The category-defining managed serverless vector DB with the most polished docs and pay-per-operation billing.
Category report · 7 providers evaluated
Best Vector Database APIs
Vector database APIs power semantic search, RAG, and AI memory by storing and querying high-dimensional embeddings. The category splits into purpose-built managed services (Pinecone, Weaviate, Qdrant, Zilliz/Milvus, Chroma, Turbopuffer) and vector capabilities bolted onto general databases (pgvector via Supabase, MongoDB Atlas). Compare on documentation/DX quality, reliability (published SLAs and proven scale), SDK/ecosystem breadth, and how fast a developer or AI agent can self-serve a working API key. Pricing models diverge sharply: usage-based per read/write/storage units (Pinecone, Turbopuffer, Chroma), resource-based vCPU/RAM (Qdrant), compute-unit (Zilliz), or bundled into a broader DB plan (Supabase, MongoDB).
- Best overall87.5
- Best docs & dx90
- Best with free tier
- Best reliability90
- Best ecosystem88
- Best access92
What is the best Vector Database API?
| Provider | Free | ||||||
|---|---|---|---|---|---|---|---|
| 1 | 90 | 88 | 82 | 90 | 87.5A | Yes | |
| 2 | 85 | 90 | 88 | 80 | 86.0A | Yes | |
| 3 | 82 | 88 | 85 | 88 | 85.4A | Yes | |
| 4 | 86 | 80 | 84 | 92 | 85.2A | Yes | |
| 5 | 82 | 78 | 80 | 90 | 82.1B | Yes | |
| 6 | 84 | 80 | 80 | 72 | 79.6B | Yes | |
| 7 | 74 | 76 | 58 | 60 | 67.7C | No |
Table 1. Best Vector Database APIs ranked by the APIbenchmarks Index. Specification columns are vendor-stated; ABI is computed per the published methodology.
Composite scores
Figure 1. APIbenchmarks Index for Vector Database APIs, bar length proportional to composite score; colour encodes letter grade.
Explore the scores
Documentation & DX
Weight 30% · higher is better
Reliability
Weight 25% · higher is better
Ecosystem & SDKs
Weight 25% · higher is better
Accessibility
Weight 20% · higher is better
Build quality vs accessibility
Up and to the right is better: well built and easy to start with.
Provider scorecards
Vector search native to Atlas, letting teams add embeddings to an existing operational DB with no new service.
Fully managed Milvus with a generous permanent free tier, 99.95% SLA, and massive open-source lineage.
pgvector bundled free into the Supabase Postgres stack, ideal for teams keeping embeddings beside relational data.
Rust-built engine with predictable resource-based pricing, a free always-on cluster, and strong filtering.
Feature-rich vector DB with hybrid search and agentic query tooling, but only a 14-day free sandbox.
Object-storage-first serverless search powering Cursor and Notion, extremely cheap at scale but no free tier.
How this category is scored
Each provider is evaluated against the same four criteria and scored from 0 to 100. Every figure links to a primary source. Latency and observed-uptime probes are reserved for a future phase. Corrections: corrections@apibenchmarks.com Every provider in Vector Database APIs is scored on the same four criteria: documentation & dx (30%), reliability (25%), ecosystem & sdks (25%), accessibility (20%). Price is reported separately because price units are not comparable. Full methodology →

Frequently asked questions
- What is the best Vector Database API?
- By the APIbenchmarks Index, Pinecone rates highest (ABI 87.5, grade A). Serverless vector search for production RAG The ABI weights documentation, reliability, ecosystem, and accessibility; price is reported separately, so the right pick still depends on your budget and workload.
- Which vector database APIs have a free tier?
- Pinecone, MongoDB Atlas Vector Search, Zilliz Cloud (Milvus), Supabase Vector (pgvector), Qdrant Cloud, Weaviate Cloud offer a free tier or trial credits.
- How is the APIbenchmarks Index calculated?
- The ABI is a weighted composite of four dimensions scored on absolute reference scales: documentation & DX (30%), reliability (25%), ecosystem & SDKs (25%), and accessibility (20%). Price is excluded from the composite because price units are not comparable across categories. The full formula is on the methodology page.
Popular comparisons
References
61 primary sources · show all
- https://www.pinecone.io/pricing/
- https://docs.pinecone.io/reference/pinecone-sdks
- https://docs.pinecone.io/guides/get-started/key-features
- https://www.pinecone.io/blog/serverless-architecture/
- https://status.pinecone.io/history
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- https://community.pinecone.io/t/random-performances-on-the-serverless-formula/4136
- https://blocksandfiles.com/2025/12/01/pinecone-dedicated-read-nodes/
- https://docs.pinecone.io/guides/manage-cost/understanding-cost
- https://www.mongodb.com/products/platform/atlas-vector-search
- https://www.mongodb.com/docs/atlas/atlas-vector-search/benchmark/results/
- https://www.mongodb.com/company/blog/innovation/new-benchmark-tests-reveal-key-vector-search-performance-factors
- https://www.mongodb.com/pricing
- https://www.mongodb.com/cloud/atlas/reliability
- https://www.g2.com/products/mongodb-atlas/reviews
- https://www.peerspot.com/products/mongodb-atlas-reviews
- https://news.ycombinator.com/item?id=45919356
- https://www.mongodb.com/resources/languages
- https://zilliz.com/pricing
- https://zilliz.com/sla
- https://zilliz.com/blog/zilliz-cloud-oct-2025-update
- https://leanopstech.com/blog/milvus-zilliz-cloud-pricing-2026/
- https://www.g2.com/products/zilliz/reviews
- https://www.g2.com/products/milvus/reviews
- https://milvus.io/blog/milvus-26-preview-72-memory-reduction-without-compromising-recall-and-4x-faster-than-elasticsearch.md
- https://github.com/zilliztech/VectorDBBench
- https://www.prnewswire.com/news-releases/zilliz-named-highest-performer-and-easiest-to-use-in-g2s-summer-2025-grid-report-for-vector-databases-302503076.html
- https://supabase.com/docs/guides/database/extensions/pgvector
- https://supabase.com/blog/increase-performance-pgvector-hnsw
- https://supabase.com/blog/pgvector-0-7-0
- https://supabase.com/pricing
- https://supabase.com/sla
- https://supabase.com/docs/guides/ai/vecs-python-client
- https://www.g2.com/products/supabase-supabase/reviews
- https://news.ycombinator.com/item?id=35554045
- https://supabase.com/docs/guides/ai/automatic-embeddings
- https://qdrant.tech/pricing/
- https://qdrant.tech/benchmarks/
- https://qdrant.tech/benchmarks/single-node-speed-benchmark/
- https://status.qdrant.io/
- https://cloud.qdrant.io/sla
- https://qdrant.tech/documentation/interfaces/
- https://nirantk.com/writing/pgvector-vs-qdrant/
- https://www.peerspot.com/products/qdrant-reviews
- https://www.producthunt.com/products/qdrant/reviews
- https://weaviate.io/pricing
- https://weaviate.io/blog/weaviate-cloud-pricing-update
- https://docs.weaviate.io/weaviate/benchmarks/ann
- https://docs.weaviate.io/weaviate/client-libraries
- https://www.g2.com/products/weaviate/reviews
- https://www.eesel.ai/blog/weaviate-pricing
- https://aws.amazon.com/marketplace/pp/prodview-27nbweprm7hha
- https://turbopuffer.com/pricing
- https://turbopuffer.com/blog/turbopuffer
- https://turbopuffer.com/docs
- https://turbopuffer.com/docs/pricing-log
- https://github.com/turbopuffer/turbopuffer-typescript
- https://github.com/turbopuffer/turbopuffer-go
- https://news.ycombinator.com/item?id=40916786
- https://zilliz.com/blog/the-cost-of-consequence-what-no-one-tells-you-about-serverless-vector-databases
- https://www.modern-datatools.com/tools/turbopuffer
