Weaviate is an enterprise-focused vector database with multi-modal support and advanced filtering, while Chroma is a lightweight, developer-friendly alternative optimized for quick prototyping and small-to-medium scale deployments. Weaviate scales to billions of vectors; Chroma is better suited for projects under 10 million vectors.
Weaviate vs Chroma — Side-by-side comparison on A Versus B
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
Lightweight, open-source vector database optimized for Python developers and rapid development.
Maximum Scalability. Setup Complexity. Multi-modal Support. Hybrid Search (Vector + Keyword). Learning Curve. Enterprise Features. Community Adoption
Choose Weaviate if you need enterprise-grade scalability, multi-modal search, hybrid filtering, or plan to handle 100M+ vectors with high availability requirements. Choose Chroma if you're prototyping, building a small-to-medium RAG application, want minimal setup overhead, or prefer embedded vector search without infrastructure management.
Weaviate: Unlimited (self-hosted) | Chroma: N/A
Weaviate: $500-800 (managed) | Chroma: N/A
Weaviate: 30-45 minutes (self-hosted) | Chroma: 1-2 minutes
Weaviate: User-managed (no SLA) | Chroma: Community-dependent (no SLA)
Weaviate: BM25 keyword + vector | Chroma: N/A
Weaviate: Standalone cluster (Kubernetes, Docker, Cloud) | Chroma: N/A
Weaviate: 3 methods (HNSW, flat, dynamic) | Chroma: N/A
Average Query Latency (1M vectors, 384-dim)
Weaviate: 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | Chroma: N/A
Minimum Monthly Infrastructure Cost (Self-hosted Production)
Weaviate: Yes, with built-in tenant isolation | Chroma: N/A
Weaviate: 2 (GraphQL, REST) | Chroma: N/A
Weaviate: 2 billion vectors | Chroma: N/A
Weaviate: 5-10 minutes (cloud) | Chroma: N/A
Weaviate: Native BM25 + vector search | Chroma: N/A
Weaviate: $0-5000+/month (SaaS) | Chroma: N/A
Weaviate: 20+ LLM providers | Chroma: N/A
Weaviate: Limited (planning phase) | Chroma: No
Weaviate: 3 (text, image, audio) | Chroma: N/A
Weaviate: 100M+ (distributed) | Chroma: ~10M
Weaviate: 30-60 (with Docker) | Chroma: 2-5 (pip install)
Weaviate: Native multi-tenancy with data isolation | Chroma: Not supported
Weaviate: Docker, Kubernetes, Cloud (AWS/GCP/Azure) | Chroma: In-process, SQLite, HTTP API
Weaviate: 100M+ (distributed) | Chroma: 1-10M (single node)
Weaviate: Advanced GraphQL + WHERE clauses with boolean logic | Chroma: Basic metadata filters
Weaviate: Text, image, audio, video | Chroma: Text embeddings only
Weaviate: Native Kubernetes-ready Helm charts | Chroma: Not native; runs as Python process
Weaviate: Docker/Kubernetes cluster (4GB+ RAM minimum) | Chroma: Python 3.7+; runs on laptop or serverless
Weaviate: Supported but secondary to GraphQL API | Chroma: Official, first-class integration
Query Latency (1M vectors, 10 concurrent)
Weaviate: $300 (SaaS) or $0 (self-hosted) | Chroma: N/A
Weaviate: Yes (OpenAI, Cohere, HuggingFace, Azure) | Chroma: N/A
Weaviate: 3 (GraphQL, REST, Python) | Chroma: N/A
Weaviate: Unlimited (tested to 4096+) | Chroma: N/A
Weaviate: ~500 enterprise customers | Chroma: N/A
Weaviate: 1+ billion | Chroma: 10-50 million
Weaviate: 30-60 minutes | Chroma: 2-5 minutes
Weaviate: Text, image, audio via modules | Chroma: Text only
Enterprise Features (RBAC/Multi-tenancy)
Weaviate: Kubernetes, Docker, cloud (AWS/GCP/Azure) | Chroma: Embedded, Python, Serverless (SaaS beta)
Weaviate: $0 (self-hosted) / $50+ (managed) | Chroma: N/A
Weaviate: 30+ minutes (self-hosted) | Chroma: 2-5
Weaviate: Unlimited (open-source) | Chroma: N/A
Weaviate: HNSW, FLAT, IVF, PQ | Chroma: N/A
Weaviate: Vector-first (GraphQL, REST) | Chroma: N/A
Weaviate: 100+ with optimization | Chroma: N/A
Weaviate: 24-72 hours | Chroma: 0.1 days (2-4 hours)
Weaviate: Yes (REST + GraphQL) | Chroma: N/A
Weaviate: 25 minutes | Chroma: 7 minutes
Weaviate: 500M+ vectors | Chroma: ~10M vectors
Weaviate: Complex WHERE clauses, nested conditions, cross-references | Chroma: Basic metadata filters only
Weaviate: Full native support with tenant isolation | Chroma: Not supported
Weaviate: BSL 1.1 (Source-available, eventually open) | Chroma: Apache 2.0 (Fully Open)
Weaviate: 99.5% guaranteed uptime | Chroma: Community-driven, no SLA
Weaviate: 9,500+ stars | Chroma: 12,000+ stars
Weaviate: N/A | Chroma: $0 (free, open-source)
Weaviate: N/A | Chroma: ~10M (single instance practical limit)
Weaviate: N/A | Chroma: 2-5 (pip install + Python)
Weaviate: N/A | Chroma: $0 (self-hosted only)
Weaviate: N/A | Chroma: Limited to hardware (~1B)
Weaviate: N/A | Chroma: Yes (6+ models included)
Weaviate: N/A | Chroma: Native (boolean operators)
Weaviate: N/A | Chroma: Experimental/Limited
Query Latency (1M vectors, single query)
Weaviate: N/A | Chroma: External (0 native)
Weaviate: N/A | Chroma: 1 (similarity search)
Weaviate: N/A | Chroma: 3 (in-memory, SQLite, cloud)
Query Latency (1M vectors, 768-dim, 10th percentile)
Weaviate: N/A | Chroma: Yes (OpenAI, HuggingFace, Ollama)
Weaviate: N/A | Chroma: 5-10 minutes (Python package)
Weaviate: N/A | Chroma: JSON metadata filters (limited)
Weaviate: N/A | Chroma: Heuristic Search Algorithm (HNSW)
Weaviate: N/A | Chroma: Python + JavaScript
Weaviate: N/A | Chroma: Not recommended; in-process only
Weaviate: N/A | Chroma: Basic equality/contains only
Weaviate: N/A | Chroma: 12 embedding models
Weaviate: N/A | Chroma: Manual (requires wrapper code)
Weaviate: N/A | Chroma: $0 (open-source)
Weaviate: N/A | Chroma: $0 (self-hosted infrastructure only)
Weaviate: N/A | Chroma: 10M (single machine limit)
Weaviate: N/A | Chroma: Not tested (infeasible)
Weaviate: N/A | Chroma: No (client libraries only)
Weaviate: N/A | Chroma: Yes, official integration
Single-Vector Search Latency (1M vectors)
Weaviate: N/A | Chroma: Limited (metadata filtering only)
Weaviate: N/A | Chroma: Yes (Hugging Face, OpenAI, etc.)
Weaviate: N/A | Chroma: Yes (Apache 2.0)
Weaviate: N/A | Chroma: No (requires external database)
Weaviate: N/A | Chroma: Basic metadata matching
Weaviate: N/A | Chroma: Flat, approximate nearest neighbor
Weaviate: N/A | Chroma: Python, JavaScript, Go
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Weaviate: N/A | Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers)
Weaviate: N/A | Chroma: 2 (SQLite, DuckDB)
How to choose between Weaviate and Chroma
Weaviate vs Chroma — Structured Comparison Data
Machine-readable comparison dataset for Weaviate vs Chroma. Contains 151 attribute comparisons, 5 FAQ pairs.
Weaviate vs Chroma — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Weaviate vs Chroma.
Which is better for a small RAG chatbot prototype?
Chroma wins decisively. You can have a working RAG system running in your local Python environment in under 5 minutes. Weaviate requires Docker/Kubernetes setup and is overkill for prototyping. Start with Chroma; migrate to Weaviate only when you exceed 50M vectors or need enterprise features.
Can I use Weaviate and Chroma interchangeably?
Not for large scale. Both support vector search and similarity queries, but Weaviate adds hybrid BM25 search, multi-modal AI modules, and scales 100x further. APIs differ significantly: Weaviate uses GraphQL; Chroma uses Python/REST. Migration between them requires code refactoring.
Chroma is free and open-source with no infrastructure costs for embedded use. Weaviate is also open-source and free, but running production deployments requires Kubernetes infrastructure (AWS EKS, Google GKE, etc.), adding $200-2,000+/month depending on vector volume. Weaviate Cloud offers managed hosting starting at ~$100/month.
Which handles images and documents better?
Weaviate supports multi-modal embeddings natively via its module system (can embed images, PDFs, audio). Chroma handles text embeddings only; you must pre-compute image/audio embeddings externally and store vectors as text. For multi-modal RAG, Weaviate is the clear winner.
Can I start with Chroma and upgrade to Weaviate later?
Yes, but there's migration effort. Both store vectors similarly, but Weaviate's GraphQL API and schema structure differ from Chroma's Python API. You can export vectors from Chroma and import into Weaviate, but application code requires rewriting. Plan for 1-2 weeks of refactoring for medium-sized projects.
How to Choose Between Weaviate and Chroma
Maximum Scalability: Weaviate has Billions of vectors, while Chroma has ~10-50 million vectors (practical limit).
Setup Complexity: Weaviate has Moderate (Kubernetes/Docker recommended), while Chroma has Minimal (pip install, embed in Python).
Multi-modal Support: Weaviate has Yes (text, images, audio via modules), while Chroma has Text embeddings primarily.
Consider: Hybrid Search (Vector + Keyword)
Hybrid Search (Vector + Keyword): Weaviate has Yes (BM25 + vector hybrid), while Chroma has Vector-only, limited filtering.
Learning Curve: Weaviate has Steep (extensive API, multiple configuration options), while Chroma has Shallow (simple Python API, 10 lines to start).
Enterprise Features: Weaviate has RBAC, multi-tenancy, high availability, while Chroma has Basic auth, single-tenant only.
Community Adoption: Weaviate has ~4,000+ GitHub stars, active enterprise use, while Chroma has ~11,000+ GitHub stars, strong among startups.