Chroma vs Weaviate
Chroma
Lightweight, open-source vector database optimized for Python-first RAG and embedding search workflows.
Developers prototyping RAG applications, startups building MVPs, AI engineers experimenting with LLMs, students learning vector embeddings, small teams without DevOps resources.
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
Enterprises running production search platforms, teams managing 50M+ vectors, organizations needing multi-modal AI search, companies requiring complex access control and audit logging, startups planning scale from day one.
Short Answer
Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and RAG applications with minimal setup, while Weaviate is an enterprise-grade vector database with advanced filtering, multi-modal search, and production-scale distributed architecture. Chroma excels for quick experimentation; Weaviate wins for complex, large-scale deployments.
Our Verdict
AI-assistedChoose Chroma if you're building a prototype, RAG chatbot, or small-to-medium application that prioritizes ease-of-use and rapid iteration; its minimal dependencies and in-memory option make it ideal for MVPs and local development. Choose Weaviate if you need production-grade reliability, complex filtered searches, multi-modal capabilities, or plan to scale beyond 10M vectors across distributed infrastructure; its enterprise features and GraphQL API justify the added complexity.
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Choose Chroma if
Developers prototyping RAG applications, startups building MVPs, AI engineers experimenting with LLMs, students learning vector embeddings, small teams without DevOps resources.
Choose Weaviate if
Enterprises running production search platforms, teams managing 50M+ vectors, organizations needing multi-modal AI search, companies requiring complex access control and audit logging, startups planning scale from day one.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Chroma | Weaviate | Diff |
|---|---|---|---|
| Monthly Starting Cost(USD) | $0 (free, open-source) | — | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — | — |
| Maximum Vector Dimensions(dimensions) | 65,536 | Unlimited | — |
| Query Latency (p99)(milliseconds) | 50-200ms | 50-150ms | +25% |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — | — |
| GitHub Stars(stars) | ~15,000 stars (as of 2026) | ~9,500 stars (as of 2026) | +58% |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — | — |
| Starting Cost (Annual)(USD) | $0 (free) | — | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — | — |
| Query Latency (p95)(milliseconds) | 50-200ms local | — | — |
| Documentation Quality Score(out of 10) | 8/10 | — | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — | — |
| Setup Time to Production(days) | 0.1 days (2-4 hours) | — | — |
| Maximum Vector Scale(vectors) | ~10 million efficiently | — | — |
| Query Latency (1M vectors)(milliseconds) | 50-200ms | — | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | — | — |
| Data Connectors(connectors) | 0 (manual) | — | — |
| LLM Provider Support(providers) | External (0 native) | — | — |
| Minimum Deployment Size(megabytes) | 50 | — | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | — | — |
| Storage Backends(backend types) | 3 (in-memory, SQLite, cloud) | — | — |
| Query Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) | ~50ms | — | — |
| GitHub Stars (as of 2026)(stars) | ~14,000 | — | — |
| Time to First Query(minutes) | 5 minutes | 30-45 minutes (self-hosted) | -87% |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — | — |
| Number of Supported Languages(languages) | Python + JavaScript | — | — |
| Maximum Vectors Per Instance(vectors) | ~10M | 100M+ (distributed) | -90% |
| Average Query Latency(milliseconds) | 10-50ms | 50-150ms | -70% |
| Setup Time to First Query(minutes) | 2-5 (pip install) | 30-60 (with Docker) | -93% |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | 4-8GB | -75% |
| Setup Time (First Query)(minutes) | 2-5 minutes | 30-60 minutes | -93% |
| Max Recommended Vector Count(vectors) | 1-10M (single node) | 100M+ (distributed) | -90% |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | $500-800 (managed) | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | 75ms | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | $800 | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | 100+ | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | 2 (GraphQL, REST) | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | 100,000 QPS | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 2 billion vectors | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 5-10 minutes (cloud) | — |
| GitHub Community Stars(stars) | 13,000+ stars | 13,000+ stars | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 20+ LLM providers | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | 100-500 ms | — |
| Memory per 1M Vectors(GB) | 8-12 GB | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | 20-30 seconds | — |
| Built-in LLM Integrations(count) | 15+ providers | 15+ providers | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | $25/month | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | 5,000-10,000 | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Chroma
Rapid prototyping & RAG applications
Weaviate
Enterprise production & complex queries
Chroma
In-memory or simple persistent storage🏆
Weaviate
Distributed cluster with Kubernetes support
Chroma
Basic metadata filters only
Weaviate
Advanced WHERE filters, GraphQL API, complex boolean logic🏆
Chroma
Text embeddings only
Weaviate
Text, images, audio, video embeddings🏆
Chroma
Up to ~1-10M vectors
Weaviate
100M+ vectors across distributed nodes🏆
Chroma
2-5 minutes🏆
Weaviate
30-60 minutes
Chroma
~15,000 GitHub stars🏆
Weaviate
~9,500 GitHub stars
Full Comparison
| Attribute | Chroma | Weaviate |
|---|---|---|
| Monthly Starting Cost(USD) | $0 (free, open-source) | — |
| Cost at 10M Vectors/Month(USD) | $0 (self-hosted only) | — |
| Starting Cost (Annual)(USD) | $0 (free) | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | — |
| Maximum Vector Storage(Vectors) | ~10M (single instance practical limit) | — |
| Maximum Vectors at Scale(millions) | Limited to hardware (~1B) | — |
| Maximum Vector Scale(vectors) | ~10 million efficiently | — |
| Maximum Practical Dataset Size(vectors) | ~10 million | — |
| Maximum Vectors Per Instance(vectors) | ~10M | 100M+ (distributed) |
Show 3 more attributesMax Recommended Vector Count(vectors) 1-10M (single node) 100M+ (distributed) Maximum Scalability (distributed nodes)(nodes) 100+ — Maximum Collection Size(billion vectors) 2 billion vectors — | ||
| Maximum Vector Dimensions(dimensions) | 65,536 | Unlimited |
| Query Latency (p99)(milliseconds) | 50-200ms | 50-150ms |
| Query Latency (p95)(milliseconds) | 50-200ms local | — |
| Query Latency (1M vectors)(milliseconds) | 50-200ms | — |
| Query Latency (1M vectors, single query)(milliseconds) | 150-300ms | — |
| Minimum Deployment Size(megabytes) | 50 | — |
Show 7 more attributesQuery Latency (1M vectors, 768-dim, 10th percentile)(milliseconds) ~50ms — Average Query Latency(milliseconds) 10-50ms 50-150ms Indexing Methods Supported(count) 3 methods (HNSW, flat, dynamic) — Average Query Latency (1M vectors, 384-dim)(milliseconds) 75ms — Query Throughput(operations per second (QPS)) 100,000 QPS — Query Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — | ||
| Uptime SLA(percent) | None (community-supported) | Not guaranteed (self-hosted) |
| Uptime Guarantee(percent) | No SLA | — |
| Setup Time (Local Development)(Minutes) | 2-5 (pip install + Python) | — |
| Installation Complexity(steps to deploy) | 5-10 minutes (Python package) | — |
| Setup Time to First Query(minutes) | 2-5 (pip install) | 30-60 (with Docker) |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
| GitHub Stars(stars) | ~15,000 stars (as of 2026) | ~9,500 stars (as of 2026) |
| Documentation Quality Score(out of 10) | 8/10 | — |
| Metadata Filter Complexity(operators supported) | Basic ($where) | — |
| Embedded Tokenizer Support | Yes (6+ models included) | — |
| Metadata Filtering Support | Native (boolean operators) | — |
| Data Connectors(connectors) | 0 (manual) | — |
| Retrieval Strategy Types(strategies) | 1 (similarity search) | — |
Show 10 more attributesStorage Backends(backend types) 3 (in-memory, SQLite, cloud) — Built-in Embedding Generation Yes (OpenAI, HuggingFace, Ollama) — Hybrid Search Support (BM25 + Vector) No Yes Multi-tenancy Support Not supported Native with isolation Query Filtering Support Basic metadata filters Advanced GraphQL + WHERE clauses with boolean logic Multi-Modal Search Text embeddings only Text, image, audio, video Native Hybrid Search Support(null) BM25 keyword + vector — Built-in Hybrid Search Support Native BM25 + vector search — Number of Native LLM Integrations(integrations) 20+ LLM providers — GPU Acceleration Support Limited (planning phase) — | ||
| Setup Time to Production(days) | 0.1 days (2-4 hours) | — |
| Setup Time (First Query)(minutes) | 2-5 minutes | 30-60 minutes |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| GPU Support | Experimental/Limited | — |
| Memory Usage (10M vectors)(GB) | 3-5 GB | — |
| Setup Time(minutes) | 5 | — |
| LLM Provider Support(providers) | External (0 native) | — |
| Production Observability(feature count) | Basic logging | — |
| Kubernetes-Native Deployment | Not recommended; in-process only | — |
| SQL Filtering Capability | JSON metadata filters (limited) | — |
| Open Source License | Apache 2.0 (fully open) | — |
| GitHub Stars (as of 2026)(stars) | ~14,000 | — |
| GitHub Community Stars(stars) | 13,000+ stars | — |
| Supported Index Types(count) | Heuristic Search Algorithm (HNSW) | — |
| Time to First Query(minutes) | 5 minutes | 30-45 minutes (self-hosted) |
| Memory Footprint (at rest, 1M vectors)(MB) | ~800MB | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Number of Supported Languages(languages) | Python + JavaScript | — |
| Complex Metadata Filtering Support | Basic equality/contains only | — |
| Minimum Memory for 1M Vectors(GB) | 1-2GB | 4-8GB |
| Supported Deployment Modes | In-process, SQLite, HTTP API | Docker, Kubernetes, Cloud (AWS/GCP/Azure) |
| Minimum Setup Infrastructure | Python 3.7+; runs on laptop or serverless | Docker/Kubernetes cluster (4GB+ RAM minimum) |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Kubernetes Support | Not native; runs as Python process | Native Kubernetes-ready Helm charts |
| LangChain Integration Maturity | Official, first-class integration | Supported but secondary to GraphQL API |
| Free Tier Vector Limit(vectors) | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $500-800 (managed) | — |
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
Show 3 more attributes
Show 7 more attributes
Show 10 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Chroma
Pros
- Zero-setup in-memory mode (pip install chroma, import, use)
- Python-native API with simple persist() for SQLite backend
- 15,000+ GitHub stars indicating strong community adoption
- ~5 minute onboarding vs competitors; minimal learning curve
- Built-in LangChain/LlamaIndex integrations for RAG pipelines
Cons
- Basic metadata filtering only; no complex boolean query logic
- Text embeddings only; no image/audio/video search support
- Not recommended above 10M vectors; single-node scaling limits
Weaviate
Pros
- GraphQL API enables complex nested queries and boolean filtering (WHERE clauses with AND/OR/NOT logic)
- Multi-modal search: text, image, audio, video embeddings in single query
- Distributed architecture scales to 100M+ vectors across multiple nodes
- Kubernetes-native deployment with cloud provider integrations (AWS, GCP, Azure)
- Hybrid search combining vector similarity + BM25 keyword ranking
Cons
- 30-60 minute setup; requires Kubernetes or Docker orchestration knowledge
- Steeper learning curve; GraphQL and schema design add complexity
- Higher memory/compute footprint; not suitable for resource-constrained environments
Frequently Asked Questions
For small datasets (<10M vectors), Chroma and Weaviate have similar query latency (10-50ms), but Weaviate's distributed design scales better. At 100M+ vectors, Weaviate maintains <100ms p99 latency while Chroma degrades significantly. For prototyping, both are fast enough; for production scale, Weaviate wins.
Resources & Learn More
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