Pinecone vs pgvector
type: Article
description: Pinecone is a fully managed cloud vector database service requiring no infrastructure setup, while pgvector is an open-source PostgreSQL extension that requires self-hosting and maintenance. Pinecone offers higher operational convenience at a cost, whereas pgvector provides lower costs with more control but demands technical expertise.
language: en-US
date published: 2026-07-09T15:36:06.998Z
date created: 2026-07-09T15:36:06.998Z
date modified: 2026-07-09T15:36:07.053Z
content reference time: 2026-07-09T15:36:07.053Z
author: A Versus B
publisher: A Versus B
about: Pinecone
about: pgvector
abstract: Pinecone is a fully managed cloud vector database service requiring no infrastructure setup, while pgvector is an open-source PostgreSQL extension that requires self-hosting and maintenance. Pinecone offers higher operational convenience at a cost, whereas pgvector provides lower costs with more control but demands technical expertise.
text: Deployment Model. Setup & Infrastructure. Monthly Cost (1M vectors). Scalability. Query Latency (p99). Maintenance Required. Supported Vector Dimensions
conclusion text: Choose Pinecone if you prioritize ease of use, automatic scaling, and enterprise support for production applications where infrastructure management overhead is a concern. Choose pgvector if you have cost constraints, existing PostgreSQL infrastructure, or prefer open-source solutions with full control over your data and deployment.
additional property: Setup Time (Basic)
  type: PropertyValue
  value: Pinecone: 5-10 | pgvector: N/A
additional property: Initial Cost
  type: PropertyValue
  value: Pinecone: $0 (free tier limited to 1M vectors) | pgvector: N/A
additional property: Monthly Cost at 100M Vectors
  type: PropertyValue
  value: Pinecone: $400-600 | pgvector: N/A
additional property: Supported Index Types
  type: PropertyValue
  value: Pinecone: 3 (pod, serverless, custom) | pgvector: 2 (HNSW, IVFFlat)
  value reference: Pinecone
additional property: Vector Store Integrations
  type: PropertyValue
  value: Pinecone: 0 (standalone database) | pgvector: N/A
additional property: Query Latency (p50)
  type: PropertyValue
  value: Pinecone: 50-80 | pgvector: N/A
additional property: Free Tier Vector Capacity
  type: PropertyValue
  value: Pinecone: 1 | pgvector: N/A
additional property: Pricing Model
  type: PropertyValue
  value: Pinecone: Pay-per-usage (storage + queries) | pgvector: N/A
additional property: Estimated Monthly Cost at 100GB
  type: PropertyValue
  value: Pinecone: $200-400 (managed pricing) | pgvector: N/A
additional property: Vector Dimension Limit
  type: PropertyValue
  value: Pinecone: Unlimited | pgvector: N/A
additional property: Time to First Query
  type: PropertyValue
  value: Pinecone: 5-10 minutes | pgvector: 45-120 minutes
  value reference: Pinecone
additional property: GitHub Stars/Community Size
  type: PropertyValue
  value: Pinecone: ~2,500 stars | pgvector: N/A
additional property: Self-Hosting Available
  type: PropertyValue
  value: Pinecone: No (SaaS only) | pgvector: N/A
additional property: Metadata Filtering Complexity
  type: PropertyValue
  value: Pinecone: Boolean operators, ranges, sparse-dense hybrid | pgvector: N/A
additional property: Minimum Setup Time
  type: PropertyValue
  value: Pinecone: 15-30 minutes | pgvector: 120-300 minutes
  value reference: Pinecone
additional property: Cost for 1M Monthly Read Operations
  type: PropertyValue
  value: Pinecone: $0.40-1.25 | pgvector: $0 (self-hosted only)
  value reference: pgvector
additional property: Vector Dimensionality Support
  type: PropertyValue
  value: Pinecone: Up to 20,000 dimensions | pgvector: Up to 2,000 dimensions
  value reference: Pinecone
additional property: Uptime SLA Guarantee
  type: PropertyValue
  value: Pinecone: 99.99% | pgvector: User dependent (no SLA)
additional property: SQL Relational Query Integration
  type: PropertyValue
  value: Pinecone: No (separate system) | pgvector: Yes (unified via SQL)
additional property: GitHub Community Stars
  type: PropertyValue
  value: Pinecone: ~2,500 (closed-source) | pgvector: 4,200+ stars
  value reference: pgvector
additional property: Monthly Starting Cost
  type: PropertyValue
  value: Pinecone: $70 (minimum pod + index) | pgvector: N/A
additional property: Maximum Vector Storage
  type: PropertyValue
  value: Pinecone: 100M+ (unlimited with multi-pod) | pgvector: N/A
additional property: Maximum Vector Dimensions
  type: PropertyValue
  value: Pinecone: 20,000 | pgvector: 2,000
  value reference: Pinecone
additional property: Query Latency (p99)
  type: PropertyValue
  value: Pinecone: 50-100ms | pgvector: 100-500ms
  value reference: Pinecone
additional property: Setup Time (Local Development)
  type: PropertyValue
  value: Pinecone: 15-20 (account + API key setup) | pgvector: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: Pinecone: Not public (proprietary) | pgvector: ~10,800
additional property: Cost at 10M Vectors/Month
  type: PropertyValue
  value: Pinecone: ~$150-200 (pod + index + compute) | pgvector: N/A
additional property: Free Tier Vector Limit
  type: PropertyValue
  value: Pinecone: 100,000 vectors | pgvector: N/A
additional property: Estimated Monthly Cost (1M vectors)
  type: PropertyValue
  value: Pinecone: $10 + storage | pgvector: N/A
additional property: Native Hybrid Search Support
  type: PropertyValue
  value: Pinecone: Metadata filtering only | pgvector: N/A
additional property: Monthly Cost (1M vectors, 1K queries/day)
  type: PropertyValue
  value: Pinecone: $45-80 | pgvector: N/A
additional property: Maximum Vectors Supported
  type: PropertyValue
  value: Pinecone: 5 billion (enterprise) | pgvector: N/A
additional property: Average Query Latency (p50)
  type: PropertyValue
  value: Pinecone: 45-120ms | pgvector: N/A
additional property: Setup Time (production-ready)
  type: PropertyValue
  value: Pinecone: 0.25 hours | pgvector: N/A
additional property: Native Integration Count
  type: PropertyValue
  value: Pinecone: 25+ (LangChain, LlamaIndex, OpenAI) | pgvector: N/A
additional property: Data Export Capability
  type: PropertyValue
  value: Pinecone: Limited; JSON export only, subject to egress costs | pgvector: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Pinecone: 3-5 minutes | pgvector: 1-4 hours
  value reference: pgvector
additional property: Starting Cost (Annual)
  type: PropertyValue
  value: Pinecone: $50 (Starter tier minimum) | pgvector: N/A
additional property: Maximum Vectors at Scale
  type: PropertyValue
  value: Pinecone: 10B+ (unlimited) | pgvector: N/A
additional property: Query Latency (P95)
  type: PropertyValue
  value: Pinecone: <100ms global | pgvector: N/A
additional property: Uptime Guarantee
  type: PropertyValue
  value: Pinecone: 99.95% | pgvector: N/A
additional property: Documentation Quality Score
  type: PropertyValue
  value: Pinecone: 9/10 | pgvector: N/A
additional property: Metadata Filter Complexity
  type: PropertyValue
  value: Pinecone: Advanced (AND/OR/NOT) | pgvector: N/A
additional property: Free Tier Capacity
  type: PropertyValue
  value: Pinecone: 100,000 free vectors | pgvector: Unlimited (self-hosted)
additional property: Production Starter Cost
  type: PropertyValue
  value: Pinecone: $70 | pgvector: $0 (infra only)
  value reference: pgvector
additional property: Average Query Latency (P99)
  type: PropertyValue
  value: Pinecone: 50-100ms | pgvector: 100-300ms
  value reference: Pinecone
additional property: Setup to Production Time
  type: PropertyValue
  value: Pinecone: 0.5 | pgvector: 2-4
  value reference: Pinecone
additional property: Starting Monthly Cost
  type: PropertyValue
  value: Pinecone: $10 minimum | pgvector: N/A
additional property: Maximum Query Throughput
  type: PropertyValue
  value: Pinecone: 5,000,000+ | pgvector: 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/pgvector
significant link: https://www.aversusb.net/alternatives/pgvector
significant link: https://www.aversusb.net/api/answer/pinecone-vs-pgvector))
significant link: https://www.aversusb.net/api/v1/schema/pinecone-vs-pgvector))
related link: https://www.aversusb.net/compare/pinecone-vs-pgvector
related link: https://www.aversusb.net/compare/pinecone-vs-pgvector)
related link: https://www.aversusb.net/compare/llamaindex-vs-pinecone
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
related link: https://www.aversusb.net/compare/chroma-vs-pinecone
related link: https://www.aversusb.net/compare/weaviate-vs-pgvector
teaches: How to choose between Pinecone and pgvector
genre: Software

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

A Versus B
type: Organization

Pinecone
type: SoftwareApplication
language: en-US
description: Fully managed cloud vector database with enterprise features and 99.95% uptime SLA.
image: ImageObject
  caption: Pinecone
subject of: Pinecone vs pgvector
image: ImageObject
  caption: Pinecone

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

Pinecone vs pgvector — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Pinecone vs pgvector. Contains 128 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: Features
variable measured: Vector Store Integrations
  type: PropertyValue
  value reference: RAG Support
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 Speed
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: Minimum Setup Time
  type: PropertyValue
  value reference: Ease of Use
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: Capability
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
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: Features
variable measured: Data Export Capability
  type: PropertyValue
  value reference: Flexibility
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Deployment
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
variable measured: Documentation Quality Score
  type: PropertyValue
  value reference: Developer Experience
variable measured: Metadata Filter Complexity
  type: PropertyValue
  value reference: Features
variable measured: Free Tier Capacity
  type: PropertyValue
  value reference: Limits
variable measured: Production Starter Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Average Query Latency (P99)
  type: PropertyValue
  value reference: Performance
variable measured: Setup to Production Time
  type: PropertyValue
  value reference: Operations
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: 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: Implementation
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: Performance & Scale
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)
variable measured: Time to Production
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: Supported Vector Dimensions
is based on: https://www.aversusb.net/compare/pinecone-vs-pgvector))

Pinecone vs pgvector — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Pinecone vs pgvector.
has defined term: Ease of Use
has defined term: Pricing
has defined term: Features
has defined term: RAG Support
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 Speed
has defined term: Community & Adoption
has defined term: Deployment
has defined term: Reliability
has defined term: Community
has defined term: Scalability
has defined term: Capability
has defined term: Pricing & Limits
has defined term: Flexibility
has defined term: Developer Experience
has defined term: Operations
has defined term: Implementation
has defined term: Performance & Scale
has defined term: Integration
has defined term: Security
has defined term: Infrastructure
has defined term: Integrations
has defined term: Setup
has defined term: Licensing
has defined term: Adoption
has defined term: Resource Usage
has defined term: Architecture
has defined term: AI Features
has defined term: Cost
has defined term: Enterprise Features
has defined term: Querying
has defined term: Capabilities
has defined term: Operational Ease
has defined term: API
has defined term: Feature

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

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

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

RAG Support
type: DefinedTerm
term code: rag-support
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

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

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

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

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

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

Setup Speed
type: DefinedTerm
term code: setup-speed
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

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

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

Performance & Scale
type: DefinedTerm
term code: performance-&-scale
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

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

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

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

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

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

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

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

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

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

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

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

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

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

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

Operational Ease
type: DefinedTerm
term code: operational-ease
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

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

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

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T15:36:07.053Z
author: A Versus B
publisher: A Versus B
is part of: pinecone-vs-pgvector))
main entity: Which is better for cost-sensitive projects?
  type: Question
  accepted answer: Answer
    text: pgvector is significantly more cost-effective, costing $10-50/month for infrastructure versus Pinecone's $150-300/month for comparable vector volumes. However, pgvector requires operational overhead and engineering time for maintenance, which can offset cost savings in small teams.
    language: en-US
main entity: Can pgvector handle enterprise-scale deployments?
  type: Question
  accepted answer: Answer
    text: pgvector can handle up to ~100M vectors reliably in a single instance before performance degrades. Beyond that, you need to implement custom sharding strategies. Pinecone is purpose-built for billions of vectors with automatic sharding, making it the enterprise default.
    language: en-US
main entity: Is pgvector suitable for production applications?
  type: Question
  accepted answer: Answer
    text: Yes, pgvector is production-ready and used by companies like OpenAI and Anthropic internally. However, you're responsible for high availability, backups, and disaster recovery. Pinecone includes these features automatically with a 99.99% SLA.
    language: en-US
main entity: What are the main technical differences?
  type: Question
  accepted answer: Answer
    text: Pinecone uses a proprietary optimized HNSW variant optimized for cloud scale, supports up to 20,000 vector dimensions, and includes native sparse-dense hybrid search. pgvector offers HNSW and IVFFlat options, maxes out at ~2,000 dimensions, and relies on PostgreSQL's SQL engine for complex queries.
    language: en-US
main entity: Can I migrate from pgvector to Pinecone later?
  type: Question
  accepted answer: Answer
    text: Yes, migration is straightforward: export vectors and metadata from PostgreSQL, transform to Pinecone's JSON format, and bulk ingest via their API. Most teams accomplish this in 1-2 days. The reverse (Pinecone to pgvector) is similarly simple.
    language: en-US

How to Choose Between Pinecone and pgvector
type: HowTo
description: Pinecone is a fully managed cloud vector database service requiring no infrastructure setup, while pgvector is an open-source PostgreSQL extension that requires self-hosting and maintenance. Pinecone offers higher operational convenience at a cost, whereas pgvector provides lower costs with more control but demands technical expertise.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Setup & Infrastructure
step: Consider: Monthly Cost (1M vectors)
step: Consider: Scalability
step: Consider: Query Latency (p99)
step: Consider: Maintenance Required
step: Consider: Supported Vector Dimensions
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Pinecone if you prioritize ease of use, automatic scaling, and enterprise support for production applications where infrastructure management overhead is a concern. Choose pgvector if you have cost constraints, existing PostgreSQL infrastructure, or prefer open-source solutions with full control over your data and deployment.

Consider: Deployment Model
type: HowToStep
position: 1
text: Deployment Model: Pinecone has Fully managed cloud service (SaaS), while pgvector has Self-hosted PostgreSQL extension.

Consider: Setup & Infrastructure
type: HowToStep
position: 2
text: Setup & Infrastructure: Pinecone has Zero infrastructure needed, instant deployment, while pgvector has Requires PostgreSQL instance + manual configuration.

Consider: Monthly Cost (1M vectors)
type: HowToStep
position: 3
text: Monthly Cost (1M vectors): Pinecone has $150-300/month, while pgvector has $10-50/month (RDS) + operational overhead.

Consider: Scalability
type: HowToStep
position: 4
text: Scalability: Pinecone has Auto-scaling, handles billions of vectors, while pgvector has Limited by single PostgreSQL instance unless sharded.

Consider: Query Latency (p99)
type: HowToStep
position: 5
text: Query Latency (p99): Pinecone has 50-100ms, while pgvector has 100-500ms (depending on index size).

Consider: Maintenance Required
type: HowToStep
position: 6
text: Maintenance Required: Pinecone has Fully managed by Pinecone, while pgvector has User responsible for updates, backups, tuning.

Consider: Supported Vector Dimensions
type: HowToStep
position: 7
text: Supported Vector Dimensions: Pinecone has Up to 20,000 dimensions, while pgvector has Up to 2,000 dimensions (practical limit).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Pinecone vs pgvector
review body: Pinecone is a fully managed cloud vector database service requiring no infrastructure setup, while pgvector is an open-source PostgreSQL extension that requires self-hosting and maintenance. Pinecone offers higher operational convenience at a cost, whereas pgvector provides lower costs with more control but demands technical expertise.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Pinecone vs pgvector

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

Setup Time (first query)
type: PropertyValue
value: Pinecone: 15-30 | pgvector: N/A

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

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

Initial Setup Time
type: PropertyValue
value: Pinecone: 10 minutes | pgvector: N/A

Minimum Monthly Cost
type: PropertyValue
value: Pinecone: $0 (free tier with limits) | pgvector: N/A

Production Plan Cost
type: PropertyValue
value: Pinecone: $84 (Pro plan, 5M vectors) | pgvector: N/A

Maximum Vector Capacity
type: PropertyValue
value: Pinecone: 1B+ (distributed) | pgvector: <1 billion (practical limit)
value reference: Pinecone

Query Latency (p99) at 100M Vectors
type: PropertyValue
value: Pinecone: < 100ms | pgvector: N/A

REST API Support
type: PropertyValue
value: Pinecone: Yes (REST + gRPC) | pgvector: N/A

RBAC & Enterprise Security
type: PropertyValue
value: Pinecone: Yes (SOC 2 Type II, HIPAA) | pgvector: N/A

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

Time to Production
type: PropertyValue
value: Pinecone: 15-30 minutes | pgvector: N/A

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

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

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

Maximum Vectors Per Index
type: PropertyValue
value: Pinecone: 100 billion | pgvector: N/A

Query Latency (p50, local/optimal)
type: PropertyValue
value: Pinecone: 50-100ms | pgvector: N/A

Monthly Base Cost (starter tier)
type: PropertyValue
value: Pinecone: $25-50 | pgvector: N/A

Supported Vector Dimensions
type: PropertyValue
value: Pinecone: Up to 20,000 | pgvector: N/A

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

Free Tier Storage
type: PropertyValue
value: Pinecone: 1M vectors | pgvector: N/A

Production Monthly Cost (Baseline)
type: PropertyValue
value: Pinecone: $1,500-3,000 | pgvector: N/A

Setup Complexity (1-10 scale)
type: PropertyValue
value: Pinecone: 2/10 | pgvector: N/A

API SDKs Available
type: PropertyValue
value: Pinecone: 6+ languages (Python, Node.js, Go, Java, Rust, gRPC) | pgvector: N/A

Enterprise Security Compliance
type: PropertyValue
value: Pinecone: SOC 2 Type II, HIPAA-ready, GDPR compliant | pgvector: N/A

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

Max Vector Dimensions Supported
type: PropertyValue
value: Pinecone: 10K dimensions | pgvector: N/A

Hybrid Search Capability
type: PropertyValue
value: Pinecone: Yes (sparse-dense vectors) | pgvector: N/A

Time to Production Deployment
type: PropertyValue
value: Pinecone: 2-4 hours | pgvector: N/A

Open-Source
type: PropertyValue
value: Pinecone: No | pgvector: N/A

p50 Query Latency (Global)
type: PropertyValue
value: Pinecone: 25ms | pgvector: N/A

Storage Cost (1M vectors, 1536-dim)
type: PropertyValue
value: Pinecone: $50-150 | pgvector: N/A

Supported Programming Languages
type: PropertyValue
value: Pinecone: Python, JavaScript, Go, Java, REST API | pgvector: N/A

Setup Time
type: PropertyValue
value: Pinecone: <5 minutes | pgvector: 30-120 minutes
value reference: Pinecone

Cost for 1M Vectors/Month
type: PropertyValue
value: Pinecone: $150-300 | pgvector: $10-50
value reference: pgvector

Uptime SLA
type: PropertyValue
value: Pinecone: 99.95% | pgvector: Self-managed (varies)

Supported Indexing Algorithms
type: PropertyValue
value: Pinecone: Proprietary optimized (HNSW variant) | pgvector: HNSW, IVFFlat, Exact

Metadata Filtering Support
type: PropertyValue
value: Pinecone: Native, advanced filtering on metadata | pgvector: Limited (SQL WHERE clauses only)

Infrastructure Required
type: PropertyValue
value: Pinecone: None (fully managed) | pgvector: PostgreSQL instance (AWS RDS, self-hosted, etc.)

Minimum Monthly Cost (Production)
type: PropertyValue
value: Pinecone: $150-300 | pgvector: N/A

Setup Time to First Query
type: PropertyValue
value: Pinecone: 5-10 minutes | pgvector: 45 minutes
value reference: Pinecone

Maximum Recommended Vectors
type: PropertyValue
value: Pinecone: 100M+ | pgvector: N/A

Query Latency (1M vectors)
type: PropertyValue
value: Pinecone: 50-100ms | pgvector: N/A

Metadata Filter Operators
type: PropertyValue
value: Pinecone: 50+ | pgvector: N/A

GitHub Stars (Community)
type: PropertyValue
value: Pinecone: ~5,200 | pgvector: N/A

Memory Footprint (Installed)
type: PropertyValue
value: Pinecone: Cloud-managed | pgvector: N/A

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

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

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

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

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

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

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

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

Query Latency (1M vectors, 768-dim, 10th percentile)
type: PropertyValue
value: Pinecone: N/A | pgvector: ~120ms

Built-in Embedding Generation
type: PropertyValue
value: Pinecone: N/A | pgvector: No (external only)

Installation Complexity
type: PropertyValue
value: Pinecone: N/A | pgvector: Integrated (no new deployment)

SQL Filtering Capability
type: PropertyValue
value: Pinecone: N/A | pgvector: Full SQL WHERE clauses (unlimited)

Open Source License
type: PropertyValue
value: Pinecone: N/A | pgvector: PostgreSQL License (permissive)

GitHub Stars (as of 2026)
type: PropertyValue
value: Pinecone: N/A | pgvector: ~10,500

ACID Compliance
type: PropertyValue
value: Pinecone: N/A | pgvector: Yes (full support)

Deployment Complexity
type: PropertyValue
value: Pinecone: N/A | pgvector: 7/10

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

Multi-Tenancy Support
type: PropertyValue
value: Pinecone: N/A | pgvector: Requires schema/RLS workarounds

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

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

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

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

Single-Vector Search Latency (1M vectors)
type: PropertyValue
value: Pinecone: N/A | pgvector: 30-50ms

Maximum Supported Vector Dimensions
type: PropertyValue
value: Pinecone: N/A | pgvector: 2000+

Native SQL Support
type: PropertyValue
value: Pinecone: N/A | pgvector: Full SQL with vector operators

Embedding Auto-Generation
type: PropertyValue
value: Pinecone: N/A | pgvector: No (external preprocessing required)

Open Source Availability
type: PropertyValue
value: Pinecone: N/A | pgvector: Yes (PostgreSQL License)

Managed Cloud Cost (1M queries/month)
type: PropertyValue
value: Pinecone: N/A | pgvector: $20-80 (AWS RDS)

Relational Data Integration
type: PropertyValue
value: Pinecone: N/A | pgvector: Native (single database)

Average Latency (1M vectors)
type: PropertyValue
value: Pinecone: N/A | pgvector: 55ms

SQL Query Support
type: PropertyValue
value: Pinecone: N/A | pgvector: Yes (full SQL support)

GitHub Stars (2026)
type: PropertyValue
value: Pinecone: N/A | pgvector: 9,500+

SpeakableSpecification
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Pinecone — Wikipedia
type: CreativeWork

pgvector — Wikipedia
type: CreativeWork

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

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

Time to Production
type: PropertyValue
value reference: Deployment

Deployment Options
type: PropertyValue
value reference: Infrastructure

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

Supported Vector Dimensions
type: PropertyValue
value reference: Capability

LangChain Integration Native Support
type: PropertyValue
value reference: Integrations

Free Tier Storage
type: PropertyValue
value reference: Pricing

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

Setup Complexity (1-10 scale)
type: PropertyValue
value reference: Ease of Use

API SDKs Available
type: PropertyValue
value reference: Integration

Enterprise Security Compliance
type: PropertyValue
value reference: Security

SLA Uptime Guarantee
type: PropertyValue
value reference: Reliability

Max Vector Dimensions Supported
type: PropertyValue
value reference: Features

Hybrid Search Capability
type: PropertyValue
value reference: Features

Time to Production Deployment
type: PropertyValue
value reference: Setup

Open-Source
type: PropertyValue
value reference: Licensing

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

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

Supported Programming Languages
type: PropertyValue
value reference: Integration

Setup Time
type: PropertyValue
value reference: Implementation

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

Uptime SLA
type: PropertyValue
value reference: Reliability

Supported Indexing Algorithms
type: PropertyValue
value reference: Capability

Metadata Filtering Support
type: PropertyValue
value reference: Features

Infrastructure Required
type: PropertyValue
value reference: Operations

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

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

Maximum Recommended Vectors
type: PropertyValue
value reference: Scalability

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

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

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

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: Deployment

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

ACID Compliance
type: PropertyValue
value reference: Reliability

Deployment Complexity
type: PropertyValue
value reference: Operations

Vector Indexing Algorithm Options
type: PropertyValue
value reference: Performance

Multi-Tenancy Support
type: PropertyValue
value reference: Features

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

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

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

SQL Query Support
type: PropertyValue
value reference: Capability

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