Best Vector Databases: Tested & Compared in 2026
We benchmarked 8 vector databases on query latency, write throughput, cost at scale, and production readiness. Tests run on 100M 768-dim vectors with identical hardware. Full methodology on GitHub.
How We Evaluated
Query Latency
p50 and p99 latency for nearest-neighbor search on 100M 768-dim vectors with top_k=10.
Cost at Scale
Monthly cost to serve 100M vectors with 1K queries/day, including storage, compute, and network egress.
Write Throughput
Sustained vector upsert rate (vectors/sec) measured during bulk ingestion of 10M vectors.
Search Capabilities
Support for hybrid search (dense + sparse + BM25), metadata filtering, multi-vector, and advanced query patterns.
Production Readiness
Storage durability, replication, tiering, observability, multi-tenancy, and operational overhead.
Mixpeek Vector Store (MVS)
Object-storage-native vector database that runs on your own S3-compatible storage. Dense, sparse, and BM25 hybrid search with automatic hot/warm/cold tiering — no separate database cluster to manage. Bring your own Backblaze B2, Cloudflare R2, Tigris, Wasabi, or AWS S3.
Pros
- +~8ms p50 hot search, 92ms warm — competitive latency at a fraction of the cost
- +BYO object storage: runs on any S3-compatible backend you already pay for
- +Automatic tiering moves cold data to object storage (up to 90% cost reduction)
- +Dense + sparse + BM25 hybrid search, aggregations, transactions, and standing queries
- +52K vectors/sec write throughput — fastest in our benchmark
Cons
- -Currently in private beta — invite required
- -Warm-tier latency (~92ms) higher than always-hot databases for cold-start queries
- -Newer product with a smaller community than Qdrant or Milvus
Qdrant
High-performance vector search engine written in Rust. Strong payload filtering, named vectors, and a mature managed cloud offering. The go-to choice for teams that want an open-source vector DB with a proven production track record.
Pros
- +12ms p50 latency at 100M vectors — consistently fast
- +Advanced payload filtering alongside vector search
- +Named vectors for multi-modal embeddings per point
- +Open-source with active community and managed cloud
Cons
- -All data stays hot — no automatic tiering to cheaper storage
- -Managed cloud costs scale linearly with vector count ($5K/mo at 100M)
- -Requires a separate embedding pipeline
- -Cluster management overhead for very large deployments
Pinecone
Fully managed serverless vector database with zero operational overhead. Simple API, generous free tier, and good metadata filtering. The easiest vector database to get started with, but costs become unpredictable at scale.
Pros
- +Zero ops — fully managed with serverless scaling
- +Simple API and excellent onboarding experience
- +Good metadata filtering and namespace isolation
- +Serverless option scales to zero when idle
Cons
- -35ms p50 latency — slower than Qdrant and MVS in our benchmarks
- -No self-hosting option — vendor lock-in
- -Pricing unpredictable at scale ($7K/mo at 100M vectors in our test)
- -15K vectors/sec write throughput — slowest in our benchmark
Weaviate
Open-source vector database with built-in vectorizer modules. Can generate embeddings during ingestion using CLIP, Cohere, or OpenAI models. Hybrid search with BM25 built in. Good balance of features and operational simplicity.
Pros
- +Built-in vectorizer modules reduce pipeline complexity
- +Hybrid search combining BM25 and vector search natively
- +18ms p50 latency — solid mid-range performance
- +Active open-source community with good documentation
Cons
- -Built-in vectorizers add resource overhead and complexity
- -Higher memory footprint than Qdrant
- -No automatic storage tiering — all data stays hot ($3.5K/mo at 100M)
- -Multi-tenancy support still maturing
Milvus / Zilliz
Scalable open-source vector database designed for billion-scale deployments. Distributed architecture with GPU-accelerated indexing. Zilliz Cloud provides a managed offering. The most battle-tested option for truly massive collections.
Pros
- +Proven at billion-vector scale with GPU-accelerated indexing
- +Multiple index types (IVF, HNSW, DiskANN, ScaNN)
- +Strong partition and sharding support for distributed deployments
- +Managed offering (Zilliz Cloud) reduces operational burden
Cons
- -Complex deployment — many moving parts (etcd, MinIO, Pulsar)
- -Higher operational overhead than managed alternatives
- -Metadata filtering less flexible than Qdrant
- -Documentation inconsistent across versions
Turbopuffer
Object-storage-native vector database with a similar philosophy to MVS — data lives in S3 with a caching layer for hot queries. Competitive latency for warm data and very cost-effective at scale. Early-stage but promising.
Pros
- +Object-storage-native like MVS — very cost-effective at scale
- +Good warm-data latency with intelligent caching
- +Simple API with low operational overhead
- +Transparent pricing model
Cons
- -No hybrid search (dense only — no sparse or BM25)
- -No aggregations, transactions, or standing queries
- -Smaller feature set than MVS, Qdrant, or Weaviate
- -Early-stage with limited production case studies
pgvector (PostgreSQL)
PostgreSQL extension that adds vector similarity search to your existing Postgres database. Zero additional infrastructure if you already run Postgres. Good for small to mid-scale workloads where you want vectors alongside relational data.
Pros
- +Zero additional infrastructure — just add the extension
- +Full SQL support alongside vector search
- +ACID transactions for vector and relational data together
- +Massive ecosystem of Postgres tooling and hosting options
Cons
- -Performance degrades significantly above 10M vectors
- -No purpose-built ANN index — HNSW support is newer and less tuned
- -Lacks advanced features like multi-vector, hybrid search, or tiering
- -Not designed for high-throughput vector workloads
Chroma
Lightweight, developer-friendly vector database designed for RAG applications and rapid prototyping. Embedded-first architecture that runs in-process with your Python app. Not built for production scale, but unbeatable for getting started quickly.
Pros
- +Fastest time-to-hello-world of any vector DB
- +Runs in-process — no separate server needed
- +Great Python and JavaScript SDKs
- +Built-in embedding functions for quick prototyping
Cons
- -Not designed for production scale (struggles above 1M vectors)
- -No distributed architecture or replication
- -Limited query capabilities compared to Qdrant or MVS
- -No storage tiering or cost optimization features
Frequently Asked Questions
What is the best vector database for production in 2026?
It depends on your scale and budget. For most production workloads, MVS offers the best cost-to-performance ratio because it uses your existing object storage (S3, B2, R2) instead of requiring a separate always-hot database. For teams that need consistently sub-10ms latency and can afford always-hot storage, Qdrant is the proven choice. For zero-ops simplicity, Pinecone is easiest to get started with but becomes expensive at scale.
Which vector database is cheapest at 100M+ vectors?
MVS is significantly cheaper at scale because it stores vectors on your own object storage (S3, Backblaze B2, Cloudflare R2, etc.) and only keeps frequently queried data hot. In our benchmark, MVS cost $800/month for 100M vectors (80% warm tier) compared to $5,000 for Qdrant Cloud, $3,500 for Weaviate, and $7,000 for Pinecone. Turbopuffer follows a similar object-storage-native model and is also cost-effective, but lacks hybrid search.
What is the difference between a vector database and a vector store?
In practice, they are often used interchangeably. A 'vector store' sometimes refers to a simpler system that just stores and retrieves vectors (like pgvector or Chroma), while a 'vector database' implies full database capabilities: ACID transactions, replication, filtering, aggregations, and production-grade durability. MVS blurs this line further by being a vector database that stores data in object storage — giving you database features with store-level economics.
Can I use my own object storage with a vector database?
Most vector databases (Qdrant, Pinecone, Weaviate, Milvus) manage their own storage — you cannot bring your own S3 bucket. MVS and Turbopuffer are exceptions: both are built on object storage from the ground up. MVS supports any S3-compatible backend (AWS S3, Backblaze B2, Cloudflare R2, Tigris, Wasabi), so your data stays in storage you already control and pay for. This also means no vendor lock-in on the storage layer.
Which vector database is best for RAG (retrieval-augmented generation)?
For RAG, you want hybrid search (combining dense vectors with keyword matching) and good metadata filtering. Weaviate and MVS both offer native BM25 + vector hybrid search. MVS adds multi-stage retrieval pipelines that let you chain filter → sort → reduce → enrich stages — useful for complex RAG that needs more than a single similarity query. For simple RAG prototypes, Chroma is the fastest to set up.
How do vector database benchmarks work?
Our benchmarks use 100M 768-dimensional vectors (float32) on equivalent hardware. We measure p50/p90/p99 query latency at top_k=10, sustained write throughput (vectors/sec during bulk upsert), and monthly cost at a standardized query load (1K queries/day). Full methodology, raw data, and reproduction scripts are available at github.com/mixpeek/mvs-benchmark.
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