Chroma vs Pinecone
type: Article
description: Chroma is an open-source, lightweight vector database designed for embedded AI applications with zero infrastructure overhead, while Pinecone is a managed cloud vector database optimized for production-scale similarity search at enterprise levels. Chroma runs locally; Pinecone is fully hosted and serverless.
language: en-US
date published: 2026-07-07T06:06:56.276Z
date created: 2026-07-07T06:06:56.276Z
date modified: 2026-07-07T06:06:56.341Z
content reference time: 2026-07-07T06:06:56.341Z
author: A Versus B
publisher: A Versus B
about: Chroma
about: Pinecone
abstract: Chroma is an open-source, lightweight vector database designed for embedded AI applications with zero infrastructure overhead, while Pinecone is a managed cloud vector database optimized for production-scale similarity search at enterprise levels. Chroma runs locally; Pinecone is fully hosted and serverless.
text: Deployment Model. Setup Time. Starting Cost. Max Vectors Supported. Query Latency at Scale. Infrastructure Management. API-First Design
conclusion text: Choose Chroma if you're building prototypes, demos, or small-scale AI applications (< 1M vectors) where cost is critical and you control infrastructure. Choose Pinecone if you need production-grade reliability, auto-scaling to billions of vectors, enterprise SLAs, and don't want to manage infrastructure yourself.
additional property: Monthly Starting Cost
  type: PropertyValue
  value: Chroma: $0 (free, open-source) | Pinecone: $70 (minimum pod + index)
  value reference: Chroma
additional property: Maximum Vector Storage
  type: PropertyValue
  value: Chroma: ~10M (single instance practical limit) | Pinecone: 100M+ (unlimited with multi-pod)
  value reference: Pinecone
additional property: Maximum Vector Dimensions
  type: PropertyValue
  value: Chroma: 65,536 | Pinecone: 20,000
  value reference: Chroma
additional property: Query Latency (p99)
  type: PropertyValue
  value: Chroma: 50-200ms | Pinecone: 50-100ms
  value reference: Pinecone
additional property: Uptime SLA
  type: PropertyValue
  value: Chroma: N/A (user-managed) | Pinecone: 99.95%
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Chroma: 2-5 (pip install + Python) | Pinecone: 15-20 (account + API key setup)
  value reference: Chroma
additional property: GitHub Stars
  type: PropertyValue
  value: Chroma: ~15,000 stars (as of 2026) | Pinecone: Not open-source
additional property: Cost at 10M Vectors/Month
  type: PropertyValue
  value: Chroma: $0 (self-hosted only) | Pinecone: ~$150-200 (pod + index + compute)
  value reference: Chroma
additional property: Starting Cost (Annual)
  type: PropertyValue
  value: Chroma: $0 (free) | Pinecone: $50 (Starter tier minimum)
  value reference: Chroma
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Chroma: Limited to hardware (~1B) | Pinecone: 10B+ (unlimited)
  value reference: Pinecone
additional property: Query Latency (p95)
  type: PropertyValue
  value: Chroma: 50-200ms local | Pinecone: <100ms global
  value reference: Pinecone
additional property: Uptime Guarantee
  type: PropertyValue
  value: Chroma: No SLA | Pinecone: 99.95%
additional property: Documentation Quality Score
  type: PropertyValue
  value: Chroma: 8/10 | Pinecone: 9/10
  value reference: Pinecone
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Chroma: Basic ($where) | Pinecone: Advanced (AND/OR/NOT)
  value reference: Pinecone
additional property: Setup Time to Production
  type: PropertyValue
  value: Chroma: 0.1 days (2-4 hours) | Pinecone: 3-5 minutes
  value reference: Chroma
additional property: Maximum Vector Scale
  type: PropertyValue
  value: Chroma: ~10 million efficiently | Pinecone: N/A
additional property: Query Latency (1M vectors)
  type: PropertyValue
  value: Chroma: 50-200ms | Pinecone: N/A
additional property: Embedded Tokenizer Support
  type: PropertyValue
  value: Chroma: Yes (6+ models included) | Pinecone: N/A
additional property: Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Native (boolean operators) | Pinecone: N/A
additional property: GPU Support
  type: PropertyValue
  value: Chroma: Experimental/Limited | Pinecone: N/A
additional property: Memory Usage (10M vectors)
  type: PropertyValue
  value: Chroma: 3-5 GB | Pinecone: N/A
additional property: Query Latency (1M vectors, single query)
  type: PropertyValue
  value: Chroma: 150-300ms | Pinecone: N/A
additional property: Maximum Practical Dataset Size
  type: PropertyValue
  value: Chroma: ~10 million | Pinecone: N/A
additional property: Data Connectors
  type: PropertyValue
  value: Chroma: 0 (manual) | Pinecone: N/A
additional property: Setup Time
  type: PropertyValue
  value: Chroma: 5 | Pinecone: N/A
additional property: LLM Provider Support
  type: PropertyValue
  value: Chroma: External (0 native) | Pinecone: N/A
additional property: Minimum Deployment Size
  type: PropertyValue
  value: Chroma: 50 | Pinecone: N/A
additional property: Retrieval Strategy Types
  type: PropertyValue
  value: Chroma: 1 (similarity search) | Pinecone: N/A
additional property: Storage Backends
  type: PropertyValue
  value: Chroma: 3 (in-memory, SQLite, cloud) | Pinecone: N/A
additional property: Production Observability
  type: PropertyValue
  value: Chroma: Basic logging | Pinecone: N/A
additional property: Query Latency (1M vectors, 768-dim, 10th percentile)
  type: PropertyValue
  value: Chroma: ~50ms | Pinecone: N/A
additional property: Built-in Embedding Generation
  type: PropertyValue
  value: Chroma: Yes (OpenAI, HuggingFace, Ollama) | Pinecone: N/A
additional property: Installation Complexity
  type: PropertyValue
  value: Chroma: 5-10 minutes (Python package) | Pinecone: N/A
additional property: SQL Filtering Capability
  type: PropertyValue
  value: Chroma: JSON metadata filters (limited) | Pinecone: N/A
additional property: Open Source License
  type: PropertyValue
  value: Chroma: Apache 2.0 (fully open) | Pinecone: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Chroma: ~14,000 | Pinecone: N/A
additional property: Supported Index Types
  type: PropertyValue
  value: Chroma: Heuristic Search Algorithm (HNSW) | Pinecone: 1 (vector-only)
additional property: Time to First Query
  type: PropertyValue
  value: Chroma: 5 minutes | Pinecone: 5-10 minutes
  value reference: Chroma
additional property: Memory Footprint (at rest, 1M vectors)
  type: PropertyValue
  value: Chroma: ~800MB | Pinecone: N/A
additional property: Number of Supported Languages
  type: PropertyValue
  value: Chroma: Python + JavaScript | Pinecone: N/A
additional property: Kubernetes-Native Deployment
  type: PropertyValue
  value: Chroma: Not recommended; in-process only | Pinecone: N/A
additional property: Complex Metadata Filtering Support
  type: PropertyValue
  value: Chroma: Basic equality/contains only | Pinecone: N/A
additional property: Maximum Vectors Per Instance
  type: PropertyValue
  value: Chroma: ~10M | Pinecone: N/A
additional property: Average Query Latency
  type: PropertyValue
  value: Chroma: 10-50ms | Pinecone: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: Chroma: 2-5 (pip install) | Pinecone: N/A
additional property: Hybrid Search Support (BM25 + Vector)
  type: PropertyValue
  value: Chroma: No | Pinecone: N/A
additional property: Multi-tenancy Support
  type: PropertyValue
  value: Chroma: Not supported | Pinecone: N/A
additional property: Minimum Memory for 1M Vectors
  type: PropertyValue
  value: Chroma: 1-2GB | Pinecone: N/A
additional property: Supported Deployment Modes
  type: PropertyValue
  value: Chroma: In-process, SQLite, HTTP API | Pinecone: 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/pinecone
significant link: https://www.aversusb.net/alternatives/pinecone
significant link: https://www.aversusb.net/api/answer/chroma-vs-pinecone)
significant link: https://www.aversusb.net/api/v1/schema/chroma-vs-pinecone)
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/pinecone-vs-weaviate)
related link: https://www.aversusb.net/compare/llamaindex-vs-pinecone
related link: https://www.aversusb.net/compare/pinecone-vs-pgvector
related link: https://www.aversusb.net/compare/pinecone-vs-qdrant
related link: https://www.aversusb.net/compare/pinecone-vs-weaviate
related link: https://www.aversusb.net/compare/pinecone-vs-milvus
teaches: How to choose between Chroma and Pinecone
genre: Software

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

A Versus B
type: Organization

Chroma
type: SoftwareApplication
language: en-US
description: Open-source vector database for embedding storage and similarity search, optimized for AI apps and local development.
subject of: Chroma vs Pinecone

Pinecone
type: SoftwareApplication
language: en-US
description: Fully managed vector database as a service with enterprise SLAs and zero infrastructure overhead.
image: ImageObject
  caption: Pinecone
subject of: Chroma vs Pinecone
image: ImageObject
  caption: Pinecone

Chroma vs Pinecone — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Chroma vs Pinecone. Contains 102 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: Capabilities
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: Performance & 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: Code Transparency
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community
variable measured: Supported Index Types
  type: PropertyValue
  value reference: Indexing
variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup
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: 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: Usability
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: User Experience
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: Setup Time (Basic)
variable measured: Initial Cost
variable measured: Monthly Cost at 100M Vectors
variable measured: Vector Store Integrations
variable measured: Query Latency (p50)
variable measured: Free Tier Vector Capacity
is based on: https://www.aversusb.net/compare/chroma-vs-pinecone)

Chroma vs Pinecone — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Chroma vs Pinecone.
has defined term: Pricing
has defined term: Scalability
has defined term: Capabilities
has defined term: Performance
has defined term: Reliability
has defined term: Ease of Use
has defined term: Community
has defined term: Performance & Reliability
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: Code Transparency
has defined term: Indexing
has defined term: Resource Efficiency
has defined term: Compatibility
has defined term: Functionality
has defined term: Resource Requirements
has defined term: Deployment
has defined term: Usability
has defined term: Container Orchestration
has defined term: Ecosystem
has defined term: User Experience
has defined term: Security
has defined term: Limits
has defined term: Cost & Licensing
has defined term: Cost & Business
has defined term: Performance & Scalability
has defined term: Community & Adoption
has defined term: Infrastructure
has defined term: Pricing & Limits
has defined term: Flexibility
has defined term: Architecture

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

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

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

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

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

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

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

Performance & Reliability
type: DefinedTerm
term code: performance-&-reliability
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

Code Transparency
type: DefinedTerm
term code: code-transparency
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

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

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

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

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

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

Usability
type: DefinedTerm
term code: usability
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

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

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

Limits
type: DefinedTerm
term code: limits
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

Cost & Licensing
type: DefinedTerm
term code: cost-&-licensing
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

Cost & Business
type: DefinedTerm
term code: cost-&-business
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

Performance & Scalability
type: DefinedTerm
term code: performance-&-scalability
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

Community & Adoption
type: DefinedTerm
term code: community-&-adoption
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

Pricing & Limits
type: DefinedTerm
term code: pricing-&-limits
in defined term set: Chroma vs Pinecone — Attribute Category Vocabulary

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

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

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-07T06:06:56.341Z
author: A Versus B
publisher: A Versus B
is part of: chroma-vs-pinecone)
main entity: Can I use Chroma for production applications?
  type: Question
  accepted answer: Answer
    text: Yes, Chroma can be used in production for small to medium workloads (< 1M vectors) where you control infrastructure and accept single-machine limitations. However, for applications expecting high throughput, auto-scaling, or 24/7 uptime SLAs, Pinecone is safer. Many startups run Chroma in production on a single server until they scale beyond its limits.
    language: en-US
main entity: How much data can each store?
  type: Question
  accepted answer: Answer
    text: Chroma is limited by your machine's RAM and disk; practically maxes out around 10M vectors on a standard server. Pinecone stores 1B+ vectors across distributed infrastructure with built-in replication. For reference, 1M vectors at 1536 dimensions consumes ~6GB of RAM in Chroma.
    language: en-US
main entity: Which is faster for similarity search?
  type: Question
  accepted answer: Answer
    text: Pinecone is faster at scale: < 100ms p99 latency even at 100M+ vectors. Chroma latency depends on your hardware; on a single machine with 1M vectors, expect 50-200ms. However, Chroma can be faster for small datasets (< 100K vectors) on local machines due to no network overhead.
    language: en-US
main entity: Can I migrate from Chroma to Pinecone later?
  type: Question
  accepted answer: Answer
    text: Yes, migration is straightforward: export vectors from Chroma, transform to Pinecone's format, and bulk-upsert via API. Both use standard vector embedding formats. However, metadata schemas may differ; plan 1-2 weeks for large migrations (> 50M vectors). Pinecone provides migration guides.
    language: en-US
main entity: Do I need to choose one? Can I use both?
  type: Question
  accepted answer: Answer
    text: Many teams use Chroma for local development/testing and Pinecone for production. This hybrid approach lets you prototype cheaply, then scale to production without rewriting integrations. Both have similar Python APIs, making the transition straightforward.
    language: en-US

How to Choose Between Chroma and Pinecone
type: HowTo
description: Chroma is an open-source, lightweight vector database designed for embedded AI applications with zero infrastructure overhead, while Pinecone is a managed cloud vector database optimized for production-scale similarity search at enterprise levels. Chroma runs locally; Pinecone is fully hosted and serverless.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Setup Time
step: Consider: Starting Cost
step: Consider: Max Vectors Supported
step: Consider: Query Latency at Scale
step: Consider: Infrastructure Management
step: Consider: API-First Design
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Chroma if you're building prototypes, demos, or small-scale AI applications (< 1M vectors) where cost is critical and you control infrastructure. Choose Pinecone if you need production-grade reliability, auto-scaling to billions of vectors, enterprise SLAs, and don't want to manage infrastructure yourself.

Consider: Deployment Model
type: HowToStep
position: 1
text: Deployment Model: Chroma has Open-source, self-hosted or in-process, while Pinecone has Fully managed SaaS cloud platform.

Consider: Setup Time
type: HowToStep
position: 2
text: Setup Time: Chroma has < 5 minutes (pip install), while Pinecone has 5-15 minutes (API key + configuration).

Consider: Starting Cost
type: HowToStep
position: 3
text: Starting Cost: Chroma has $0 (open-source, self-hosted), while Pinecone has $0-$84/month (starter plan).

Consider: Max Vectors Supported
type: HowToStep
position: 4
text: Max Vectors Supported: Chroma has Limited by local hardware (typically < 10M), while Pinecone has 1B+ vectors across indexes.

Consider: Query Latency at Scale
type: HowToStep
position: 5
text: Query Latency at Scale: Chroma has 100-500ms (single machine, 1M vectors), while Pinecone has < 100ms (p99 latency, 100M+ vectors).

Consider: Infrastructure Management
type: HowToStep
position: 6
text: Infrastructure Management: Chroma has User responsible (scaling, updates, backups), while Pinecone has Fully managed by Pinecone.

Consider: API-First Design
type: HowToStep
position: 7
text: API-First Design: Chroma has Python/JavaScript client libraries; no REST API in free tier, while Pinecone has Full REST + gRPC API; multi-language SDKs.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Chroma vs Pinecone
review body: Chroma is an open-source, lightweight vector database designed for embedded AI applications with zero infrastructure overhead, while Pinecone is a managed cloud vector database optimized for production-scale similarity search at enterprise levels. Chroma runs locally; Pinecone is fully hosted and serverless.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Chroma vs Pinecone

Setup Time (First Query)
type: PropertyValue
value: Chroma: 2-5 minutes | Pinecone: < 5 minutes
value reference: Chroma

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

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

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

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

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

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

Initial Setup Time
type: PropertyValue
value: Chroma: 2 minutes | Pinecone: 10 minutes
value reference: Chroma

Minimum Monthly Cost
type: PropertyValue
value: Chroma: $0 (open-source) | Pinecone: $0 (free tier with limits)

Production Plan Cost
type: PropertyValue
value: Chroma: $0 (self-hosted infrastructure only) | Pinecone: $84 (Pro plan, 5M vectors)
value reference: Chroma

Maximum Vector Capacity
type: PropertyValue
value: Chroma: 10M (single machine limit) | Pinecone: 1B+ (distributed)
value reference: Pinecone

Query Latency (p99) at 100M Vectors
type: PropertyValue
value: Chroma: Not tested (infeasible) | Pinecone: < 100ms

REST API Support
type: PropertyValue
value: Chroma: No (client libraries only) | Pinecone: Yes (REST + gRPC)

RBAC & Enterprise Security
type: PropertyValue
value: Chroma: No | Pinecone: Yes (SOC 2 Type II, HIPAA)

Setup Time (Basic)
type: PropertyValue
value: Chroma: N/A | Pinecone: 5-10

Initial Cost
type: PropertyValue
value: Chroma: N/A | Pinecone: $0 (free tier limited to 1M vectors)

Monthly Cost at 100M Vectors
type: PropertyValue
value: Chroma: N/A | Pinecone: $400-600

Vector Store Integrations
type: PropertyValue
value: Chroma: N/A | Pinecone: 0 (standalone database)

Query Latency (p50)
type: PropertyValue
value: Chroma: N/A | Pinecone: 50-80

Free Tier Vector Capacity
type: PropertyValue
value: Chroma: N/A | Pinecone: 1

Pricing Model
type: PropertyValue
value: Chroma: N/A | Pinecone: Pay-per-usage (storage + queries)

Estimated Monthly Cost at 100GB
type: PropertyValue
value: Chroma: N/A | Pinecone: $200-400 (managed pricing)

Vector Dimension Limit
type: PropertyValue
value: Chroma: N/A | Pinecone: Unlimited

GitHub Stars/Community Size
type: PropertyValue
value: Chroma: N/A | Pinecone: ~2,500 stars

Self-Hosting Available
type: PropertyValue
value: Chroma: N/A | Pinecone: No (SaaS only)

Metadata Filtering Complexity
type: PropertyValue
value: Chroma: N/A | Pinecone: Basic payload filtering

SLA Uptime Guarantee
type: PropertyValue
value: Chroma: N/A | Pinecone: 99.95% (enterprise tier)

Minimum Setup Time
type: PropertyValue
value: Chroma: N/A | Pinecone: 15-30 minutes

Cost for 1M Monthly Read Operations
type: PropertyValue
value: Chroma: N/A | Pinecone: $0.40-1.25

Vector Dimensionality Support
type: PropertyValue
value: Chroma: N/A | Pinecone: Up to 20,000 dimensions

Uptime SLA Guarantee
type: PropertyValue
value: Chroma: N/A | Pinecone: 99.99%

SQL Relational Query Integration
type: PropertyValue
value: Chroma: N/A | Pinecone: No (separate system)

GitHub Community Stars
type: PropertyValue
value: Chroma: N/A | Pinecone: ~2,500 (closed-source)

Free Tier Vector Limit
type: PropertyValue
value: Chroma: N/A | Pinecone: 100,000 vectors

Estimated Monthly Cost (1M vectors)
type: PropertyValue
value: Chroma: N/A | Pinecone: $10 + storage

Native Hybrid Search Support
type: PropertyValue
value: Chroma: N/A | Pinecone: Metadata filtering only

Monthly Cost (1M vectors, 1K queries/day)
type: PropertyValue
value: Chroma: N/A | Pinecone: $45-80

Maximum Vectors Supported
type: PropertyValue
value: Chroma: N/A | Pinecone: 5 billion (enterprise)

Average Query Latency (p50)
type: PropertyValue
value: Chroma: N/A | Pinecone: 45-120ms

Setup Time (production-ready)
type: PropertyValue
value: Chroma: N/A | Pinecone: 0.25 hours

Native Integration Count
type: PropertyValue
value: Chroma: N/A | Pinecone: 25+ (LangChain, LlamaIndex, OpenAI)

Data Export Capability
type: PropertyValue
value: Chroma: N/A | Pinecone: Limited; JSON export only, subject to egress costs

Starting Monthly Cost
type: PropertyValue
value: Chroma: N/A | Pinecone: $25

Maximum Query Throughput
type: PropertyValue
value: Chroma: N/A | Pinecone: 5,000,000+

P99 Query Latency
type: PropertyValue
value: Chroma: N/A | Pinecone: < 50ms

GitHub Stars (Community)
type: PropertyValue
value: Chroma: N/A | Pinecone: Proprietary (not open-source)

Code Customization
type: PropertyValue
value: Chroma: N/A | Pinecone: Limited (SaaS constraints)

Free Tier Availability
type: PropertyValue
value: Chroma: N/A | Pinecone: None

Monthly Cost (1M vectors, 768 dims)
type: PropertyValue
value: Chroma: N/A | Pinecone: $4.00 + query fees

Time to Production
type: PropertyValue
value: Chroma: N/A | Pinecone: 5 minutes

Deployment Options
type: PropertyValue
value: Chroma: N/A | Pinecone: SaaS only (managed)

Hybrid Search Support
type: PropertyValue
value: Chroma: N/A | Pinecone: Yes (dense + BM25)

API Compatibility
type: PropertyValue
value: Chroma: N/A | Pinecone: Proprietary SDK + REST

SpeakableSpecification
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Chroma — Wikipedia
type: CreativeWork

Pinecone — Wikipedia
type: CreativeWork

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

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

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

Initial Cost
type: PropertyValue
value reference: Pricing

Monthly Cost at 100M Vectors
type: PropertyValue
value reference: Pricing

Vector Store Integrations
type: PropertyValue
value reference: Features

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

Free Tier Vector Capacity
type: PropertyValue
value reference: Limits

Pricing Model
type: PropertyValue
value reference: Cost & Licensing

Estimated Monthly Cost at 100GB
type: PropertyValue
value reference: Cost & Business

Vector Dimension Limit
type: PropertyValue
value reference: Performance & Scalability

GitHub Stars/Community Size
type: PropertyValue
value reference: Community & Adoption

Self-Hosting Available
type: PropertyValue
value reference: Infrastructure

Metadata Filtering Complexity
type: PropertyValue
value reference: Features

SLA Uptime Guarantee
type: PropertyValue
value reference: Reliability

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

Free Tier Vector Limit
type: PropertyValue
value reference: Pricing & Limits

Estimated Monthly Cost (1M vectors)
type: PropertyValue
value reference: Pricing & Limits

Native Hybrid Search Support
type: PropertyValue
value reference: Features

Monthly Cost (1M vectors, 1K queries/day)
type: PropertyValue
value reference: Pricing

Maximum Vectors Supported
type: PropertyValue
value reference: Scalability

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

Setup Time (production-ready)
type: PropertyValue
value reference: Ease of Use

Native Integration Count
type: PropertyValue
value reference: Ecosystem

Data Export Capability
type: PropertyValue
value reference: Flexibility

Starting Monthly Cost
type: PropertyValue
value reference: Pricing

Maximum Query Throughput
type: PropertyValue
value reference: Performance

P99 Query Latency
type: PropertyValue
value reference: Performance

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

Code Customization
type: PropertyValue
value reference: Flexibility

Free Tier Availability
type: PropertyValue
value reference: Pricing

Monthly Cost (1M vectors, 768 dims)
type: PropertyValue
value reference: Pricing

Time to Production
type: PropertyValue
value reference: Ease of Use

Deployment Options
type: PropertyValue
value reference: Architecture

Hybrid Search Support
type: PropertyValue
value reference: Features

API Compatibility
type: PropertyValue
value reference: Integration