Chroma vs pgvector
type: Article
description: 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.
language: en-US
date published: 2026-07-07T12:53:18.683Z
date created: 2026-07-07T12:53:18.683Z
date modified: 2026-07-07T12:53:18.743Z
content reference time: 2026-07-07T12:53:18.743Z
author: A Versus B
publisher: A Versus B
about: Chroma
about: pgvector
abstract: 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.
text: Architecture Type. Setup Complexity. Vector Search Latency (1M vectors). SQL Query Support. Maximum Vector Dimension Support. Pricing Model. Relational Data Integration
conclusion text: 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.
additional property: Monthly Starting Cost
  type: PropertyValue
  value: Chroma: $0 (free, open-source) | pgvector: N/A
additional property: Maximum Vector Storage
  type: PropertyValue
  value: Chroma: ~10M (single instance practical limit) | pgvector: N/A
additional property: Maximum Vector Dimensions
  type: PropertyValue
  value: Chroma: 65,536 | pgvector: 2,000
  value reference: Chroma
additional property: Query Latency (p99)
  type: PropertyValue
  value: Chroma: 50-200ms | pgvector: 50-500ms
  value reference: Chroma
additional property: Uptime SLA
  type: PropertyValue
  value: Chroma: Community-dependent (no SLA) | pgvector: N/A
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Chroma: 2-5 (pip install + Python) | pgvector: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: Chroma: 15,400+ | pgvector: N/A
additional property: Cost at 10M Vectors/Month
  type: PropertyValue
  value: Chroma: $0 (self-hosted only) | pgvector: N/A
additional property: Starting Cost (Annual)
  type: PropertyValue
  value: Chroma: $0 (free) | pgvector: N/A
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Chroma: Limited to hardware (~1B) | pgvector: N/A
additional property: Query Latency (p95)
  type: PropertyValue
  value: Chroma: 50-200ms local | pgvector: N/A
additional property: Uptime Guarantee
  type: PropertyValue
  value: Chroma: No SLA | pgvector: N/A
additional property: Documentation Quality Score
  type: PropertyValue
  value: Chroma: 8/10 | pgvector: N/A
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Chroma: Basic ($where) | pgvector: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Chroma: 0.1 days (2-4 hours) | pgvector: 1-4 hours
  value reference: Chroma
additional property: Maximum Vector Scale
  type: PropertyValue
  value: Chroma: ~10 million efficiently | pgvector: N/A
additional property: Query Latency (1M vectors)
  type: PropertyValue
  value: Chroma: 50-200ms | pgvector: N/A
additional property: Embedded Tokenizer Support
  type: PropertyValue
  value: Chroma: Yes (6+ models included) | pgvector: N/A
additional property: Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Native (boolean operators) | pgvector: N/A
additional property: GPU Support
  type: PropertyValue
  value: Chroma: Experimental/Limited | pgvector: N/A
additional property: Memory Usage (10M vectors)
  type: PropertyValue
  value: Chroma: 3-5 GB | pgvector: N/A
additional property: Query Latency (1M vectors, single query)
  type: PropertyValue
  value: Chroma: 150-300ms | pgvector: N/A
additional property: Maximum Practical Dataset Size
  type: PropertyValue
  value: Chroma: ~10 million | pgvector: N/A
additional property: Data Connectors
  type: PropertyValue
  value: Chroma: 0 (manual) | pgvector: N/A
additional property: Setup Time
  type: PropertyValue
  value: Chroma: 5 | pgvector: N/A
additional property: LLM Provider Support
  type: PropertyValue
  value: Chroma: External (0 native) | pgvector: N/A
additional property: Minimum Deployment Size
  type: PropertyValue
  value: Chroma: 50 | pgvector: N/A
additional property: Retrieval Strategy Types
  type: PropertyValue
  value: Chroma: 1 (similarity search) | pgvector: N/A
additional property: Storage Backends
  type: PropertyValue
  value: Chroma: 3 (in-memory, SQLite, cloud) | pgvector: N/A
additional property: Production Observability
  type: PropertyValue
  value: Chroma: Basic logging | pgvector: N/A
additional property: Query Latency (1M vectors, 768-dim, 10th percentile)
  type: PropertyValue
  value: Chroma: ~50ms | pgvector: ~120ms
  value reference: Chroma
additional property: Built-in Embedding Generation
  type: PropertyValue
  value: Chroma: Yes (OpenAI, HuggingFace, Ollama) | pgvector: No (external only)
additional property: Installation Complexity
  type: PropertyValue
  value: Chroma: 5-10 minutes (Python package) | pgvector: Integrated (no new deployment)
additional property: SQL Filtering Capability
  type: PropertyValue
  value: Chroma: JSON metadata filters (limited) | pgvector: Full SQL WHERE clauses (unlimited)
additional property: Open Source License
  type: PropertyValue
  value: Chroma: Apache 2.0 | pgvector: PostgreSQL License (permissive)
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Chroma: ~14,000 | pgvector: ~10,500
  value reference: Chroma
additional property: Supported Index Types
  type: PropertyValue
  value: Chroma: Heuristic Search Algorithm (HNSW) | pgvector: IVFFlat, HNSW (v0.7+)
additional property: Time to First Query
  type: PropertyValue
  value: Chroma: 1-2 minutes | pgvector: 45-120 minutes
  value reference: Chroma
additional property: Memory Footprint (at rest, 1M vectors)
  type: PropertyValue
  value: Chroma: ~800MB | pgvector: N/A
additional property: Number of Supported Languages
  type: PropertyValue
  value: Chroma: Python + JavaScript | pgvector: N/A
additional property: Kubernetes-Native Deployment
  type: PropertyValue
  value: Chroma: Not recommended; in-process only | pgvector: N/A
additional property: Complex Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Basic equality/contains only | pgvector: N/A
additional property: Maximum Vectors Per Instance
  type: PropertyValue
  value: Chroma: ~10M | pgvector: N/A
additional property: Average Query Latency
  type: PropertyValue
  value: Chroma: 10-50ms | pgvector: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: Chroma: 2-5 (pip install) | pgvector: N/A
additional property: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value: Chroma: No | pgvector: N/A
additional property: Multi-tenancy Support
  type: PropertyValue
  value: Chroma: Not supported | pgvector: Requires schema/RLS workarounds
additional property: Minimum Memory for 1M Vectors
  type: PropertyValue
  value: Chroma: 1-2GB | pgvector: N/A
additional property: Supported Deployment Modes
  type: PropertyValue
  value: Chroma: In-process, SQLite, HTTP API | pgvector: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/chroma
significant link: https://www.aversusb.net/alternatives/chroma
significant link: https://www.aversusb.net/entity/pgvector
significant link: https://www.aversusb.net/alternatives/pgvector
significant link: https://www.aversusb.net/api/answer/chroma-vs-pgvector)
significant link: https://www.aversusb.net/api/v1/schema/chroma-vs-pgvector)
related link: https://www.aversusb.net/compare/chroma-vs-pgvector
related link: https://www.aversusb.net/compare/pinecone-vs-pgvector
related link: https://www.aversusb.net/compare/pinecone-vs-chroma
related link: https://www.aversusb.net/compare/chroma-vs-pinecone
related link: https://www.aversusb.net/compare/weaviate-vs-pgvector
related link: https://www.aversusb.net/compare/chroma-vs-faiss
related link: https://www.aversusb.net/compare/chroma-vs-llamaindex
related link: https://www.aversusb.net/compare/chroma-vs-qdrant
teaches: How to choose between Chroma and pgvector
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Chroma vs pgvector — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Chroma
type: SoftwareApplication
language: en-US
description: Lightweight, open-source vector database optimized for LLM applications and rapid prototyping.
subject of: Chroma vs pgvector

pgvector
type: SoftwareApplication
alternate name: Pgvector
language: en-US
description: PostgreSQL extension enabling vector similarity search alongside traditional SQL queries.
subject of: Chroma vs pgvector

Chroma vs pgvector — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Chroma vs pgvector. Contains 101 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Monthly Starting Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vector Storage
  type: PropertyValue
  value reference: Scalability
variable measured: Maximum Vector Dimensions
  type: PropertyValue
  value reference: Performance
variable measured: Query Latency (p99)
  type: PropertyValue
  value reference: Performance
variable measured: Uptime SLA
  type: PropertyValue
  value reference: Reliability
variable measured: Setup Time (Local Development)
  type: PropertyValue
  value reference: Ease of Use
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: Cost at 10M Vectors/Month
  type: PropertyValue
  value reference: Pricing
variable measured: Starting Cost (Annual)
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vectors at Scale
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (p95)
  type: PropertyValue
  value reference: Performance
variable measured: Uptime Guarantee
  type: PropertyValue
  value reference: Reliability
variable measured: Documentation Quality Score
  type: PropertyValue
  value reference: Support
variable measured: Metadata Filter Complexity
  type: PropertyValue
  value reference: Features
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Implementation
variable measured: Maximum Vector Scale
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (1M vectors)
  type: PropertyValue
  value reference: Performance
variable measured: Embedded Tokenizer Support
  type: PropertyValue
  value reference: Features
variable measured: Metadata Filtering Support
  type: PropertyValue
  value reference: Features
variable measured: GPU Support
  type: PropertyValue
  value reference: Hardware
variable measured: Memory Usage (10M vectors)
  type: PropertyValue
  value reference: Resource Usage
variable measured: Query Latency (1M vectors, single query)
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Practical Dataset Size
  type: PropertyValue
  value reference: Scalability
variable measured: Data Connectors
  type: PropertyValue
  value reference: Features
variable measured: Setup Time
  type: PropertyValue
  value reference: Ease of Use
variable measured: LLM Provider Support
  type: PropertyValue
  value reference: Integration
variable measured: Minimum Deployment Size
  type: PropertyValue
  value reference: Performance
variable measured: Retrieval Strategy Types
  type: PropertyValue
  value reference: Features
variable measured: Storage Backends
  type: PropertyValue
  value reference: Features
variable measured: Production Observability
  type: PropertyValue
  value reference: Enterprise
variable measured: Query Latency (1M vectors, 768-dim, 10th percentile)
  type: PropertyValue
  value reference: Performance
variable measured: Built-in Embedding Generation
  type: PropertyValue
  value reference: Features
variable measured: Installation Complexity
  type: PropertyValue
  value reference: Setup
variable measured: SQL Filtering Capability
  type: PropertyValue
  value reference: Querying
variable measured: Open Source License
  type: PropertyValue
  value reference: Licensing
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
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variable measured: Supported Index Types
  type: PropertyValue
  value reference: Indexing
variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup & Deployment
variable measured: Memory Footprint (at rest, 1M vectors)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Number of Supported Languages
  type: PropertyValue
  value reference: Compatibility
variable measured: Kubernetes-Native Deployment
  type: PropertyValue
  value reference: Enterprise
variable measured: Complex Metadata Filtering Support
  type: PropertyValue
  value reference: Functionality
variable measured: Maximum Vectors Per Instance
  type: PropertyValue
  value reference: Scalability
variable measured: Average Query Latency
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time to First Query
  type: PropertyValue
  value reference: Ease of Use
variable measured: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value reference: Features
variable measured: Multi-tenancy Support
  type: PropertyValue
  value reference: Architecture
variable measured: Minimum Memory for 1M Vectors
  type: PropertyValue
  value reference: Resource Requirements
variable measured: Supported Deployment Modes
  type: PropertyValue
  value reference: Deployment
variable measured: Setup Time (first query)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Max Recommended Vector Count
  type: PropertyValue
  value reference: Scalability
variable measured: Query Filtering Support
  type: PropertyValue
  value reference: Features
variable measured: Multi-Modal Search
  type: PropertyValue
  value reference: Features
variable measured: Kubernetes Support
  type: PropertyValue
  value reference: Container Orchestration
variable measured: Minimum Setup Infrastructure
  type: PropertyValue
  value reference: Deployment
variable measured: LangChain Integration Maturity
  type: PropertyValue
  value reference: Ecosystem
variable measured: Initial Setup Time
  type: PropertyValue
  value reference: Operational Efficiency
variable measured: Minimum Monthly Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Production Plan Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vector Capacity
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (p99) at 100M Vectors
variable measured: REST API Support
variable measured: RBAC & Enterprise Security
variable measured: Maximum Vectors Per Index
variable measured: Query Latency (p50, local/optimal)
variable measured: Monthly Base Cost (starter tier)
variable measured: Supported Vector Dimensions
variable measured: LangChain Integration Native Support
variable measured: Single-Vector Search Latency (1M vectors)
is based on: https://www.aversusb.net/compare/chroma-vs-pgvector)

Chroma vs pgvector — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Chroma vs pgvector.
has defined term: Pricing
has defined term: Scalability
has defined term: Performance
has defined term: Reliability
has defined term: Ease of Use
has defined term: Community
has defined term: Support
has defined term: Features
has defined term: Implementation
has defined term: Hardware
has defined term: Resource Usage
has defined term: Integration
has defined term: Enterprise
has defined term: Setup
has defined term: Querying
has defined term: Licensing
has defined term: Indexing
has defined term: Setup & Deployment
has defined term: Resource Efficiency
has defined term: Compatibility
has defined term: Functionality
has defined term: Architecture
has defined term: Resource Requirements
has defined term: Deployment
has defined term: Developer Experience
has defined term: Container Orchestration
has defined term: Ecosystem
has defined term: Operational Efficiency
has defined term: Security
has defined term: Capability
has defined term: Integrations
has defined term: Feature
has defined term: Technology
has defined term: Operations
has defined term: AI Features
has defined term: Cost
has defined term: Enterprise Features
has defined term: Capabilities
has defined term: API

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Hardware
type: DefinedTerm
term code: hardware
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Querying
type: DefinedTerm
term code: querying
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Licensing
type: DefinedTerm
term code: licensing
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Indexing
type: DefinedTerm
term code: indexing
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Setup & Deployment
type: DefinedTerm
term code: setup-&-deployment
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Container Orchestration
type: DefinedTerm
term code: container-orchestration
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Operational Efficiency
type: DefinedTerm
term code: operational-efficiency
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Feature
type: DefinedTerm
term code: feature
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Technology
type: DefinedTerm
term code: technology
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

AI Features
type: DefinedTerm
term code: ai-features
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Enterprise Features
type: DefinedTerm
term code: enterprise-features
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

API
type: DefinedTerm
term code: api
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-07T12:53:18.743Z
author: A Versus B
publisher: A Versus B
is part of: chroma-vs-pgvector)
main entity: Which is faster for vector search—Chroma or pgvector?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I use pgvector and Chroma together?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which requires less DevOps overhead?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Does pgvector work with OpenAI embeddings or Hugging Face models?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can Chroma handle 100M+ vectors in production?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Chroma and pgvector
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Architecture Type
step: Consider: Setup Complexity
step: Consider: Vector Search Latency (1M vectors)
step: Consider: SQL Query Support
step: Consider: Maximum Vector Dimension Support
step: Consider: Pricing Model
step: Consider: Relational Data Integration
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Architecture Type
type: HowToStep
position: 1
text: Architecture Type: Chroma has Standalone vector database, while pgvector has PostgreSQL extension.

Consider: Setup Complexity
type: HowToStep
position: 2
text: Setup Complexity: Chroma has Minutes (single command), while pgvector has Hours (PostgreSQL + extension installation).

Consider: Vector Search Latency (1M vectors)
type: HowToStep
position: 3
text: Vector Search Latency (1M vectors): Chroma has ~15-25ms, while pgvector has ~30-50ms.

Consider: SQL Query Support
type: HowToStep
position: 4
text: SQL Query Support: Chroma has Limited (API-based filtering), while pgvector has Full SQL with vector operators.

Consider: Maximum Vector Dimension Support
type: HowToStep
position: 5
text: Maximum Vector Dimension Support: Chroma has 2048 dimensions, while pgvector has 2000+ dimensions.

Consider: Pricing Model
type: HowToStep
position: 6
text: Pricing Model: Chroma has Free (open-source), Cloud: $0.10-0.50/1M queries, while pgvector has Free (open-source) or PostgreSQL hosting costs.

Consider: Relational Data Integration
type: HowToStep
position: 7
text: Relational Data Integration: Chroma has No native support, while pgvector has Native (same database).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Chroma vs pgvector
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Chroma vs pgvector

Setup Time (first query)
type: PropertyValue
value: Chroma: 2-5 | pgvector: N/A

Max Recommended Vector Count
type: PropertyValue
value: Chroma: 1-10M (single node) | pgvector: N/A

Query Filtering Support
type: PropertyValue
value: Chroma: Basic metadata filters | pgvector: N/A

Multi-Modal Search
type: PropertyValue
value: Chroma: Text embeddings only | pgvector: N/A

Kubernetes Support
type: PropertyValue
value: Chroma: Not native; runs as Python process | pgvector: N/A

Minimum Setup Infrastructure
type: PropertyValue
value: Chroma: Python 3.7+; runs on laptop or serverless | pgvector: N/A

LangChain Integration Maturity
type: PropertyValue
value: Chroma: Official, first-class integration | pgvector: N/A

Initial Setup Time
type: PropertyValue
value: Chroma: 2 minutes | pgvector: N/A

Minimum Monthly Cost
type: PropertyValue
value: Chroma: $0 (open-source) | pgvector: N/A

Production Plan Cost
type: PropertyValue
value: Chroma: $0 (self-hosted infrastructure only) | pgvector: N/A

Maximum Vector Capacity
type: PropertyValue
value: Chroma: 10M (single machine limit) | pgvector: <1 billion (practical limit)
value reference: Chroma

Query Latency (p99) at 100M Vectors
type: PropertyValue
value: Chroma: Not tested (infeasible) | pgvector: N/A

REST API Support
type: PropertyValue
value: Chroma: No (client libraries only) | pgvector: N/A

RBAC & Enterprise Security
type: PropertyValue
value: Chroma: No | pgvector: N/A

Maximum Vectors Per Index
type: PropertyValue
value: Chroma: ~10 million | pgvector: N/A

Query Latency (p50, local/optimal)
type: PropertyValue
value: Chroma: 5-20ms | pgvector: N/A

Monthly Base Cost (starter tier)
type: PropertyValue
value: Chroma: $0 (open-source) | pgvector: N/A

Supported Vector Dimensions
type: PropertyValue
value: Chroma: Unlimited | pgvector: N/A

LangChain Integration Native Support
type: PropertyValue
value: Chroma: Yes, official integration | pgvector: N/A

Single-Vector Search Latency (1M vectors)
type: PropertyValue
value: Chroma: 15-25ms | pgvector: 30-50ms
value reference: Chroma

Maximum Supported Vector Dimensions
type: PropertyValue
value: Chroma: 2048 | pgvector: 2000+
value reference: Chroma

Native SQL Support
type: PropertyValue
value: Chroma: Limited (metadata filtering only) | pgvector: Full SQL with vector operators

Embedding Auto-Generation
type: PropertyValue
value: Chroma: Yes (Hugging Face, OpenAI, etc.) | pgvector: No (external preprocessing required)

Open-Source Availability
type: PropertyValue
value: Chroma: Yes (Apache 2.0) | pgvector: Yes (PostgreSQL License)

Managed Cloud Cost (1M queries/month)
type: PropertyValue
value: Chroma: $50-150 | pgvector: $20-80 (AWS RDS)
value reference: pgvector

Relational Data Integration
type: PropertyValue
value: Chroma: No (requires external database) | pgvector: Native (single database)

Query Latency (1M vectors, p99)
type: PropertyValue
value: Chroma: ~350ms | pgvector: N/A

Maximum Recommended Vectors
type: PropertyValue
value: Chroma: 50-100M | pgvector: N/A

Setup Time (local environment)
type: PropertyValue
value: Chroma: 2-3 minutes | pgvector: N/A

Supported Embedding Dimensions
type: PropertyValue
value: Chroma: Up to 2048 | pgvector: N/A

Filtering Query Support
type: PropertyValue
value: Chroma: Basic metadata matching | pgvector: N/A

Primary Indexing Algorithm
type: PropertyValue
value: Chroma: Flat, approximate nearest neighbor | pgvector: N/A

Language/SDK Support
type: PropertyValue
value: Chroma: Python, JavaScript, Go | pgvector: N/A

Minimum Setup Time
type: PropertyValue
value: Chroma: N/A | pgvector: 120-300 minutes

Cost for 1M Monthly Read Operations
type: PropertyValue
value: Chroma: N/A | pgvector: $0 (self-hosted only)

Vector Dimensionality Support
type: PropertyValue
value: Chroma: N/A | pgvector: Up to 2,000 dimensions

Uptime SLA Guarantee
type: PropertyValue
value: Chroma: N/A | pgvector: User dependent (no SLA)

SQL Relational Query Integration
type: PropertyValue
value: Chroma: N/A | pgvector: Yes (unified via SQL)

GitHub Community Stars
type: PropertyValue
value: Chroma: N/A | pgvector: 4,200+ stars

Deployment Model
type: PropertyValue
value: Chroma: N/A | pgvector: PostgreSQL extension module

Indexing Methods Supported
type: PropertyValue
value: Chroma: N/A | pgvector: 2 methods (IVFFlat, HNSW)

Average Query Latency (1M vectors, 384-dim)
type: PropertyValue
value: Chroma: N/A | pgvector: 120ms

Integrated LLM Providers
type: PropertyValue
value: Chroma: N/A | pgvector: None (requires external integration)

Minimum Monthly Infrastructure Cost (Self-hosted Production)
type: PropertyValue
value: Chroma: N/A | pgvector: $150

Native Multi-tenancy Support
type: PropertyValue
value: Chroma: N/A | pgvector: No, application-level only

Maximum Scalability (distributed nodes)
type: PropertyValue
value: Chroma: N/A | pgvector: 1-3 (read replicas)

API Query Language Support
type: PropertyValue
value: Chroma: N/A | pgvector: 1 (SQL only)

Vector Indexing Algorithm Options
type: PropertyValue
value: Chroma: N/A | pgvector: HNSW, IVFFlat

Query Type Flexibility
type: PropertyValue
value: Chroma: N/A | pgvector: Full SQL + vector operators

Scalability Limit (Single Node)
type: PropertyValue
value: Chroma: N/A | pgvector: 10-50 before latency issues

Operational Complexity (1-10 scale)
type: PropertyValue
value: Chroma: N/A | pgvector: Very Low (2/10)

Native RESTful API
type: PropertyValue
value: Chroma: N/A | pgvector: No (SQL-only via PostgreSQL client)

SpeakableSpecification
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Chroma — Wikipedia
type: CreativeWork

pgvector — Wikipedia
type: CreativeWork

Compare Chroma vs pgvector
type: Action
additional type: CompareAction
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object: Chroma
object: pgvector

Query Latency (p99) at 100M Vectors
type: PropertyValue
value reference: Performance

REST API Support
type: PropertyValue
value reference: Integration

RBAC & Enterprise Security
type: PropertyValue
value reference: Security

Maximum Vectors Per Index
type: PropertyValue
value reference: Scalability

Query Latency (p50, local/optimal)
type: PropertyValue
value reference: Performance

Monthly Base Cost (starter tier)
type: PropertyValue
value reference: Pricing

Supported Vector Dimensions
type: PropertyValue
value reference: Capability

LangChain Integration Native Support
type: PropertyValue
value reference: Integrations

Single-Vector Search Latency (1M vectors)
type: PropertyValue
value reference: Performance

Maximum Supported Vector Dimensions
type: PropertyValue
value reference: Capability

Native SQL Support
type: PropertyValue
value reference: Querying

Embedding Auto-Generation
type: PropertyValue
value reference: Feature

Open-Source Availability
type: PropertyValue
value reference: Licensing

Managed Cloud Cost (1M queries/month)
type: PropertyValue
value reference: Pricing

Relational Data Integration
type: PropertyValue
value reference: Capability

Query Latency (1M vectors, p99)
type: PropertyValue
value reference: Performance

Maximum Recommended Vectors
type: PropertyValue
value reference: Scalability

Setup Time (local environment)
type: PropertyValue
value reference: Ease of Use

Supported Embedding Dimensions
type: PropertyValue
value reference: Features

Filtering Query Support
type: PropertyValue
value reference: Features

Primary Indexing Algorithm
type: PropertyValue
value reference: Technology

Language/SDK Support
type: PropertyValue
value reference: Integration

Minimum Setup Time
type: PropertyValue
value reference: Deployment

Cost for 1M Monthly Read Operations
type: PropertyValue
value reference: Pricing

Vector Dimensionality Support
type: PropertyValue
value reference: Features

Uptime SLA Guarantee
type: PropertyValue
value reference: Reliability

SQL Relational Query Integration
type: PropertyValue
value reference: Features

GitHub Community Stars
type: PropertyValue
value reference: Community

Deployment Model
type: PropertyValue
value reference: Operations

Indexing Methods Supported
type: PropertyValue
value reference: Performance

Average Query Latency (1M vectors, 384-dim)
type: PropertyValue
value reference: Performance

Integrated LLM Providers
type: PropertyValue
value reference: AI Features

Minimum Monthly Infrastructure Cost (Self-hosted Production)
type: PropertyValue
value reference: Cost

Native Multi-tenancy Support
type: PropertyValue
value reference: Enterprise Features

Maximum Scalability (distributed nodes)
type: PropertyValue
value reference: Scalability

API Query Language Support
type: PropertyValue
value reference: Developer Experience

Vector Indexing Algorithm Options
type: PropertyValue
value reference: Performance

Query Type Flexibility
type: PropertyValue
value reference: Capabilities

Scalability Limit (Single Node)
type: PropertyValue
value reference: Performance

Operational Complexity (1-10 scale)
type: PropertyValue
value reference: Operations

Native RESTful API
type: PropertyValue
value reference: API