weaviate
Teams that need multimodal search, built-in vectorization modules, and a broader search platform.
You gain platform breadth and module flexibility but accept a larger operational footprint and steeper learning curve.
Vector Databases
Dedicated vector storage and similarity search engines used for AI/ML retrieval, semantic search, and RAG pipelines.
Top pick
Top pick for Solo Dev
chroma
Best for local prototyping with zero setup cost and instant iteration.
Best for local prototyping with zero setup cost and instant iteration.
- DX Score
- 8.2
- Time to Hello World
- 2 min
- Free Tier
- Open Source
- Starts at
- $0
Comparison matrix
The shortlist at a glance
Comparison matrix
| Tool | Self Hosted | Hybrid Search | Multi Tenancy | Managed Option | Max Vectors Free | Filtering Quality | Cost Predictability | Embedding Integration | Pick |
|---|---|---|---|---|---|---|---|---|---|
| qdrant | Unlimited | Excellent | High | Basic | |||||
| pinecone | 100 | Excellent | Medium | Native | |||||
| chroma | Unlimited | Basic | High | Native | Pick | ||||
| weaviate | Unlimited | Excellent | Medium | Native | |||||
| pgvector | Unlimited | Good | High | None |
Also great
Strong alternatives when your constraints shift
pgvector
Teams already on Postgres that want vector search without adding a separate service.
You avoid operational complexity but accept weaker filtering performance and limited scalability past a few million vectors.
Avoid
Where we would push you elsewhere
milvus
Milvus requires etcd, MinIO or S3, and a message queue in production, creating significant operational overhead for most teams.
Better alternatives: qdrant, pinecone, weaviate
Methodology
How this recommendation gets made
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend vector-databases --context solo-dev
# or query from code
recommend({
category: "vector-databases",
context: "solo-dev"
})
Top pick
Top pick for Startup
qdrant
Best quality-to-cost ratio with flexible self-hosted or managed deployment.
Qdrant offers the strongest balance of retrieval quality, filtering performance, deployment flexibility, and cost control. Its Rust-based engine delivers consistently fast queries with excellent filtered search, it runs equally well self-hosted or managed, the API is clean and well-typed, and the free self-hosted path keeps experimentation costs at zero. It avoids the vendor lock-in of fully managed alternatives without sacrificing operational polish.
- DX Score
- 9.0
- Time to Hello World
- 5 min
- Free Tier
- Self-Hosted
- Starts at
- $0
Comparison matrix
The shortlist at a glance
Comparison matrix
| Tool | Self Hosted | Hybrid Search | Multi Tenancy | Managed Option | Max Vectors Free | Filtering Quality | Cost Predictability | Embedding Integration | Pick |
|---|---|---|---|---|---|---|---|---|---|
| qdrant | Unlimited | Excellent | High | Basic | Pick | ||||
| pinecone | 100 | Excellent | Medium | Native | |||||
| chroma | Unlimited | Basic | High | Native | |||||
| weaviate | Unlimited | Excellent | Medium | Native | |||||
| pgvector | Unlimited | Good | High | None |
Also great
Strong alternatives when your constraints shift
weaviate
Teams that need multimodal search, built-in vectorization modules, and a broader search platform.
You gain platform breadth and module flexibility but accept a larger operational footprint and steeper learning curve.
pgvector
Teams already on Postgres that want vector search without adding a separate service.
You avoid operational complexity but accept weaker filtering performance and limited scalability past a few million vectors.
Avoid
Where we would push you elsewhere
milvus
Milvus requires etcd, MinIO or S3, and a message queue in production, creating significant operational overhead for most teams.
Better alternatives: qdrant, pinecone, weaviate
Methodology
How this recommendation gets made
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend vector-databases --context startup
# or query from code
recommend({
category: "vector-databases",
context: "startup"
})
Top pick
Top pick for Scale Up
pinecone
Best managed operations and commercial support at scale.
Best managed operations and commercial support at scale.
- DX Score
- 9.2
- Time to Hello World
- 3 min
- Free Tier
- Starter
- Starts at
- $0
Comparison matrix
The shortlist at a glance
Comparison matrix
| Tool | Self Hosted | Hybrid Search | Multi Tenancy | Managed Option | Max Vectors Free | Filtering Quality | Cost Predictability | Embedding Integration | Pick |
|---|---|---|---|---|---|---|---|---|---|
| qdrant | Unlimited | Excellent | High | Basic | |||||
| pinecone | 100 | Excellent | Medium | Native | Pick | ||||
| chroma | Unlimited | Basic | High | Native | |||||
| weaviate | Unlimited | Excellent | Medium | Native | |||||
| pgvector | Unlimited | Good | High | None |
Also great
Strong alternatives when your constraints shift
weaviate
Teams that need multimodal search, built-in vectorization modules, and a broader search platform.
You gain platform breadth and module flexibility but accept a larger operational footprint and steeper learning curve.
pgvector
Teams already on Postgres that want vector search without adding a separate service.
You avoid operational complexity but accept weaker filtering performance and limited scalability past a few million vectors.
Avoid
Where we would push you elsewhere
milvus
Milvus requires etcd, MinIO or S3, and a message queue in production, creating significant operational overhead for most teams.
Better alternatives: qdrant, pinecone, weaviate
Methodology
How this recommendation gets made
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend vector-databases --context scale-up
# or query from code
recommend({
category: "vector-databases",
context: "scale-up"
})
Top pick
Top pick for Enterprise
pinecone
Best enterprise controls, SLAs, and managed operational surface.
Best enterprise controls, SLAs, and managed operational surface.
- DX Score
- 9.2
- Time to Hello World
- 3 min
- Free Tier
- Starter
- Starts at
- $0
Comparison matrix
The shortlist at a glance
Comparison matrix
| Tool | Self Hosted | Hybrid Search | Multi Tenancy | Managed Option | Max Vectors Free | Filtering Quality | Cost Predictability | Embedding Integration | Pick |
|---|---|---|---|---|---|---|---|---|---|
| qdrant | Unlimited | Excellent | High | Basic | |||||
| pinecone | 100 | Excellent | Medium | Native | Pick | ||||
| chroma | Unlimited | Basic | High | Native | |||||
| weaviate | Unlimited | Excellent | Medium | Native | |||||
| pgvector | Unlimited | Good | High | None |
Also great
Strong alternatives when your constraints shift
weaviate
Teams that need multimodal search, built-in vectorization modules, and a broader search platform.
You gain platform breadth and module flexibility but accept a larger operational footprint and steeper learning curve.
pgvector
Teams already on Postgres that want vector search without adding a separate service.
You avoid operational complexity but accept weaker filtering performance and limited scalability past a few million vectors.
Avoid
Where we would push you elsewhere
milvus
Milvus requires etcd, MinIO or S3, and a message queue in production, creating significant operational overhead for most teams.
Better alternatives: qdrant, pinecone, weaviate
Methodology
How this recommendation gets made
Hands-on onboarding runs with the best-supported SDK path.
Pricing snapshots captured at publish time and reviewed on drift.
Benchmarks recorded in a repeatable environment with notes on tradeoffs.
Use this in your agent
npx @vetted/cli recommend vector-databases --context enterprise
# or query from code
recommend({
category: "vector-databases",
context: "enterprise"
})