Chroma is a standalone vector database optimized for simplicity and speed in AI/ML workflows, while pgvector is a PostgreSQL extension adding vector capabilities to an existing relational database. Chroma excels for dedicated vector search, whereas pgvector suits teams needing hybrid relational-vector queries.
Chroma vs pgvector — Side-by-side comparison on A Versus B
Lightweight, open-source vector database optimized for LLM applications and rapid prototyping.
PostgreSQL extension enabling vector similarity search alongside traditional SQL queries.
Architecture Type. Setup Complexity. Vector Search Latency (1M vectors). SQL Query Support. Maximum Vector Dimension Support. Pricing Model. Relational Data Integration
Choose Chroma if you need a purpose-built vector database with minimal setup, fast vector search, and straightforward AI/ML integrations for retrieval-augmented generation (RAG) and semantic search. Choose pgvector if you already use PostgreSQL, need complex SQL queries combining vector and relational data, or require a single database for hybrid workloads like recommendation systems with user metadata.
Chroma: $0 (free, open-source) | pgvector: N/A
Chroma: ~10M (single instance practical limit) | pgvector: N/A
Chroma: Community-dependent (no SLA) | pgvector: N/A
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: ~10 million efficiently | pgvector: N/A
Chroma: Yes (6+ models included) | pgvector: N/A
Chroma: Native (boolean operators) | pgvector: N/A
Chroma: Experimental/Limited | pgvector: N/A
Query Latency (1M vectors, single query)
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: Apache 2.0 | pgvector: PostgreSQL License (permissive)
Chroma: Heuristic Search Algorithm (HNSW) | pgvector: IVFFlat, HNSW (v0.7+)
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: 2-5 (pip install) | 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: $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: 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: Full SQL + vector operators
Chroma: N/A | pgvector: 10-50 before latency issues
Chroma: N/A | pgvector: No (SQL-only via PostgreSQL client)
How to choose between Chroma and pgvector
Chroma vs pgvector — Structured Comparison Data
Machine-readable comparison dataset for Chroma vs pgvector. Contains 101 attribute comparisons, 5 FAQ pairs.
Chroma vs pgvector — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Chroma vs pgvector.
Which is faster for vector search—Chroma or pgvector?
Chroma is faster: ~15-25ms for 1M-vector queries vs. pgvector's ~30-50ms on similar hardware. Chroma achieves this through simplified architecture focused solely on vectors. However, pgvector's HNSW indexing (available since v0.5) has closed the gap significantly; the difference is negligible for most applications under 10M vectors.
Yes. Some architectures use pgvector for relational data (user profiles, metadata) and Chroma for pure vector search (embeddings), syncing via API. However, this adds complexity. Choose one unless you specifically need different strengths—pgvector alone suffices for hybrid queries; Chroma alone works for vector-only use cases.
Chroma requires significantly less: deploy via Docker in 1-2 minutes with zero configuration. pgvector requires PostgreSQL expertise (installation, tuning, VACUUM operations, index management). If you already run PostgreSQL, pgvector adds minimal overhead; otherwise, Chroma is substantially simpler.
Does pgvector work with OpenAI embeddings or Hugging Face models?
pgvector stores pre-computed embeddings; it doesn't auto-generate them. You must compute embeddings externally (e.g., via OpenAI API, Hugging Face Inference API, Ollama) and insert vectors into PostgreSQL. Chroma automates this process with built-in model integrations.
Can Chroma handle 100M+ vectors in production?
Yes, but with caveats. Chroma Cloud (managed service) scales to 100M+ vectors with distributed indexing. Self-hosted Chroma on a single machine handles ~10-50M vectors depending on hardware. pgvector scales similarly; both require careful index tuning and hardware investment beyond these thresholds. Neither is ideal for >1B vector databases—specialized solutions like Pinecone or Weaviate may be better.
How to Choose Between Chroma and pgvector
Architecture Type: Chroma has Standalone vector database, while pgvector has PostgreSQL extension.
Setup Complexity: Chroma has Minutes (single command), while pgvector has Hours (PostgreSQL + extension installation).
Consider: Vector Search Latency (1M vectors)
Vector Search Latency (1M vectors): Chroma has ~15-25ms, while pgvector has ~30-50ms.
SQL Query Support: Chroma has Limited (API-based filtering), while pgvector has Full SQL with vector operators.
Consider: Maximum Vector Dimension Support
Maximum Vector Dimension Support: Chroma has 2048 dimensions, while pgvector has 2000+ dimensions.
Pricing Model: Chroma has Free (open-source), Cloud: $0.10-0.50/1M queries, while pgvector has Free (open-source) or PostgreSQL hosting costs.
Relational Data Integration: Chroma has No native support, while pgvector has Native (same database).