Qdrant vs Chroma
Rust-based vector DB — high performance, OSS, managed cloud
vs. Open-source vector DB designed for AI apps — embeddings-first, dev-friendly
Pricing tiers
Qdrant
Free Forever
Single-node 0.5 vCPU / 1 GB RAM / 4 GB disk. Free cloud inference models.
Free
Standard
Usage-based. Dedicated resources, flexible scaling. 99.5% SLA. Backups + DR. Free inference tokens.
$0 base (usage-based)
Self-Host (OSS)
Apache 2.0 licensed. Run for free.
$0 base (usage-based)
Hybrid Cloud (BYOC)
Run managed cluster on your infra. Data stays in your network.
Custom
Premium
Min spend required. SSO + private VPC links. 99.9% SLA. 24x7 enterprise support.
Custom
Private Cloud
Dedicated + isolated. Custom SLA. Large enterprise.
Custom
Chroma
Cloud — Free
$5 free credits. Great for trying it out.
Free
Cloud — Pro
$100 credits included, then usage-based. Dedicated resources, SOC2, priority support.
$0 base (usage-based)
Self-Host (OSS)
MIT-licensed. Embedded or Docker. Free forever.
$0 base (usage-based)
Cloud — Enterprise
Custom. VPC, compliance, dedicated support.
Custom
Free-tier quotas head-to-head
Comparing free on Qdrant vs cloud-free on Chroma.
| Metric | Qdrant | Chroma |
|---|---|---|
| No overlapping quota metrics for these tiers. | ||
Features
Qdrant · 13 features
- BYOC (Hybrid Cloud) — Managed Qdrant in your cloud account.
- Cloud Inference — Hosted embedding models for free tokens.
- Cluster Monitoring — Prometheus metrics + health.
- Collections — Typed collections with named vectors + payload schema.
- Distributed — Horizontal sharding + Raft replication.
- Hybrid Search — Sparse + dense + keyword in one query.
- Multi-Vector — Multiple vectors per point (text + image, etc.).
- Open Source — Apache 2.0 licensed.
- Payload Filters — Rich filter DSL with indexed fields.
- Quantization — Scalar + product + binary for memory reduction.
- RBAC — API-key scopes + roles.
- Snapshots + Restore — Backup + DR primitives.
- Sparse Vectors — BM25 + SPLADE sparse embeddings natively.
Chroma · 11 features
- Client-Server Mode — Run Chroma via Docker; clients connect over HTTP.
- Collections — Named groups of embeddings + metadata.
- Distributed (Cloud) — Horizontal scaling on Chroma Cloud.
- Embedded Mode — In-process Python — chromadb.Client() and go. Zero setup.
- Embedding Functions — Plug-in embedders (OpenAI, Cohere, SentenceTransformers, HF).
- Full-Text Search — BM25 + vector hybrid.
- Metadata Filters — Where-clause query language.
- Migration Tools — Import from Pinecone + other stores.
- Multi-Modal — Text + image embeddings (CLIP, etc.).
- Python + JS APIs — Same API shape across both SDKs.
- Serverless Cloud — Pay for storage + queries, auto-scale.
Developer interfaces
| Kind | Qdrant | Chroma |
|---|---|---|
| SDK | go-client, java-client, qdrant-client (py), qdrant-client (rust), qdrant-dotnet, @qdrant/js-client-rest | chromadb (JS/TS), chromadb (Python) |
| REST | Qdrant REST API | Chroma HTTP API |
| MCP | Qdrant MCP | Chroma MCP |
| OTHER | Qdrant gRPC | Docker Server, Embedded Mode (in-process Python) |
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