Pinecone vs pgvector
type: Article
description: Pinecone is a managed vector database service with built-in scaling and simplified operations, while pgvector is a PostgreSQL extension offering self-hosted vector search within your existing database. Pinecone requires vendor lock-in and monthly costs, whereas pgvector provides lower operational overhead but demands more infrastructure management.
language: en-US
date published: 2026-07-07T22:37:41.772Z
date created: 2026-07-07T22:37:41.772Z
date modified: 2026-07-07T22:37:41.813Z
content reference time: 2026-07-07T22:37:41.813Z
author: A Versus B
publisher: A Versus B
about: Pinecone
about: pgvector
abstract: Pinecone is a managed vector database service with built-in scaling and simplified operations, while pgvector is a PostgreSQL extension offering self-hosted vector search within your existing database. Pinecone requires vendor lock-in and monthly costs, whereas pgvector provides lower operational overhead but demands more infrastructure management.
text: Deployment Model. Setup Time to Production. Starting Monthly Cost. Vector Dimensions Supported. Query Latency (avg). Multi-tenancy Support. Vendor Lock-in Risk
conclusion text: Choose Pinecone if you prioritize rapid deployment, minimal operational overhead, and built-in scaling for production AI applications where cost per query is acceptable. Choose pgvector if you want to avoid vendor lock-in, prefer lower total cost of ownership, already use PostgreSQL, and have infrastructure expertise to manage deployments.
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: Basic payload filtering | pgvector: N/A
additional property: SLA Uptime Guarantee
  type: PropertyValue
  value: Pinecone: 99.95% (enterprise tier) | 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+ (practical limit)
  value reference: Pinecone
additional property: Query Latency (p99)
  type: PropertyValue
  value: Pinecone: 50-100ms | pgvector: 50-500ms
  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: ~3,200 (closed source) | pgvector: ~10,800
  value reference: pgvector
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: Uptime SLA
  type: PropertyValue
  value: Pinecone: 99.9% | pgvector: Self-managed (variable)
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/llamaindex-vs-pinecone
related link: https://www.aversusb.net/compare/pinecone-vs-qdrant
related link: https://www.aversusb.net/compare/pinecone-vs-chroma
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: Managed serverless vector database with global scale and enterprise features
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 enabling vector similarity search in existing databases
subject of: Pinecone vs pgvector

Pinecone vs pgvector — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Pinecone vs pgvector. Contains 105 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: 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 & Deployment
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: Developer Experience
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: Features
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 Adoption
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: Free Tier Capacity
  type: PropertyValue
  value reference: Pricing
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: Uptime SLA
  type: PropertyValue
  value reference: Reliability
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: Accessibility
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: 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
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: Performance
has defined term: Limits
has defined term: Cost & Licensing
has defined term: Cost & Business
has defined term: Performance & Scalability
has defined term: Setup & Deployment
has defined term: Community & Adoption
has defined term: Deployment
has defined term: Reliability
has defined term: Developer Experience
has defined term: Community
has defined term: Scalability
has defined term: Community Adoption
has defined term: Pricing & Limits
has defined term: Ecosystem
has defined term: Flexibility
has defined term: Implementation
has defined term: Support
has defined term: Operations
has defined term: Accessibility
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: AI Features
has defined term: Cost
has defined term: Enterprise Features
has defined term: Setup & Onboarding
has defined term: Querying
has defined term: Licensing
has defined term: Capabilities
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

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 & Deployment
type: DefinedTerm
term code: setup-&-deployment
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

Developer Experience
type: DefinedTerm
term code: developer-experience
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

Community Adoption
type: DefinedTerm
term code: community-adoption
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

Ecosystem
type: DefinedTerm
term code: ecosystem
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

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

Support
type: DefinedTerm
term code: support
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

Accessibility
type: DefinedTerm
term code: accessibility
in defined term set: Pinecone vs pgvector — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
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

Architecture
type: DefinedTerm
term code: architecture
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

Integrations
type: DefinedTerm
term code: integrations
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

Setup & Onboarding
type: DefinedTerm
term code: setup-&-onboarding
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

Licensing
type: DefinedTerm
term code: licensing
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

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: 2026-07-07T22:37:41.813Z
author: A Versus B
publisher: A Versus B
is part of: pinecone-vs-pgvector)
main entity: Can I migrate from Pinecone to pgvector?
  type: Question
  accepted answer: Answer
    text: Yes, but with effort. You'll need to export vectors and metadata from Pinecone's API, transform them into PostgreSQL format, and re-index. pgvector uses standard SQL, so once data is loaded, applications can use standard PostgreSQL clients. The main challenge is updating application code from Pinecone's client library to PostgreSQL queries. Migration typically takes 2-4 weeks depending on dataset size.
    language: en-US
main entity: Which is better for production AI applications?
  type: Question
  accepted answer: Answer
    text: Pinecone excels for production systems requiring guaranteed uptime (99.9% SLA), sub-100ms latency at scale, and multi-tenant isolation. pgvector is suitable for production if you can manage infrastructure or use managed PostgreSQL services (AWS RDS, Google Cloud SQL). At 10M+ vectors, Pinecone's managed scaling becomes advantageous; below 1M vectors, pgvector's cost advantage dominates.
    language: en-US
main entity: What's the cost difference at scale?
  type: Question
  accepted answer: Answer
    text: At 10M vectors with 100k daily queries: Pinecone costs ~$500-1,000/month (index + queries). pgvector on managed PostgreSQL (AWS RDS) costs ~$200-400/month for equivalent compute, plus operational overhead. For 100M+ vectors, Pinecone's per-query model can exceed $5,000/month, while pgvector's PostgreSQL instance remains flat-rate, making pgvector 3-4x cheaper at massive scale.
    language: en-US
main entity: Does pgvector support real-time updates like Pinecone?
  type: Question
  accepted answer: Answer
    text: Yes, pgvector updates vectors in real-time via standard SQL INSERT/UPDATE commands. However, rebuilding HNSW indices (the faster algorithm) after updates requires offline maintenance. Pinecone handles index updates automatically in the background. For applications with frequent updates (>1k/second), Pinecone's automatic index management provides an operational advantage.
    language: en-US
main entity: Which has better documentation and community support?
  type: Question
  accepted answer: Answer
    text: Pinecone has comprehensive official documentation, dedicated support tiers, and active Discord community (~15k members). pgvector has excellent community support on GitHub, Stack Overflow, and PostgreSQL forums, but fewer official resources. Pinecone wins on structured support; pgvector wins on community problem-solving due to larger PostgreSQL ecosystem.
    language: en-US

How to Choose Between Pinecone and pgvector
type: HowTo
description: Pinecone is a managed vector database service with built-in scaling and simplified operations, while pgvector is a PostgreSQL extension offering self-hosted vector search within your existing database. Pinecone requires vendor lock-in and monthly costs, whereas pgvector provides lower operational overhead but demands more infrastructure management.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Setup Time to Production
step: Consider: Starting Monthly Cost
step: Consider: Vector Dimensions Supported
step: Consider: Query Latency (avg)
step: Consider: Multi-tenancy Support
step: Consider: Vendor Lock-in Risk
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Pinecone if you prioritize rapid deployment, minimal operational overhead, and built-in scaling for production AI applications where cost per query is acceptable. Choose pgvector if you want to avoid vendor lock-in, prefer lower total cost of ownership, already use PostgreSQL, and have infrastructure expertise to manage deployments.

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

Consider: Setup Time to Production
type: HowToStep
position: 2
text: Setup Time to Production: Pinecone has 15-30 minutes, while pgvector has 2-4 hours.

Consider: Starting Monthly Cost
type: HowToStep
position: 3
text: Starting Monthly Cost: Pinecone has $0 free tier, then $70-500+, while pgvector has $0 (open source).

Consider: Vector Dimensions Supported
type: HowToStep
position: 4
text: Vector Dimensions Supported: Pinecone has Up to 20,000 dimensions, while pgvector has Unlimited (hardware dependent).

Consider: Query Latency (avg)
type: HowToStep
position: 5
text: Query Latency (avg): Pinecone has 50-100ms (P99), while pgvector has 100-300ms (P99).

Consider: Multi-tenancy Support
type: HowToStep
position: 6
text: Multi-tenancy Support: Pinecone has Native namespaces, while pgvector has Row-level security required.

Consider: Vendor Lock-in Risk
type: HowToStep
position: 7
text: Vendor Lock-in Risk: Pinecone has High (proprietary API), while pgvector has Low (standard SQL).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Pinecone vs pgvector
review body: Pinecone is a managed vector database service with built-in scaling and simplified operations, while pgvector is a PostgreSQL extension offering self-hosted vector search within your existing database. Pinecone requires vendor lock-in and monthly costs, whereas pgvector provides lower operational overhead but demands more infrastructure management.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Pinecone vs pgvector

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

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

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

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

GitHub Stars (Community)
type: PropertyValue
value: Pinecone: Proprietary (not open-source) | 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: 1 GB | 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

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

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)

SpeakableSpecification
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Pinecone — Wikipedia
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pgvector — Wikipedia
type: CreativeWork

Compare Pinecone vs pgvector
type: Action
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object: Pinecone
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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

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: 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

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: Deployment

API SDKs Available
type: PropertyValue
value reference: Integration

Enterprise Security Compliance
type: PropertyValue
value reference: Security

Deployment Model
type: PropertyValue
value reference: Operations

Indexing Methods Supported
type: PropertyValue
value reference: Performance

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

Integrated LLM Providers
type: PropertyValue
value reference: AI Features

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

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

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

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

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 & Onboarding

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 Adoption

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: Operations

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: Licensing

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

Relational Data Integration
type: PropertyValue
value reference: Capability