Pinecone vs Chroma
Managed vector database for AI — RAG, semantic search, recommendations
vs. Open-source vector DB designed for AI apps — embeddings-first, dev-friendly
Pricing tiers
Pinecone
Starter (Free)
2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes. us-east-1 AWS only.
Free
Standard
$50/month minimum. Unlimited storage ($0.33/GB/mo) + writes ($4-4.50/M) + reads ($16-18/M). 20 indexes/project. Multi-region, multi-cloud.
$50/mo
HIPAA Add-on
$190/month add-on for HIPAA-eligible workloads.
$190/mo
Enterprise
$500/month minimum. Higher per-unit rates for dedicated infra + SLA. 200 indexes.
$500/mo
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 starter on Pinecone vs cloud-free on Chroma.
| Metric | Pinecone | Chroma |
|---|---|---|
| No overlapping quota metrics for these tiers. | ||
Features
Pinecone · 13 features
- Backups + PITR — Automated + manual backups.
- HIPAA Eligible — BAA available via add-on.
- Metadata Filtering — Filter vectors on metadata at query time.
- Monitoring — Metrics endpoint, export to Datadog/Prometheus.
- Namespaces — Multi-tenancy inside an index. Isolate vectors per customer.
- Pinecone Assistant — RAG-as-a-service: upload docs → get a ready chat endpoint.
- Pinecone Inference — Hosted embedding models (multilingual-e5, llama-text-embed-v2, etc.) inside data…
- Pod-Based Indexes — Dedicated pods (p1, s1, p2) for consistent low-latency workloads.
- Private Networking — AWS PrivateLink / VPC peering on Enterprise.
- RBAC — Per-project + per-API-key roles.
- Rerank (Cohere-backed) — Optional reranker on top of vector search.
- Serverless Indexes — Pay per use. No provisioning. Auto-scales.
- Sparse-Dense Vectors — Hybrid search: sparse (keyword) + dense (semantic) together.
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 | Pinecone | Chroma |
|---|---|---|
| CLI | Pinecone CLI | — |
| SDK | go-pinecone, @pinecone-database/pinecone, pinecone-java-client, Pinecone.NET, pinecone (Python) | chromadb (JS/TS), chromadb (Python) |
| REST | Data Plane (per-index), Pinecone Control Plane | Chroma HTTP API |
| MCP | Pinecone MCP | Chroma MCP |
| OTHER | — | Docker Server, Embedded Mode (in-process Python) |
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