Chroma is a lightweight, Python-first vector database optimized for rapid prototyping and small-to-medium projects, while Weaviate is a more feature-rich, enterprise-grade vector database with advanced filtering, multi-tenancy, and production-scale capabilities.
Chroma vs Weaviate — Side-by-side comparison on A Versus B
Lightweight, open-source vector database optimized for Python developers and rapid development.
Enterprise-grade vector database with advanced search capabilities, multi-tenancy, and Kubernetes-native deployment.
Primary Use Case. Deployment Options. Setup Complexity (Time to First Vector). Advanced Filtering Capabilities. Multi-Tenancy Support. Memory Footprint (Typical Small Deployment). Enterprise Support & SLAs
Choose Chroma if you're building prototypes, learning RAG systems, or need a lightweight embedded vector store that gets you working in minutes. Choose Weaviate if you're deploying production search infrastructure, need multi-tenancy, advanced filtering, or require enterprise-grade support and scalability for 100M+ vectors.
Chroma: $0 (free, open-source) | Weaviate: N/A
Chroma: ~10M (single instance practical limit) | Weaviate: N/A
Chroma: Community-dependent (no SLA) | Weaviate: User-managed (no SLA)
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: 0.1 days (2-4 hours) | Weaviate: 24-72 hours
Chroma: Yes (6+ models included) | Weaviate: N/A
Chroma: Native (boolean operators) | Weaviate: N/A
Chroma: Experimental/Limited | Weaviate: N/A
Query Latency (1M vectors, single query)
Chroma: External (0 native) | Weaviate: N/A
Chroma: 1 (similarity search) | Weaviate: N/A
Chroma: 3 (in-memory, SQLite, cloud) | Weaviate: N/A
Query Latency (1M vectors, 768-dim, 10th percentile)
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: 12,000+ stars | Weaviate: 9,500+ stars
Chroma: Heuristic Search Algorithm (HNSW) | Weaviate: N/A
Chroma: 1-2 minutes | Weaviate: 30-45 minutes (self-hosted)
Chroma: Python + JavaScript | Weaviate: N/A
Chroma: Not recommended; in-process only | Weaviate: N/A
Chroma: Basic equality/contains only | Weaviate: N/A
Chroma: ~10M | Weaviate: 100M+ (distributed)
Chroma: 2-5 (pip install) | Weaviate: 30-60 (with Docker)
Chroma: Not supported | Weaviate: Native multi-tenancy with data isolation
Chroma: In-process, SQLite, HTTP API | Weaviate: Docker, Kubernetes, Cloud (AWS/GCP/Azure)
Chroma: 2-5 | Weaviate: 30+ minutes (self-hosted)
Chroma: 1-10M (single node) | Weaviate: 100M+ (distributed)
Chroma: Basic metadata filters | Weaviate: Advanced GraphQL + WHERE clauses with boolean logic
Chroma: Text embeddings only | Weaviate: Text, image, audio, video
Chroma: Not native; runs as Python process | Weaviate: Native Kubernetes-ready Helm charts
Chroma: Python 3.7+; runs on laptop or serverless | Weaviate: Docker/Kubernetes cluster (4GB+ RAM minimum)
Chroma: Official, first-class integration | Weaviate: Supported but secondary to GraphQL API
Chroma: 10-50 million | Weaviate: 1+ billion
Chroma: 2-5 minutes | Weaviate: 30-60 minutes
Chroma: Text only | Weaviate: Text, image, audio via modules
Enterprise Features (RBAC/Multi-tenancy)
Chroma: Embedded, Python, Serverless (SaaS beta) | Weaviate: Kubernetes, Docker, cloud (AWS/GCP/Azure)
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 | Weaviate: N/A
Single-Vector Search Latency (1M vectors)
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 | Weaviate: N/A
Chroma: 7 minutes | Weaviate: 25 minutes
Chroma: ~10M vectors | Weaviate: 500M+ vectors
Chroma: Basic metadata filters only | Weaviate: Complex WHERE clauses, nested conditions, cross-references
Chroma: Not supported | Weaviate: Full native support with tenant isolation
Chroma: Apache 2.0 (Fully Open) | Weaviate: BSL 1.1 (Source-available, eventually open)
Chroma: Community-driven, no SLA | Weaviate: 99.5% guaranteed uptime
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | Weaviate: N/A
Chroma: 2 (SQLite, DuckDB) | Weaviate: N/A
Chroma: No | Weaviate: Limited (planning phase)
Chroma: N/A | Weaviate: Unlimited (self-hosted)
Chroma: N/A | Weaviate: $500-800 (managed)
Chroma: N/A | Weaviate: BM25 keyword + vector
Chroma: N/A | Weaviate: Standalone cluster (Kubernetes, Docker, Cloud)
Chroma: N/A | Weaviate: 3 methods (HNSW, flat, dynamic)
Average Query Latency (1M vectors, 384-dim)
Chroma: N/A | Weaviate: 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face)
Minimum Monthly Infrastructure Cost (Self-hosted Production)
Chroma: N/A | 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: 3 (text, image, audio)
Query Latency (1M vectors, 10 concurrent)
Chroma: N/A | 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: $0 (self-hosted) / $50+ (managed)
Chroma: N/A | 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: Yes (REST + GraphQL)
How to choose between Chroma and Weaviate
Chroma vs Weaviate — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs Weaviate. Contains 151 attribute comparisons, 5 FAQ pairs.
Chroma vs Weaviate — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs Weaviate.
Which should I choose for building a RAG chatbot prototype?
Chroma is the better choice for prototyping. Its embedded Python mode lets you spin up a vector database in your notebook in seconds, making it ideal for experimenting with LLM retrieval pipelines. Weaviate adds unnecessary complexity for early-stage projects.
Can I migrate from Chroma to Weaviate later?
Yes, both support exporting vectors and metadata in standard formats (JSON, Parquet). However, Weaviate's schema design and filtering syntax differ, so plan for 2-4 weeks of refactoring if migrating large production systems. Start with Chroma if you're uncertain about scale requirements.
Which is better for semantic search on 100M+ documents?
Weaviate is the only viable choice at that scale. Chroma's performance degrades significantly beyond 10M vectors due to its single-node optimization. Weaviate's distributed architecture, sharding, and replication support production-grade performance at 500M+ vectors with <500ms query latency.
No. Weaviate can run as a single Docker container for small deployments, but Kubernetes is recommended for production to leverage replication, auto-scaling, and failover. Alternatively, use Weaviate Cloud (managed SaaS starting at $350/month for 100M vectors).
Is Chroma suitable for production search applications?
Only for small-scale, single-user or team-internal use cases (<5M vectors). Chroma lacks multi-tenancy, role-based access control, and enterprise monitoring. For customer-facing search products, Weaviate's isolation and compliance features are essential.
How to Choose Between Chroma and Weaviate
Primary Use Case: Chroma has Rapid prototyping, ML pipelines, RAG applications, while Weaviate has Enterprise search, semantic search at scale, multi-tenant deployments.
Deployment Options: Chroma has In-process (embedded), Docker, minimal infrastructure, while Weaviate has Kubernetes, managed cloud, hybrid cloud, on-premises clusters.
Consider: Setup Complexity (Time to First Vector)
Setup Complexity (Time to First Vector): Chroma has 5-10 minutes with Python SDK, while Weaviate has 20-30 minutes with configuration requirements.
Consider: Advanced Filtering Capabilities
Advanced Filtering Capabilities: Chroma has Basic metadata filtering only, while Weaviate has Complex WHERE clauses, nested filtering, cross-reference queries.
Multi-Tenancy Support: Chroma has Not natively supported, while Weaviate has Full multi-tenancy with tenant-level isolation.
Consider: Memory Footprint (Typical Small Deployment)
Memory Footprint (Typical Small Deployment): Chroma has 50-150 MB RAM for embedded mode, while Weaviate has 500 MB - 2 GB for minimal cluster setup.
Enterprise Support & SLAs: Chroma has Community-driven, no official SLAs, while Weaviate has Paid enterprise support with 99.5% uptime SLA.