Why It Matters
Chroma bridges local development and production-grade AI search. Unlike databases forcing expensive RAM-based clusters, Chroma is built on object storage — up to 10x cheaper at scale. It stores documents alongside embeddings natively, supports vector, full-text, and metadata search in one platform, with the simplest DX: pip install, embed, query.
What Sets Chroma Apart
The key differentiators that make Chroma stand out from other vector databases.
Built on Object Storage — Up to 10x Cheaper
Unlike RAM-based vector databases, Chroma stores everything on object storage (S3/GCS) with intelligent SSD caching. Vectors cost $0.02/GB/month on object storage vs $5/GB/month in RAM — a dramatic cost advantage at scale.
Multi-Modal Search in One Platform
Vector similarity, full-text search (BM25/SPLADE), regex matching, and metadata filtering — all natively supported. No need to bolt on Elasticsearch for keyword search or separate metadata stores.
Simplest Developer Experience
pip install chromadb and you're running locally in seconds. Documents are stored alongside embeddings — no separate document store needed. Built-in embedding functions for OpenAI, Cohere, HuggingFace, and more.
Fully Open Source (Apache 2.0)
Complete source code on GitHub with 24K+ stars and 5M+ monthly downloads. Run locally, self-host on Kubernetes, or use Chroma Cloud. Zero vendor lock-in — your data and code are portable.
Serverless with Pure Usage Pricing
No clusters to provision, no nodes to size. Pay only for GiB written, stored, and queried. Costs scale linearly with actual usage — idle applications cost nearly nothing.
Pricing — No Surprises
Pay only for what you use — per GiB written, stored, queried, and transferred. No cluster sizing, no idle costs. Built on object storage for dramatically lower storage costs.
- $5 in free credits to start
- Up to 10 databases
- Up to 10 team members
- Usage-based pricing only
- Community Slack support
- 5M records per collection
- 1M collections per database
- SOC 2 Type II compliant
- $100 in monthly credits
- Up to 100 databases
- Up to 30 team members
- Volume discounts on usage
- Direct Slack support
- SOC 2 Type II compliant
- Higher rate limits
- Priority feature requests
- Unlimited databases
- Unlimited team members
- BYOC (Bring Your Own Cloud)
- Single-tenant deployment
- Custom SLAs & 24/7 support
- Multi-region replication
- Customer-managed encryption keys
- Private networking (VPC peering)
- Point-in-time recovery
Pricing Terms Explained
Before you use the pricing calculator, understand what each term means. No more guessing what a "Read Unit" is or how "dimensions" affect your bill.
Write Cost ($2.50/GiB)
Charged when you add or update data in a collection. Measured by the total size of data written — vectors, documents, and metadata combined.
Data per record = (dimensions × 4 bytes) + document_bytes + metadata_bytes. Total write = records × data_per_record.
Writing 1M records with 1,536-dimension vectors, 8 KB documents, and 64 bytes metadata ≈ 13 GiB × $2.50 = ~$34.
Storage Cost ($0.33/GiB/month)
Monthly charge for all data stored across your collections — vectors, documents, metadata, and indexes. Built on object storage, so dramatically cheaper than RAM-based pricing.
Storage = total_records × data_per_record. Billed per GiB per month.
6M stored records at ~13.4 KB each ≈ 80 GiB × $0.33 = ~$27/month.
Query Cost ($0.0075/TiB queried + $0.09/GiB returned)
Combined cost of data scanned during search and network egress for returned results. Chroma uses HNSW indexing so only a fraction of the collection is scanned per query.
10M queries/month on an 80 GiB collection ≈ $19/month total query cost.
Network Egress ($0.09/GiB)
Data transferred out from Chroma to your application. Includes returned documents, metadata, and distances from query results.
Returning 10 results per query with metadata and document snippets — network cost is included in the overall query pricing.
GiB (Gibibyte)
1 GiB = 1,073,741,824 bytes (2³⁰). Chroma uses binary units for all billing. Slightly larger than a GB (1,000,000,000 bytes).
Collection
A named group of records (vectors + documents + metadata). Similar to a table in a relational database. Each collection has its own embedding space and can be queried independently.
A RAG app might have one collection for 'support-articles' and another for 'product-docs', each with different embedding models.
Database
A logical container for collections. Starter allows up to 10 databases, Team up to 100. Useful for separating environments (dev, staging, prod) or tenants.
Estimate Your Chroma Costs
Answer a few simple questions about your project and we'll estimate your monthly costs — no technical knowledge required. Fine-tune with advanced options if you want.
What are you building?
Select the use case that best describes your project. This helps us estimate storage, traffic, and recommend the right configuration. See an to understand how costs work before calculating.
Use Case Fit
See how Chroma aligns with different AI and search use cases.
Technical Deep Dive
Architecture, performance, developer experience, and security — everything you need to evaluate Chroma for production use.
Architecture
Search Capabilities
Performance
Cold-start latency is higher due to object storage architecture — first query loads data into SSD cache. Subsequent queries hit warm cache with <30ms p50 latency. Recall ranges 90–100% depending on HNSW parameters.
Deployment Options
Available on AWS, GCP
Developer Experience
Security & Compliance
Honest Trade-Offs
No technology is perfect. Here are the real limitations of Chroma — so you make an informed decision, not a surprised one.
| Trade-Off | Impact | Details |
|---|---|---|
| Cold-Start Query Latency | High | Because Chroma uses object storage, the first query after inactivity can take 650ms+ as data is loaded into SSD cache. Warm queries drop to ~20ms, but latency-sensitive applications need to account for cold starts. |
| 5M Record Collection Limit (Cloud) | Medium | Cloud collections are currently limited to 5M records each. For larger datasets, you must shard across multiple collections and handle routing yourself. Self-hosted has no limit. |
| No Quantization Support | Medium | Chroma doesn't offer scalar, product, or binary quantization. While object storage makes this less critical for cost, it means you can't compress vectors for faster search. Pinecone and Qdrant both offer quantization options. |
| Smaller Cloud Provider Coverage | Medium | Chroma Cloud is currently available on AWS and GCP only. Azure support is not yet available, which may be a blocker for Azure-centric organizations. |
| No RBAC or SSO (Yet) | Medium | Cloud authentication is API-key based only. There's no role-based access control or SSO integration for enterprise teams. Fine-grained permissions are on the roadmap but not yet available. |
| Younger Ecosystem Compared to Pinecone & Qdrant | Low | While growing rapidly (5M+ downloads/month), Chroma has fewer enterprise case studies, third-party integrations, and production hardening stories compared to established competitors. |
Because Chroma uses object storage, the first query after inactivity can take 650ms+ as data is loaded into SSD cache. Warm queries drop to ~20ms, but latency-sensitive applications need to account for cold starts.
Cloud collections are currently limited to 5M records each. For larger datasets, you must shard across multiple collections and handle routing yourself. Self-hosted has no limit.
Chroma doesn't offer scalar, product, or binary quantization. While object storage makes this less critical for cost, it means you can't compress vectors for faster search. Pinecone and Qdrant both offer quantization options.
Chroma Cloud is currently available on AWS and GCP only. Azure support is not yet available, which may be a blocker for Azure-centric organizations.
Cloud authentication is API-key based only. There's no role-based access control or SSO integration for enterprise teams. Fine-grained permissions are on the roadmap but not yet available.
While growing rapidly (5M+ downloads/month), Chroma has fewer enterprise case studies, third-party integrations, and production hardening stories compared to established competitors.