Chroma vs pgvector
type: Article
description: 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.
language: en-US
date published: 2026-07-09T15:34:26.373Z
date created: 2026-07-09T15:34:26.373Z
date modified: 2026-07-09T15:34:26.416Z
content reference time: 2026-07-09T15:34:26.416Z
author: A Versus B
publisher: A Versus B
about: Chroma
about: pgvector
abstract: 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.
text: Architecture Type. SQL/Relational Data Support. Setup Complexity. Vector Search Speed (1M vectors). Maximum Vector Dimension Support. Community Adoption (GitHub stars). License Type
conclusion text: 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.
additional property: Startup Time to First Query
  type: PropertyValue
  value: Chroma: 5 minutes | pgvector: N/A
additional property: Max Practical Vector Capacity
  type: PropertyValue
  value: Chroma: 0.1-1B (managed) | pgvector: N/A
additional property: Query Latency (1M vectors, CPU)
  type: PropertyValue
  value: Chroma: 50-200ms | pgvector: N/A
additional property: GPU Acceleration
  type: PropertyValue
  value: Chroma: Not available | pgvector: N/A
additional property: Hosting Flexibility
  type: PropertyValue
  value: Chroma: Managed cloud + local/open-source | pgvector: N/A
additional property: Learning Curve (hours for LLM RAG)
  type: PropertyValue
  value: Chroma: 0.5-2 hours | pgvector: N/A
additional property: Production Users at Scale
  type: PropertyValue
  value: Chroma: 500+ | pgvector: N/A
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: Query Latency (p99)
  type: PropertyValue
  value: Chroma: 50-200ms | pgvector: 100-500ms
  value reference: Chroma
additional property: Uptime SLA
  type: PropertyValue
  value: Chroma: None (community) | pgvector: Self-managed (varies)
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Chroma: 2-5 (pip install + Python) | 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: $0 (free) | pgvector: N/A
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Chroma: Limited to hardware (~1B) | pgvector: N/A
additional property: Uptime Guarantee
  type: PropertyValue
  value: Chroma: No SLA | pgvector: N/A
additional property: Documentation Quality Score
  type: PropertyValue
  value: Chroma: 8/10 | pgvector: N/A
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Chroma: Basic ($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: Query Latency (1M vectors)
  type: PropertyValue
  value: Chroma: 100-300ms | 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 (full SQL-like support) | pgvector: Limited (SQL WHERE clauses only)
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 minutes | pgvector: 30-120 minutes
  value reference: Chroma
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: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Chroma: 12,000+ stars | pgvector: ~10,500
  value reference: Chroma
additional property: Supported Index Types
  type: PropertyValue
  value: Chroma: Heuristic Search Algorithm (HNSW) | pgvector: 2 (HNSW, IVFFlat)
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: 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
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/chroma-vs-pgvector)
related link: https://www.aversusb.net/compare/pinecone-vs-pgvector
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: Open-source vector database optimized for rapid prototyping and lightweight deployments.
subject of: Chroma vs pgvector

pgvector
type: SoftwareApplication
alternate name: Pgvector
language: en-US
description: Open-source PostgreSQL extension for vector similarity search within existing PostgreSQL databases.
subject of: Chroma vs pgvector

Chroma vs pgvector — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Chroma vs pgvector. Contains 151 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: Startup Time to First Query
  type: PropertyValue
  value reference: Developer Experience
variable measured: Max Practical Vector Capacity
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (1M vectors, CPU)
  type: PropertyValue
  value reference: Performance
variable measured: GPU Acceleration
  type: PropertyValue
  value reference: Performance
variable measured: Hosting Flexibility
  type: PropertyValue
  value reference: Infrastructure
variable measured: Learning Curve (hours for LLM RAG)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Production Users at Scale
  type: PropertyValue
  value reference: Market Adoption
variable measured: Monthly Starting Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vector Storage
  type: PropertyValue
  value reference: Scalability
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: 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: Uptime Guarantee
  type: PropertyValue
  value reference: Performance
variable measured: Documentation Quality Score
  type: PropertyValue
  value reference: Developer Experience
variable measured: Metadata Filter Complexity
  type: PropertyValue
  value reference: Features
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Deployment
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: Integration
variable measured: Setup Time
  type: PropertyValue
  value reference: Implementation
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: DevOps Features
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: Deployment
variable measured: SQL Filtering Capability
  type: PropertyValue
  value reference: Querying
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community
variable measured: Supported Index Types
  type: PropertyValue
  value reference: Features
variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup Speed
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: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value reference: Features
variable measured: Multi-Tenancy Support
  type: PropertyValue
  value reference: Features
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: ACID Compliance
  type: PropertyValue
  value reference: Reliability
variable measured: Maximum Vector Scale
  type: PropertyValue
  value reference: Performance
variable measured: Minimum Setup Time
  type: PropertyValue
  value reference: Ease of Use
variable measured: Hybrid Search (Vector + Keyword)
variable measured: Multi-Modal Support
variable measured: Enterprise Features (RBAC/Multi-tenancy)
variable measured: GitHub Stars
variable measured: Deployment Options
variable measured: Setup Time (Minutes)
variable measured: Supported Data Sources
variable measured: Query Latency (P95)
variable measured: Maximum Embeddings
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: Developer Experience
has defined term: Scalability
has defined term: Performance
has defined term: Infrastructure
has defined term: Market Adoption
has defined term: Pricing
has defined term: Reliability
has defined term: Ease of Use
has defined term: Features
has defined term: Deployment
has defined term: Hardware
has defined term: Resource Usage
has defined term: Integration
has defined term: Implementation
has defined term: DevOps Features
has defined term: Querying
has defined term: Community
has defined term: Setup Speed
has defined term: Resource Efficiency
has defined term: Compatibility
has defined term: Enterprise
has defined term: Functionality
has defined term: Resource Requirements
has defined term: Container Orchestration
has defined term: Ecosystem
has defined term: Adoption
has defined term: Operations
has defined term: Performance & Scale
has defined term: Security
has defined term: Capability
has defined term: Integrations
has defined term: Feature
has defined term: Technology
has defined term: Setup & Deployment
has defined term: Query Capabilities
has defined term: Enterprise Features
has defined term: Licensing
has defined term: Support
has defined term: Flexibility
has defined term: Hardware Optimization
has defined term: Architecture
has defined term: AI Features
has defined term: Cost
has defined term: Limits
has defined term: Capabilities
has defined term: Operational Ease
has defined term: API

Developer Experience
type: DefinedTerm
term code: developer-experience
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

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Market Adoption
type: DefinedTerm
term code: market-adoption
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
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

Features
type: DefinedTerm
term code: features
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

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

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

DevOps Features
type: DefinedTerm
term code: devops-features
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

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

Setup Speed
type: DefinedTerm
term code: setup-speed
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

Enterprise
type: DefinedTerm
term code: enterprise
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

Resource Requirements
type: DefinedTerm
term code: resource-requirements
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

Adoption
type: DefinedTerm
term code: adoption
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

Performance & Scale
type: DefinedTerm
term code: performance-&-scale
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

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

Query Capabilities
type: DefinedTerm
term code: query-capabilities
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

Licensing
type: DefinedTerm
term code: licensing
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

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Chroma vs pgvector — Attribute Category Vocabulary

Hardware Optimization
type: DefinedTerm
term code: hardware-optimization
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

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

Limits
type: DefinedTerm
term code: limits
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

Operational Ease
type: DefinedTerm
term code: operational-ease
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: 2026-07-09T15:34:26.416Z
author: A Versus B
publisher: A Versus B
is part of: chroma-vs-pgvector))
main entity: Can I use both Chroma and pgvector together?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which is better for LLM embeddings from OpenAI?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: What's the cost difference between Chroma and pgvector?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which scales better to 10M+ vectors?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can pgvector replace my existing database?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Chroma and pgvector
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Architecture Type
step: Consider: SQL/Relational Data Support
step: Consider: Setup Complexity
step: Consider: Vector Search Speed (1M vectors)
step: Consider: Maximum Vector Dimension Support
step: Consider: Community Adoption (GitHub stars)
step: Consider: License Type
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

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

Consider: SQL/Relational Data Support
type: HowToStep
position: 2
text: SQL/Relational Data Support: Chroma has No native SQL, metadata filtering only, while pgvector has Full SQL + vector search combined.

Consider: Setup Complexity
type: HowToStep
position: 3
text: Setup Complexity: Chroma has Minutes (Python install), while pgvector has Hours (PostgreSQL + extension install).

Consider: Vector Search Speed (1M vectors)
type: HowToStep
position: 4
text: Vector Search Speed (1M vectors): Chroma has ~50-100ms avg latency, while pgvector has ~30-80ms avg latency.

Consider: Maximum Vector Dimension Support
type: HowToStep
position: 5
text: Maximum Vector Dimension Support: Chroma has Unlimited (dynamic), while pgvector has 2000 dimensions.

Consider: Community Adoption (GitHub stars)
type: HowToStep
position: 6
text: Community Adoption (GitHub stars): Chroma has 12,000+ stars, while pgvector has 9,500+ stars.

Consider: License Type
type: HowToStep
position: 7
text: License Type: Chroma has Apache 2.0 (Open source), while pgvector has PostgreSQL License (Open source).

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 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Chroma vs pgvector

Supported Deployment Modes
type: PropertyValue
value: Chroma: In-process, SQLite, HTTP API | pgvector: N/A

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

ACID Compliance
type: PropertyValue
value: Chroma: No | pgvector: Yes (full support)

Maximum Vector Scale
type: PropertyValue
value: Chroma: 10-50 million | pgvector: N/A

Minimum Setup Time
type: PropertyValue
value: Chroma: 2-5 minutes | pgvector: 120-300 minutes
value reference: Chroma

Hybrid Search (Vector + Keyword)
type: PropertyValue
value: Chroma: No | pgvector: N/A

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

Enterprise Features (RBAC/Multi-tenancy)
type: PropertyValue
value: Chroma: No | pgvector: N/A

GitHub Stars
type: PropertyValue
value: Chroma: 12,500+ | pgvector: ~10,800
value reference: Chroma

Deployment Options
type: PropertyValue
value: Chroma: Embedded, Python, Serverless (SaaS beta) | pgvector: N/A

Setup Time (Minutes)
type: PropertyValue
value: Chroma: 15-30 | pgvector: N/A

Supported Data Sources
type: PropertyValue
value: Chroma: 12 embedding models | pgvector: N/A

Query Latency (P95)
type: PropertyValue
value: Chroma: 45-120 | pgvector: N/A

Maximum Embeddings
type: PropertyValue
value: Chroma: 50M (in-memory) | pgvector: N/A

LLM Integration
type: PropertyValue
value: Chroma: Manual (requires wrapper code) | pgvector: N/A

Learning Curve (Hours)
type: PropertyValue
value: Chroma: 2-4 | pgvector: N/A

Production Deployments Reported
type: PropertyValue
value: Chroma: 500+ | pgvector: N/A

Deployment Complexity
type: PropertyValue
value: Chroma: 2/10 | pgvector: 7/10
value reference: Chroma

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: 10M | 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

Time to Production (First Query)
type: PropertyValue
value: Chroma: 7 minutes | pgvector: N/A

Maximum Recommended Vector Count
type: PropertyValue
value: Chroma: ~10M vectors | pgvector: N/A

Minimum RAM Requirement (Single Node)
type: PropertyValue
value: Chroma: 64 MB | pgvector: N/A

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

Multi-Tenancy
type: PropertyValue
value: Chroma: Not supported | pgvector: N/A

Open Source License
type: PropertyValue
value: Chroma: Apache 2.0 (Fully Open) | pgvector: PostgreSQL License (permissive)

Enterprise Support SLA
type: PropertyValue
value: Chroma: Community-driven, no SLA | pgvector: N/A

Setup Time (minutes to first working example)
type: PropertyValue
value: Chroma: 3 minutes | pgvector: N/A

Maximum Vector Capacity (single instance)
type: PropertyValue
value: Chroma: 10 million | pgvector: N/A

Query Latency at 1M vectors
type: PropertyValue
value: Chroma: 50-150ms | pgvector: N/A

Memory per Million Vectors
type: PropertyValue
value: Chroma: 1.5-2.0 GB | pgvector: N/A

Built-in Embedding Model Support
type: PropertyValue
value: Chroma: OpenAI, Cohere, Hugging Face, Ollama (6+ providers) | pgvector: N/A

Index Type Options
type: PropertyValue
value: Chroma: 2 (SQLite, DuckDB) | pgvector: N/A

GPU Acceleration Support
type: PropertyValue
value: Chroma: No | pgvector: N/A

p50 Query Latency (Global)
type: PropertyValue
value: Chroma: 250ms (cloud-hosted) | pgvector: N/A

Storage Cost (1M vectors, 1536-dim)
type: PropertyValue
value: Chroma: $0 | pgvector: N/A

Metadata Filtering Complexity
type: PropertyValue
value: Chroma: Basic equality/contains | pgvector: N/A

Supported Programming Languages
type: PropertyValue
value: Chroma: Python, JavaScript, Go, Rust | pgvector: N/A

Setup Time to First Query
type: PropertyValue
value: Chroma: 2-3 minutes | pgvector: 45 minutes
value reference: Chroma

Average Latency (1M vectors)
type: PropertyValue
value: Chroma: 75ms | pgvector: 55ms
value reference: pgvector

Maximum Vector Dimensions
type: PropertyValue
value: Chroma: Unlimited | pgvector: 2,000

SQL Query Support
type: PropertyValue
value: Chroma: No (metadata filters only) | pgvector: Yes (full SQL support)

GitHub Stars (2026)
type: PropertyValue
value: Chroma: 12,000+ | pgvector: 9,500+
value reference: Chroma

Minimum Monthly Cost (Production)
type: PropertyValue
value: Chroma: $0 (self-hosted) | pgvector: N/A

Metadata Filter Operators
type: PropertyValue
value: Chroma: 10+ | pgvector: N/A

GitHub Stars (Community)
type: PropertyValue
value: Chroma: ~15,000+ | pgvector: N/A

Memory Footprint (Installed)
type: PropertyValue
value: Chroma: 15MB | pgvector: N/A

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)

Free Tier Capacity
type: PropertyValue
value: Chroma: N/A | pgvector: Unlimited (self-hosted)

Production Starter Cost
type: PropertyValue
value: Chroma: N/A | pgvector: $0 (infra only)

Average Query Latency (P99)
type: PropertyValue
value: Chroma: N/A | pgvector: 100-300ms

Setup to Production Time
type: PropertyValue
value: Chroma: N/A | pgvector: 2-4

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)

Cost for 1M Vectors/Month
type: PropertyValue
value: Chroma: N/A | pgvector: $10-50

Supported Indexing Algorithms
type: PropertyValue
value: Chroma: N/A | pgvector: HNSW, IVFFlat, Exact

Infrastructure Required
type: PropertyValue
value: Chroma: N/A | pgvector: PostgreSQL instance (AWS RDS, self-hosted, etc.)

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
xpath: //*[@id='verdict']

Chroma — Wikipedia
type: CreativeWork

pgvector — Wikipedia
type: CreativeWork

Compare Chroma vs pgvector
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/chroma-vs-pgvector))
object: Chroma
object: pgvector

Hybrid Search (Vector + Keyword)
type: PropertyValue
value reference: Features

Multi-Modal Support
type: PropertyValue
value reference: Features

Enterprise Features (RBAC/Multi-tenancy)
type: PropertyValue
value reference: Features

GitHub Stars
type: PropertyValue
value reference: Community

Deployment Options
type: PropertyValue
value reference: Infrastructure

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

Supported Data Sources
type: PropertyValue
value reference: Integration

Query Latency (P95)
type: PropertyValue
value reference: Performance

Maximum Embeddings
type: PropertyValue
value reference: Scalability

LLM Integration
type: PropertyValue
value reference: Features

Learning Curve (Hours)
type: PropertyValue
value reference: Ease of Use

Production Deployments Reported
type: PropertyValue
value reference: Adoption

Deployment Complexity
type: PropertyValue
value reference: Operations

Initial Setup Time
type: PropertyValue
value reference: Implementation

Minimum Monthly Cost
type: PropertyValue
value reference: Pricing

Production Plan Cost
type: PropertyValue
value reference: Pricing

Maximum Vector Capacity
type: PropertyValue
value reference: Performance & Scale

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: Deployment

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

Time to Production (First Query)
type: PropertyValue
value reference: Setup & Deployment

Maximum Recommended Vector Count
type: PropertyValue
value reference: Scalability

Minimum RAM Requirement (Single Node)
type: PropertyValue
value reference: Infrastructure

Advanced Filtering Support
type: PropertyValue
value reference: Query Capabilities

Multi-Tenancy
type: PropertyValue
value reference: Enterprise Features

Open Source License
type: PropertyValue
value reference: Licensing

Enterprise Support SLA
type: PropertyValue
value reference: Support

Setup Time (minutes to first working example)
type: PropertyValue
value reference: Developer Experience

Maximum Vector Capacity (single instance)
type: PropertyValue
value reference: Scalability

Query Latency at 1M vectors
type: PropertyValue
value reference: Performance

Memory per Million Vectors
type: PropertyValue
value reference: Resource Usage

Built-in Embedding Model Support
type: PropertyValue
value reference: Features

Index Type Options
type: PropertyValue
value reference: Flexibility

GPU Acceleration Support
type: PropertyValue
value reference: Hardware Optimization

p50 Query Latency (Global)
type: PropertyValue
value reference: Performance

Storage Cost (1M vectors, 1536-dim)
type: PropertyValue
value reference: Pricing

Metadata Filtering Complexity
type: PropertyValue
value reference: Features

Supported Programming Languages
type: PropertyValue
value reference: Integration

Setup Time to First Query
type: PropertyValue
value reference: Developer Experience

Average Latency (1M vectors)
type: PropertyValue
value reference: Performance

Maximum Vector Dimensions
type: PropertyValue
value reference: Capability

SQL Query Support
type: PropertyValue
value reference: Capability

GitHub Stars (2026)
type: PropertyValue
value reference: Community

Minimum Monthly Cost (Production)
type: PropertyValue
value reference: Pricing

Metadata Filter Operators
type: PropertyValue
value reference: Features

GitHub Stars (Community)
type: PropertyValue
value reference: Adoption

Memory Footprint (Installed)
type: PropertyValue
value reference: Resource Usage

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: Architecture

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

Free Tier Capacity
type: PropertyValue
value reference: Limits

Production Starter Cost
type: PropertyValue
value reference: Pricing

Average Query Latency (P99)
type: PropertyValue
value reference: Performance

Setup to Production Time
type: PropertyValue
value reference: Operations

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: Operational Ease

Native RESTful API
type: PropertyValue
value reference: API

Cost for 1M Vectors/Month
type: PropertyValue
value reference: Pricing

Supported Indexing Algorithms
type: PropertyValue
value reference: Capability

Infrastructure Required
type: PropertyValue
value reference: Operations