Chroma is a standalone vector database optimized for AI/ML workflows with simple Python APIs, while pgvector is a PostgreSQL extension that integrates vector search into existing relational databases. Chroma excels for specialized vector-only applications, whereas pgvector is better for applications needing both relational and vector data together.
Chroma vs pgvector — Side-by-side comparison on A Versus B
Open-source vector database optimized for rapid prototyping and lightweight deployments.
Open-source PostgreSQL extension for vector similarity search within existing PostgreSQL databases.
Architecture Type. SQL/Relational Data Support. Setup Complexity. Vector Search Speed (1M vectors). Maximum Vector Dimension Support. Community Adoption (GitHub stars). License Type
Choose Chroma if you need a lightweight, purpose-built vector database for AI/ML applications with rapid prototyping and don't require traditional SQL querying. Choose pgvector if you already use PostgreSQL, need to combine vector search with relational data queries, or require enterprise-grade database reliability and ACID compliance.
Chroma: 0.1-1B (managed) | pgvector: N/A
Chroma: Managed cloud + local/open-source | pgvector: N/A
Chroma: $0 (free, open-source) | pgvector: N/A
Chroma: ~10M (single instance practical limit) | pgvector: N/A
Chroma: None (community) | pgvector: Self-managed (varies)
Chroma: 2-5 (pip install + Python) | pgvector: N/A
Chroma: $0 (self-hosted only) | pgvector: N/A
Chroma: Limited to hardware (~1B) | pgvector: N/A
Chroma: 0.1 days (2-4 hours) | pgvector: 1-4 hours
Chroma: Yes (6+ models included) | pgvector: N/A
Chroma: Native (full SQL-like support) | pgvector: Limited (SQL WHERE clauses only)
Chroma: Experimental/Limited | pgvector: N/A
Query Latency (1M vectors, single query)
Chroma: 5 minutes | pgvector: 30-120 minutes
Chroma: External (0 native) | pgvector: N/A
Chroma: 1 (similarity search) | pgvector: N/A
Chroma: 3 (in-memory, SQLite, cloud) | pgvector: N/A
Query Latency (1M vectors, 768-dim, 10th percentile)
Chroma: Yes (OpenAI, HuggingFace, Ollama) | pgvector: No (external only)
Chroma: 5-10 minutes (Python package) | pgvector: Integrated (no new deployment)
Chroma: JSON metadata filters (limited) | pgvector: Full SQL WHERE clauses (unlimited)
Chroma: 12,000+ stars | pgvector: ~10,500
Chroma: Heuristic Search Algorithm (HNSW) | pgvector: 2 (HNSW, IVFFlat)
Chroma: 1-2 minutes | pgvector: 45-120 minutes
Chroma: Python + JavaScript | pgvector: N/A
Chroma: Not recommended; in-process only | pgvector: N/A
Chroma: Basic equality/contains only | pgvector: N/A
Chroma: Not supported | pgvector: Requires schema/RLS workarounds
Chroma: In-process, SQLite, HTTP API | pgvector: N/A
Chroma: 1-10M (single node) | pgvector: N/A
Chroma: Basic metadata filters | pgvector: N/A
Chroma: Text embeddings only | pgvector: N/A
Chroma: Not native; runs as Python process | pgvector: N/A
Chroma: Python 3.7+; runs on laptop or serverless | pgvector: N/A
Chroma: Official, first-class integration | pgvector: N/A
Chroma: No | pgvector: Yes (full support)
Chroma: 2-5 minutes | pgvector: 120-300 minutes
Enterprise Features (RBAC/Multi-tenancy)
Chroma: Embedded, Python, Serverless (SaaS beta) | pgvector: N/A
Chroma: 12 embedding models | pgvector: N/A
Chroma: Manual (requires wrapper code) | pgvector: N/A
Chroma: $0 (open-source) | pgvector: N/A
Chroma: $0 (self-hosted infrastructure only) | pgvector: N/A
Chroma: 10M (single machine limit) | pgvector: <1 billion (practical limit)
Chroma: Not tested (infeasible) | pgvector: N/A
Chroma: No (client libraries only) | pgvector: N/A
Chroma: Yes, official integration | pgvector: N/A
Single-Vector Search Latency (1M vectors)
Chroma: Limited (metadata filtering only) | pgvector: Full SQL with vector operators
Chroma: Yes (Hugging Face, OpenAI, etc.) | pgvector: No (external preprocessing required)
Chroma: Yes (Apache 2.0) | pgvector: Yes (PostgreSQL License)
Chroma: $50-150 | pgvector: $20-80 (AWS RDS)
Chroma: No (requires external database) | pgvector: Native (single database)
Chroma: Basic metadata matching | pgvector: N/A
Chroma: Flat, approximate nearest neighbor | pgvector: N/A
Chroma: Python, JavaScript, Go | pgvector: N/A
Chroma: Basic metadata filters only | pgvector: N/A
Chroma: Apache 2.0 (Fully Open) | pgvector: PostgreSQL License (permissive)
Chroma: Community-driven, no SLA | pgvector: N/A
Setup Time (minutes to first working example)
Maximum Vector Capacity (single instance)
Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | pgvector: N/A
Chroma: 2 (SQLite, DuckDB) | pgvector: N/A
Chroma: 250ms (cloud-hosted) | pgvector: N/A
Chroma: Basic equality/contains | pgvector: N/A
Chroma: Python, JavaScript, Go, Rust | pgvector: N/A
Chroma: 2-3 minutes | pgvector: 45 minutes
Chroma: No (metadata filters only) | pgvector: Yes (full SQL support)
Chroma: $0 (self-hosted) | pgvector: N/A
Chroma: N/A | pgvector: $0 (self-hosted only)
Chroma: N/A | pgvector: Up to 2,000 dimensions
Chroma: N/A | pgvector: User dependent (no SLA)
Chroma: N/A | pgvector: Yes (unified via SQL)
Chroma: N/A | pgvector: PostgreSQL extension module
Chroma: N/A | pgvector: 2 methods (IVFFlat, HNSW)
Average Query Latency (1M vectors, 384-dim)
Chroma: N/A | pgvector: None (requires external integration)
Minimum Monthly Infrastructure Cost (Self-hosted Production)
Chroma: N/A | pgvector: No, application-level only
Chroma: N/A | pgvector: 1-3 (read replicas)
Chroma: N/A | pgvector: Unlimited (self-hosted)
Chroma: N/A | pgvector: Full SQL + vector operators
Chroma: N/A | pgvector: 10-50 before latency issues
Chroma: N/A | pgvector: No (SQL-only via PostgreSQL client)
Chroma: N/A | pgvector: HNSW, IVFFlat, Exact
Chroma: N/A | pgvector: PostgreSQL instance (AWS RDS, self-hosted, etc.)
How to choose between Chroma and pgvector
Chroma vs pgvector — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs pgvector. Contains 151 attribute comparisons, 5 FAQ pairs.
Chroma vs pgvector — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs pgvector.
Can I use both Chroma and pgvector together?
Yes, some architectures use Chroma as a cache layer for fast vector retrieval and pgvector as the authoritative store. However, this adds complexity. Most applications choose one based on whether relational data integration is required.
Which is better for LLM embeddings from OpenAI?
Both handle OpenAI embeddings (1536 dimensions) efficiently. Chroma is faster to set up, but pgvector is more reliable for production. Choose Chroma for prototyping RAG apps, pgvector if you need to combine embeddings with customer/product data in SQL.
What's the cost difference between Chroma and pgvector?
Both are open-source and free to self-host. pgvector requires PostgreSQL hosting (AWS RDS ~$20-200/month). Chroma offers a managed cloud service (Chroma Cloud), while pgvector integration depends on your existing PostgreSQL provider.
pgvector generally scales better due to PostgreSQL's mature indexing (IVFFLAT, HNSW). Chroma can handle 10M+ vectors but requires more memory overhead. For 50M+ vectors, pgvector with proper indexing is the safer choice.
Can pgvector replace my existing database?
pgvector is a PostgreSQL extension, not a replacement. It extends your existing PostgreSQL database with vector capabilities. If you're using MySQL, Oracle, or MongoDB, you'd need to migrate to PostgreSQL to use pgvector, whereas Chroma works standalone.
How to Choose Between Chroma and pgvector
Architecture Type: Chroma has Standalone vector database, while pgvector has PostgreSQL extension/plugin.
SQL/Relational Data Support: Chroma has No native SQL, metadata filtering only, while pgvector has Full SQL + vector search combined.
Setup Complexity: Chroma has Minutes (Python install), while pgvector has Hours (PostgreSQL + extension install).
Consider: Vector Search Speed (1M vectors)
Vector Search Speed (1M vectors): Chroma has ~50-100ms avg latency, while pgvector has ~30-80ms avg latency.
Consider: Maximum Vector Dimension Support
Maximum Vector Dimension Support: Chroma has Unlimited (dynamic), while pgvector has 2000 dimensions.
Consider: Community Adoption (GitHub stars)
Community Adoption (GitHub stars): Chroma has 12,000+ stars, while pgvector has 9,500+ stars.
License Type: Chroma has Apache 2.0 (Open source), while pgvector has PostgreSQL License (Open source).