% vs 41%). Menlo Ventures revised its own 2024 enterprise GenAI spend from $13.8 billion in the 2024 report to $11.5 billion in the 2025 report. The Gartner 30% adoption prediction is cited here as Zilliz reported it. The original Gartner research is paywalled, and secondary sources word it differently (30% of enterprises vs 30% of enterprise applications). The 75.3% CAGR comes from Gartner’s public summary of the DBMS forecast, not the full report. Every benchmark here was run or published by a vendor with a commercial interest, and none has been independently replicated. DB-Engines scores measure popularity, not deployments. The adoption surveys (Retool H1 2024, Menlo Ventures 2024, Databricks 2024) are the most recent available vector-specific data. Newer figures are expected in Menlo Ventures’ next enterprise report, and the DB-Engines ranking updates monthly.
Last updated: October 3, 2026. We update this roundup quarterly, and the next refresh is expected when Menlo Ventures publishes its 2026 State of Generative AI in the Enterprise report.