Pinecone vs Chroma
type: Article
description: Pinecone is a managed cloud vector database optimized for production-scale applications with 1M+ vectors, while Chroma is a lightweight, open-source vector database designed for rapid prototyping and local development with simpler deployment.
language: en-US
date published: 2026-07-07T09:23:27.136Z
date created: 2026-07-07T09:23:27.136Z
date modified: 2026-07-07T09:23:27.197Z
content reference time: 2026-07-07T09:23:27.197Z
author: A Versus B
publisher: A Versus B
about: Pinecone
about: Chroma
abstract: Pinecone is a managed cloud vector database optimized for production-scale applications with 1M+ vectors, while Chroma is a lightweight, open-source vector database designed for rapid prototyping and local development with simpler deployment.
text: Deployment Model. Scalability Limit. Setup Time. Cost Model. Production Readiness. Query Latency (p50). Learning Curve
conclusion text: Choose Pinecone if you're building production applications requiring sub-100ms latency, supporting 1B+ vectors, and need enterprise-grade reliability with managed infrastructure. Choose Chroma if you're prototyping, running locally, prioritize cost savings, or work with small-to-medium datasets (<10M vectors) where development speed matters more than managed scalability.
additional property: Setup Time (Basic)
  type: PropertyValue
  value: Pinecone: 5-10 | Chroma: N/A
additional property: Initial Cost
  type: PropertyValue
  value: Pinecone: $0 (free tier limited to 1M vectors) | Chroma: N/A
additional property: Monthly Cost at 100M Vectors
  type: PropertyValue
  value: Pinecone: $400-600 | Chroma: N/A
additional property: Supported Index Types
  type: PropertyValue
  value: Pinecone: 1 (vector-only) | Chroma: Heuristic Search Algorithm (HNSW)
additional property: Vector Store Integrations
  type: PropertyValue
  value: Pinecone: 0 (standalone database) | Chroma: N/A
additional property: Query Latency (p50)
  type: PropertyValue
  value: Pinecone: 50-80 | Chroma: N/A
additional property: Free Tier Vector Capacity
  type: PropertyValue
  value: Pinecone: 1 | Chroma: N/A
additional property: Pricing Model
  type: PropertyValue
  value: Pinecone: Pay-per-usage (storage + queries) | Chroma: N/A
additional property: Estimated Monthly Cost at 100GB
  type: PropertyValue
  value: Pinecone: $200-400 (managed pricing) | Chroma: N/A
additional property: Vector Dimension Limit
  type: PropertyValue
  value: Pinecone: Unlimited | Chroma: N/A
additional property: Time to First Query
  type: PropertyValue
  value: Pinecone: 5-10 minutes | Chroma: 5 minutes
  value reference: Chroma
additional property: GitHub Stars/Community Size
  type: PropertyValue
  value: Pinecone: ~2,500 stars | Chroma: N/A
additional property: Self-Hosting Available
  type: PropertyValue
  value: Pinecone: No (SaaS only) | Chroma: N/A
additional property: Metadata Filtering Complexity
  type: PropertyValue
  value: Pinecone: Basic payload filtering | Chroma: N/A
additional property: SLA Uptime Guarantee
  type: PropertyValue
  value: Pinecone: 99.95% (enterprise tier) | Chroma: N/A
additional property: Minimum Setup Time
  type: PropertyValue
  value: Pinecone: 15-30 minutes | Chroma: N/A
additional property: Cost for 1M Monthly Read Operations
  type: PropertyValue
  value: Pinecone: $0.40-1.25 | Chroma: N/A
additional property: Vector Dimensionality Support
  type: PropertyValue
  value: Pinecone: Up to 20,000 dimensions | Chroma: N/A
additional property: Uptime SLA Guarantee
  type: PropertyValue
  value: Pinecone: 99.99% | Chroma: N/A
additional property: SQL Relational Query Integration
  type: PropertyValue
  value: Pinecone: No (separate system) | Chroma: N/A
additional property: GitHub Community Stars
  type: PropertyValue
  value: Pinecone: ~2,500 (closed-source) | Chroma: N/A
additional property: Monthly Starting Cost
  type: PropertyValue
  value: Pinecone: $70 (minimum pod + index) | Chroma: $0 (free, open-source)
  value reference: Chroma
additional property: Maximum Vector Storage
  type: PropertyValue
  value: Pinecone: 100M+ (unlimited with multi-pod) | Chroma: ~10M (single instance practical limit)
  value reference: Pinecone
additional property: Maximum Vector Dimensions
  type: PropertyValue
  value: Pinecone: 20,000 | Chroma: 65,536
  value reference: Chroma
additional property: Query Latency (p99)
  type: PropertyValue
  value: Pinecone: 50-100ms | Chroma: 50-200ms
  value reference: Pinecone
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Pinecone: 15-20 (account + API key setup) | Chroma: 2-5 (pip install + Python)
  value reference: Chroma
additional property: GitHub Stars
  type: PropertyValue
  value: Pinecone: 11,200+ | Chroma: 15,400+
  value reference: Chroma
additional property: Cost at 10M Vectors/Month
  type: PropertyValue
  value: Pinecone: ~$150-200 (pod + index + compute) | Chroma: $0 (self-hosted only)
  value reference: Chroma
additional property: Free Tier Vector Limit
  type: PropertyValue
  value: Pinecone: 100,000 vectors | Chroma: N/A
additional property: Estimated Monthly Cost (1M vectors)
  type: PropertyValue
  value: Pinecone: $10 + storage | Chroma: N/A
additional property: Native Hybrid Search Support
  type: PropertyValue
  value: Pinecone: Metadata filtering only | Chroma: N/A
additional property: Monthly Cost (1M vectors, 1K queries/day)
  type: PropertyValue
  value: Pinecone: $45-80 | Chroma: N/A
additional property: Maximum Vectors Supported
  type: PropertyValue
  value: Pinecone: 5 billion (enterprise) | Chroma: N/A
additional property: Average Query Latency (p50)
  type: PropertyValue
  value: Pinecone: 45-120ms | Chroma: N/A
additional property: Setup Time (production-ready)
  type: PropertyValue
  value: Pinecone: 0.25 hours | Chroma: N/A
additional property: Native Integration Count
  type: PropertyValue
  value: Pinecone: 25+ (LangChain, LlamaIndex, OpenAI) | Chroma: N/A
additional property: Data Export Capability
  type: PropertyValue
  value: Pinecone: Limited; JSON export only, subject to egress costs | Chroma: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Pinecone: 3-5 minutes | Chroma: 0.1 days (2-4 hours)
  value reference: Chroma
additional property: Starting Cost (Annual)
  type: PropertyValue
  value: Pinecone: $50 (Starter tier minimum) | Chroma: $0 (free)
  value reference: Chroma
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Pinecone: 10B+ (unlimited) | Chroma: Limited to hardware (~1B)
  value reference: Pinecone
additional property: Query Latency (p95)
  type: PropertyValue
  value: Pinecone: <100ms global | Chroma: 50-200ms local
  value reference: Pinecone
additional property: Uptime Guarantee
  type: PropertyValue
  value: Pinecone: 99.95% | Chroma: No SLA
additional property: Documentation Quality Score
  type: PropertyValue
  value: Pinecone: 9/10 | Chroma: 8/10
  value reference: Pinecone
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Pinecone: Advanced (AND/OR/NOT) | Chroma: Basic ($where)
  value reference: Pinecone
additional property: Starting Monthly Cost
  type: PropertyValue
  value: Pinecone: $25 | Chroma: N/A
additional property: Maximum Query Throughput
  type: PropertyValue
  value: Pinecone: 5,000,000+ | Chroma: N/A
additional property: P99 Query Latency
  type: PropertyValue
  value: Pinecone: < 50ms | Chroma: N/A
additional property: Setup Time (first query)
  type: PropertyValue
  value: Pinecone: 15-30 | Chroma: 2-5
  value reference: Chroma
additional property: GitHub Stars (Community)
  type: PropertyValue
  value: Pinecone: Proprietary (not open-source) | Chroma: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/pinecone
significant link: https://www.aversusb.net/alternatives/pinecone
significant link: https://www.aversusb.net/entity/chroma
significant link: https://www.aversusb.net/alternatives/chroma
significant link: https://www.aversusb.net/api/answer/pinecone-vs-chroma)
significant link: https://www.aversusb.net/api/v1/schema/pinecone-vs-chroma)
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/chroma-vs-pinecone)
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 Pinecone and Chroma
genre: Software

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

A Versus B
type: Organization

Pinecone
type: SoftwareApplication
language: en-US
description: Managed cloud vector database for production AI/ML applications at scale.
image: ImageObject
  caption: Pinecone
subject of: Pinecone vs Chroma
image: ImageObject
  caption: Pinecone

Chroma
type: SoftwareApplication
language: en-US
description: Open-source, lightweight vector database for rapid prototyping and local development.
subject of: Pinecone vs Chroma

Pinecone vs Chroma — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Pinecone vs Chroma. Contains 107 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: Setup Time (Basic)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Initial Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Monthly Cost at 100M Vectors
  type: PropertyValue
  value reference: Pricing
variable measured: Supported Index Types
  type: PropertyValue
  value reference: Indexing
variable measured: Vector Store Integrations
  type: PropertyValue
  value reference: Features
variable measured: Query Latency (p50)
  type: PropertyValue
  value reference: Performance
variable measured: Free Tier Vector Capacity
  type: PropertyValue
  value reference: Limits
variable measured: Pricing Model
  type: PropertyValue
  value reference: Cost & Licensing
variable measured: Estimated Monthly Cost at 100GB
  type: PropertyValue
  value reference: Cost & Business
variable measured: Vector Dimension Limit
  type: PropertyValue
  value reference: Performance & Scalability
variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup
variable measured: GitHub Stars/Community Size
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Self-Hosting Available
  type: PropertyValue
  value reference: Deployment
variable measured: Metadata Filtering Complexity
  type: PropertyValue
  value reference: Features
variable measured: SLA Uptime Guarantee
  type: PropertyValue
  value reference: Reliability
variable measured: Minimum Setup Time
  type: PropertyValue
  value reference: Deployment
variable measured: Cost for 1M Monthly Read Operations
  type: PropertyValue
  value reference: Pricing
variable measured: Vector Dimensionality Support
  type: PropertyValue
  value reference: Features
variable measured: Uptime SLA Guarantee
  type: PropertyValue
  value reference: Reliability
variable measured: SQL Relational Query Integration
  type: PropertyValue
  value reference: Features
variable measured: GitHub Community Stars
  type: PropertyValue
  value reference: Community
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: Setup Time (Local Development)
  type: PropertyValue
  value reference: Ease of Use
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community Engagement
variable measured: Cost at 10M Vectors/Month
  type: PropertyValue
  value reference: Pricing
variable measured: Free Tier Vector Limit
  type: PropertyValue
  value reference: Pricing & Limits
variable measured: Estimated Monthly Cost (1M vectors)
  type: PropertyValue
  value reference: Pricing & Limits
variable measured: Native Hybrid Search Support
  type: PropertyValue
  value reference: Features
variable measured: Monthly Cost (1M vectors, 1K queries/day)
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Vectors Supported
  type: PropertyValue
  value reference: Scalability
variable measured: Average Query Latency (p50)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (production-ready)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Native Integration Count
  type: PropertyValue
  value reference: Ecosystem
variable measured: Data Export Capability
  type: PropertyValue
  value reference: Flexibility
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Implementation
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: Starting Monthly Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Maximum Query Throughput
  type: PropertyValue
  value reference: Performance
variable measured: P99 Query Latency
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (first query)
  type: PropertyValue
  value reference: Developer Experience
variable measured: GitHub Stars (Community)
  type: PropertyValue
  value reference: Community
variable measured: Code Customization
  type: PropertyValue
  value reference: Flexibility
variable measured: Free Tier Availability
  type: PropertyValue
  value reference: Pricing
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
  type: PropertyValue
  value reference: Performance
variable measured: REST API Support
  type: PropertyValue
  value reference: Integration
variable measured: RBAC & Enterprise Security
  type: PropertyValue
  value reference: Security
variable measured: Monthly Cost (1M vectors, 768 dims)
  type: PropertyValue
  value reference: Pricing
variable measured: Time to Production
  type: PropertyValue
  value reference: Ease of Use
variable measured: Deployment Options
variable measured: Hybrid Search Support
variable measured: API Compatibility
variable measured: Maximum Vectors Per Index
variable measured: Query Latency (p50, local/optimal)
variable measured: Monthly Base Cost (starter tier)
variable measured: Uptime SLA
variable measured: Supported Vector Dimensions
variable measured: LangChain Integration Native Support
is based on: https://www.aversusb.net/compare/pinecone-vs-chroma)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-07T09:23:27.197Z
author: A Versus B
publisher: A Versus B
is part of: pinecone-vs-chroma)
main entity: Which vector database should I use for my RAG application?
  type: Question
  accepted answer: Answer
    text: Use Pinecone if your RAG system ingests >1M documents and requires <100ms query latency in production. Use Chroma if you're prototyping, running locally, or have <10M vectors. Pinecone scales to enterprise datasets, while Chroma excels at rapid development and cost efficiency for smaller workloads.
    language: en-US
main entity: Can I migrate from Chroma to Pinecone later?
  type: Question
  accepted answer: Answer
    text: Yes, migration is relatively straightforward—both use standard vector embeddings and support bulk import via APIs. Chroma's open format makes exporting data easy, and Pinecone provides bulk upsert endpoints. Plan 2-4 hours of engineering time for seamless migration of 10M+ vectors.
    language: en-US
main entity: What's the total cost difference for 5M vectors over 12 months?
  type: Question
  accepted answer: Answer
    text: Chroma costs ~$0 (open-source, self-hosted infrastructure only). Pinecone costs approximately $1,500-3,000/year for 5M vectors depending on index size, API calls, and storage (estimated $0.25 per 100K stored vectors + compute fees). Cost break-even favors Chroma unless you value managed uptime and global infrastructure.
    language: en-US
main entity: How do query speeds compare between Pinecone and Chroma?
  type: Question
  accepted answer: Answer
    text: Chroma achieves 5-20ms latency for local queries due to in-memory operations. Pinecone achieves 50-100ms p50 latency from global cloud infrastructure (includes network overhead). For latency-critical applications <50ms requirement, Chroma's local deployment wins; Pinecone trades latency for scalability and reliability.
    language: en-US
main entity: Is Chroma production-ready without managed infrastructure?
  type: Question
  accepted answer: Answer
    text: Chroma can run in production on your own infrastructure (Kubernetes, Docker, etc.), but you assume operational burden—monitoring, backups, scaling, uptime. Pinecone is production-ready out-of-the-box with enterprise SLA. For mission-critical applications, Pinecone's managed service reduces operational risk.
    language: en-US

How to Choose Between Pinecone and Chroma
type: HowTo
description: Pinecone is a managed cloud vector database optimized for production-scale applications with 1M+ vectors, while Chroma is a lightweight, open-source vector database designed for rapid prototyping and local development with simpler deployment.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Scalability Limit
step: Consider: Setup Time
step: Consider: Cost Model
step: Consider: Production Readiness
step: Consider: Query Latency (p50)
step: Consider: Learning Curve
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Pinecone if you're building production applications requiring sub-100ms latency, supporting 1B+ vectors, and need enterprise-grade reliability with managed infrastructure. Choose Chroma if you're prototyping, running locally, prioritize cost savings, or work with small-to-medium datasets (<10M vectors) where development speed matters more than managed scalability.

Consider: Deployment Model
type: HowToStep
position: 1
text: Deployment Model: Pinecone has Managed cloud service (SaaS), while Chroma has Open-source, self-hosted or lightweight cloud.

Consider: Scalability Limit
type: HowToStep
position: 2
text: Scalability Limit: Pinecone has 100B+ vectors per index, while Chroma has Up to ~10M vectors efficiently.

Consider: Setup Time
type: HowToStep
position: 3
text: Setup Time: Pinecone has 15-30 minutes (API keys, cloud config), while Chroma has 2-5 minutes (pip install locally).

Consider: Cost Model
type: HowToStep
position: 4
text: Cost Model: Pinecone has $0.25 per 100K vectors stored + compute, while Chroma has Free open-source, hosting costs if cloud.

Consider: Production Readiness
type: HowToStep
position: 5
text: Production Readiness: Pinecone has 99.95% uptime SLA, enterprise support, while Chroma has Community support, no SLA.

Consider: Query Latency (p50)
type: HowToStep
position: 6
text: Query Latency (p50): Pinecone has 50-100ms (global infrastructure), while Chroma has 5-20ms (local) to 200ms+ (cloud).

Consider: Learning Curve
type: HowToStep
position: 7
text: Learning Curve: Pinecone has Moderate (REST API, Python SDK), while Chroma has Minimal (simple Python syntax).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Pinecone vs Chroma
review body: Pinecone is a managed cloud vector database optimized for production-scale applications with 1M+ vectors, while Chroma is a lightweight, open-source vector database designed for rapid prototyping and local development with simpler deployment.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Pinecone vs Chroma

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Maximum Vectors Per Index
type: PropertyValue
value: Pinecone: 100 billion | Chroma: ~10 million
value reference: Pinecone

Query Latency (p50, local/optimal)
type: PropertyValue
value: Pinecone: 50-100ms | Chroma: 5-20ms
value reference: Chroma

Monthly Base Cost (starter tier)
type: PropertyValue
value: Pinecone: $25-50 | Chroma: $0 (open-source)
value reference: Chroma

Uptime SLA
type: PropertyValue
value: Pinecone: 99.95% | Chroma: Community-dependent (no SLA)

Supported Vector Dimensions
type: PropertyValue
value: Pinecone: Up to 20,000 | Chroma: Unlimited

LangChain Integration Native Support
type: PropertyValue
value: Pinecone: Yes, official integration | Chroma: Yes, official integration

Maximum Vector Scale
type: PropertyValue
value: Pinecone: N/A | Chroma: ~10 million efficiently

Query Latency (1M vectors)
type: PropertyValue
value: Pinecone: N/A | Chroma: 50-200ms

Embedded Tokenizer Support
type: PropertyValue
value: Pinecone: N/A | Chroma: Yes (6+ models included)

Metadata Filtering Support
type: PropertyValue
value: Pinecone: N/A | Chroma: Native (boolean operators)

GPU Support
type: PropertyValue
value: Pinecone: N/A | Chroma: Experimental/Limited

Memory Usage (10M vectors)
type: PropertyValue
value: Pinecone: N/A | Chroma: 3-5 GB

Query Latency (1M vectors, single query)
type: PropertyValue
value: Pinecone: N/A | Chroma: 150-300ms

Maximum Practical Dataset Size
type: PropertyValue
value: Pinecone: N/A | Chroma: ~10 million

Data Connectors
type: PropertyValue
value: Pinecone: N/A | Chroma: 0 (manual)

Setup Time
type: PropertyValue
value: Pinecone: N/A | Chroma: 5

LLM Provider Support
type: PropertyValue
value: Pinecone: N/A | Chroma: External (0 native)

Minimum Deployment Size
type: PropertyValue
value: Pinecone: N/A | Chroma: 50

Retrieval Strategy Types
type: PropertyValue
value: Pinecone: N/A | Chroma: 1 (similarity search)

Storage Backends
type: PropertyValue
value: Pinecone: N/A | Chroma: 3 (in-memory, SQLite, cloud)

Production Observability
type: PropertyValue
value: Pinecone: N/A | Chroma: Basic logging

Query Latency (1M vectors, 768-dim, 10th percentile)
type: PropertyValue
value: Pinecone: N/A | Chroma: ~50ms

Built-in Embedding Generation
type: PropertyValue
value: Pinecone: N/A | Chroma: Yes (OpenAI, HuggingFace, Ollama)

Installation Complexity
type: PropertyValue
value: Pinecone: N/A | Chroma: 5-10 minutes (Python package)

SQL Filtering Capability
type: PropertyValue
value: Pinecone: N/A | Chroma: JSON metadata filters (limited)

Open-Source License
type: PropertyValue
value: Pinecone: N/A | Chroma: Apache 2.0 (fully open)

GitHub Stars (as of 2026)
type: PropertyValue
value: Pinecone: N/A | Chroma: ~14,000

Memory Footprint (at rest, 1M vectors)
type: PropertyValue
value: Pinecone: N/A | Chroma: ~800MB

Number of Supported Languages
type: PropertyValue
value: Pinecone: N/A | Chroma: Python + JavaScript

Kubernetes-Native Deployment
type: PropertyValue
value: Pinecone: N/A | Chroma: Not recommended; in-process only

Complex Metadata Filtering Support
type: PropertyValue
value: Pinecone: N/A | Chroma: Basic equality/contains only

Maximum Vectors Per Instance
type: PropertyValue
value: Pinecone: N/A | Chroma: ~10M

Average Query Latency
type: PropertyValue
value: Pinecone: N/A | Chroma: 10-50ms

Setup Time to First Query
type: PropertyValue
value: Pinecone: N/A | Chroma: 2-5 (pip install)

Hybrid Search Support (BM25 + Vector)
type: PropertyValue
value: Pinecone: N/A | Chroma: No

Multi-tenancy Support
type: PropertyValue
value: Pinecone: N/A | Chroma: Not supported

Minimum Memory for 1M Vectors
type: PropertyValue
value: Pinecone: N/A | Chroma: 1-2GB

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

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

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

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

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

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

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

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

Pinecone — Wikipedia
type: CreativeWork

Chroma — Wikipedia
type: CreativeWork

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

Deployment Options
type: PropertyValue
value reference: Architecture

Hybrid Search Support
type: PropertyValue
value reference: Features

API Compatibility
type: PropertyValue
value reference: Integration

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

Uptime SLA
type: PropertyValue
value reference: Reliability

Supported Vector Dimensions
type: PropertyValue
value reference: Capability

LangChain Integration Native Support
type: PropertyValue
value reference: Integrations

Maximum Vector Scale
type: PropertyValue
value reference: Scalability

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

Embedded Tokenizer Support
type: PropertyValue
value reference: Features

Metadata Filtering Support
type: PropertyValue
value reference: Features

GPU Support
type: PropertyValue
value reference: Hardware

Memory Usage (10M vectors)
type: PropertyValue
value reference: Resource Usage

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

Maximum Practical Dataset Size
type: PropertyValue
value reference: Scalability

Data Connectors
type: PropertyValue
value reference: Features

Setup Time
type: PropertyValue
value reference: Ease of Use

LLM Provider Support
type: PropertyValue
value reference: Integration

Minimum Deployment Size
type: PropertyValue
value reference: Performance

Retrieval Strategy Types
type: PropertyValue
value reference: Features

Storage Backends
type: PropertyValue
value reference: Features

Production Observability
type: PropertyValue
value reference: Enterprise

Query Latency (1M vectors, 768-dim, 10th percentile)
type: PropertyValue
value reference: Performance

Built-in Embedding Generation
type: PropertyValue
value reference: Features

Installation Complexity
type: PropertyValue
value reference: Setup

SQL Filtering Capability
type: PropertyValue
value reference: Querying

Open-Source License
type: PropertyValue
value reference: Licensing

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

Memory Footprint (at rest, 1M vectors)
type: PropertyValue
value reference: Resource Efficiency

Number of Supported Languages
type: PropertyValue
value reference: Compatibility

Kubernetes-Native Deployment
type: PropertyValue
value reference: Enterprise

Complex Metadata Filtering Support
type: PropertyValue
value reference: Functionality

Maximum Vectors Per Instance
type: PropertyValue
value reference: Scalability

Average Query Latency
type: PropertyValue
value reference: Performance

Setup Time to First Query
type: PropertyValue
value reference: Ease of Use

Hybrid Search Support (BM25 + Vector)
type: PropertyValue
value reference: Features

Multi-tenancy Support
type: PropertyValue
value reference: Architecture

Minimum Memory for 1M Vectors
type: PropertyValue
value reference: Resource Requirements

Supported Deployment Modes
type: PropertyValue
value reference: Deployment

Max Recommended Vector Count
type: PropertyValue
value reference: Scalability

Query Filtering Support
type: PropertyValue
value reference: Features

Multi-Modal Search
type: PropertyValue
value reference: Features

Kubernetes Support
type: PropertyValue
value reference: Container Orchestration

Minimum Setup Infrastructure
type: PropertyValue
value reference: Deployment

LangChain Integration Maturity
type: PropertyValue
value reference: Ecosystem