Pinecone vs Weaviate
Pinecone
Managed cloud vector database with fast similarity search, advanced metadata filtering, and enterprise reliability.
Production AI applications, startups prioritizing speed-to-market, teams without DevOps resources, enterprise customers requiring SLA guarantees
Weaviate
Enterprise-ready, distributed vector database with GraphQL API, advanced filtering, and multi-modal search capabilities.
Teams with DevOps capability, organizations with strict data residency requirements, research projects requiring unlimited customization, applications heavily using hybrid search
Short Answer
Pinecone is a fully managed vector database with simpler setup and pay-as-you-go pricing, while Weaviate is an open-source alternative offering more control and flexibility at the cost of self-hosting complexity. Pinecone excels for production workloads at scale, whereas Weaviate suits teams prioritizing customization and cost control.
Our Verdict
AI-assistedChoose Pinecone if you need a production-ready vector database with minimal DevOps overhead, built-in scaling, and straightforward per-vector pricing for most business use cases. Choose Weaviate if you require fine-grained control, hybrid search capabilities, want to avoid managed service costs, or need to self-host due to data residency requirements.
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Choose Pinecone if
Production AI applications, startups prioritizing speed-to-market, teams without DevOps resources, enterprise customers requiring SLA guarantees
Choose Weaviate if
Teams with DevOps capability, organizations with strict data residency requirements, research projects requiring unlimited customization, applications heavily using hybrid search
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Key Differences at a Glance
Key Facts & Figures
| Metric | Pinecone | Weaviate | Diff |
|---|---|---|---|
| Setup Time (Basic)(minutes) | 5-10 | — | — |
| Initial Cost(USD) | $0 (free tier limited to 1M vectors) | — | — |
| Monthly Cost at 100M Vectors(USD) | $400-600 | — | — |
| Supported Index Types(count) | 1 (vector-only) | — | — |
| Vector Store Integrations(count) | 0 (standalone database) | — | — |
| Query Latency (p50)(milliseconds) | 50-80 | — | — |
| Free Tier Vector Capacity(millions of vectors) | 1 | — | — |
| Estimated Monthly Cost at 100GB(USD) | $200-400 (managed pricing) | — | — |
| Time to First Query(minutes) | 5-10 minutes | 30-45 minutes (self-hosted) | -82% |
| GitHub Stars/Community Size(stars) | ~2,500 stars | — | — |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | — | — |
| Maximum Vector Capacity(billion vectors) | 5+ billion | — | — |
| Minimum Setup Time(minutes) | 15-30 minutes | — | — |
| Cost for 1M Monthly Read Operations(USD) | $0.40-1.25 | — | — |
| Vector Dimensionality Support(maximum dimensions) | Up to 20,000 dimensions | — | — |
| Uptime SLA Guarantee(percent) | 99.99% | — | — |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | 13,000+ stars | -81% |
| Monthly Starting Cost(USD) | $70 (minimum pod + index) | — | — |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — | — |
| Maximum Vector Dimensions(dimensions) | 20,000 dimensions | Unlimited | — |
| Query Latency (p99)(milliseconds) | 50-100ms | 50-150ms | -25% |
| Uptime SLA(percent) | 99.99% | Not guaranteed (self-hosted) | — |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — | — |
| GitHub Stars | Not open-source | ~9,500 stars (as of 2026) | — |
| Cost at 10M Vectors/Month(USD) | ~$150-200 (pod + index + compute) | — | — |
| Free Tier Vector Limit(vectors) | 100,000 vectors | Unlimited (self-hosted) | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | $500-800 (managed) | -98% |
| Monthly Cost (1M vectors, 1K queries/day)(USD) | $45-80 | — | — |
| Maximum Vectors Supported(billions) | 5 billion (enterprise) | — | — |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — | — |
| Setup Time (production-ready)(hours) | 0.25 hours | — | — |
| Native Integration Count(frameworks) | 25+ (LangChain, LlamaIndex, OpenAI) | — | — |
| Setup Time to Production(days) | 3-5 minutes | — | — |
| Starting Cost (Annual)(USD) | $50 (Starter tier minimum) | — | — |
| Maximum Vectors at Scale(millions) | 10B+ (unlimited) | — | — |
| Query Latency (p95)(milliseconds) | <100ms global | — | — |
| Uptime Guarantee(percent) | 99.95% | — | — |
| Documentation Quality Score(out of 10) | 9/10 | — | — |
| Metadata Filter Complexity(operators supported) | Advanced (AND/OR/NOT) | — | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | 3 methods (HNSW, flat, dynamic) | — |
| Average Query Latency (1M vectors, 384-dim)(milliseconds) | 75ms | 75ms | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | $800 | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | 100+ | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | 2 (GraphQL, REST) | — |
| Query Throughput(operations per second (QPS)) | 100,000 QPS | 100,000 QPS | — |
| Maximum Collection Size(billion vectors) | 2 billion vectors | 2 billion vectors | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | 5-10 minutes (cloud) | — |
| Number of Native LLM Integrations(integrations) | 20+ LLM providers | 20+ LLM providers | — |
| Query Latency (95th percentile)(milliseconds) | 100-500 ms | 100-500 ms | — |
| Memory per 1M Vectors(GB) | 8-12 GB | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | 20-30 seconds | — |
| Built-in LLM Integrations(count) | 15+ providers | 15+ providers | — |
| Managed Cloud Base Price (monthly)(USD) | $25/month | $25/month | — |
| Throughput (vectors/second insert)(vectors/sec) | 5,000-10,000 | 5,000-10,000 | — |
| Maximum Vectors Per Instance(vectors) | 100M+ (distributed) | 100M+ (distributed) | — |
| Average Query Latency(milliseconds) | 50-150ms | 50-150ms | — |
| Setup Time to First Query(minutes) | 30-60 (with Docker) | 30-60 (with Docker) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | 4-8GB | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | 30-60 minutes | — |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | 100M+ (distributed) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Pinecone
Fully managed SaaS (serverless)
Weaviate
Open-source (self-hosted or managed)
Pinecone
$0 free tier, $1 per 100K vectors🏆
Weaviate
$0 open-source, $500+ for managed cloud
Pinecone
5-10 minutes to production🏆
Weaviate
30-60 minutes (self-hosted) or 15 minutes (managed)
Pinecone
Up to 20,000 dimensions
Weaviate
Unlimited dimensions🏆
Pinecone
Yes, built-in🏆
Weaviate
Limited in open-source, better in managed
Pinecone
Limited (metadata filtering only)
Weaviate
Native BM25 hybrid search🏆
Pinecone
~2,500 stars (closed-source core)
Weaviate
~11,000+ stars🏆
Full Comparison
| Attribute |  Pinecone | Weaviate |
|---|---|---|
| Setup Time (Basic)(minutes) | 5-10 | — |
| Minimum Setup Time(minutes) | 15-30 minutes | — |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — |
| Setup Time (production-ready)(hours) | 0.25 hours | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 5-10 minutes (cloud) | — |
Show 1 more attributeSetup Time to First Query(minutes) 30-60 (with Docker) — | ||
| Initial Cost(USD) | $0 (free tier limited to 1M vectors) | — |
| Monthly Cost at 100M Vectors(USD) | $400-600 | — |
| Cost for 1M Monthly Read Operations(USD) | $0.40-1.25 | — |
| Monthly Starting Cost(USD) | $70 (minimum pod + index) | — |
| Cost at 10M Vectors/Month(USD) | ~$150-200 (pod + index + compute) | — |
Show 3 more attributesMonthly Cost (1M vectors, 1K queries/day)(USD) $45-80 — Starting Cost (Annual)(USD) $50 (Starter tier minimum) — Managed Cloud Base Price (monthly)(USD) $25/month — | ||
| Supported Index Types(count) | 1 (vector-only) | — |
| Vector Store Integrations(count) | 0 (standalone database) | — |
| Metadata Filtering Complexity | Basic payload filtering | — |
| Vector Dimensionality Support(maximum dimensions) | Up to 20,000 dimensions | — |
| SQL Relational Query Integration(native support) | No (separate system) | — |
| Native Hybrid Search Support(null) | Metadata filtering only | BM25 keyword + vector |
Show 7 more attributesMetadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — Built-in Hybrid Search Support Native BM25 + vector search — Number of Native LLM Integrations(integrations) 20+ LLM providers — Hybrid Search Support (BM25 + Vector) Yes — Multi-tenancy Support Native with isolation — Query Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — | ||
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | 50-100ms | 50-150ms |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — |
| Query Latency (p95)(milliseconds) | <100ms global | — |
| Indexing Methods Supported(count) | 3 methods (HNSW, flat, dynamic) | — |
Show 6 more attributesAverage Query Latency (1M vectors, 384-dim)(milliseconds) 75ms — Query Throughput(operations per second (QPS)) 100,000 QPS — GPU Acceleration Support Limited (planning phase) — Query Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms — | ||
| Free Tier Vector Capacity(millions of vectors) | 1 | — |
| Pricing Model | Pay-per-usage (storage + queries) | — |
| Estimated Monthly Cost at 100GB(USD) | $200-400 (managed pricing) | — |
| Vector Dimension Limit(dimensions) | Unlimited | — |
| Time to First Query(minutes) | 5-10 minutes | 30-45 minutes (self-hosted) |
| GitHub Stars/Community Size(stars) | ~2,500 stars | — |
| Self-Hosting Available | No (SaaS only) | — |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Uptime SLA(percent) | 99.99% | Not guaranteed (self-hosted) |
| Uptime Guarantee(percent) | 99.95% | — |
| Maximum Vector Capacity(billion vectors) | 5+ billion | — |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — |
| Maximum Vectors Supported(billions) | 5 billion (enterprise) | — |
| Maximum Vectors at Scale(millions) | 10B+ (unlimited) | — |
| Maximum Scalability (distributed nodes)(nodes) | 100+ | — |
Show 3 more attributesMaximum Collection Size(billion vectors) 2 billion vectors — Maximum Vectors Per Instance(vectors) 100M+ (distributed) — Max Recommended Vector Count(vectors) 100M+ (distributed) — | ||
| GitHub Community Stars(stars) | ~2,500 (closed-source) | 13,000+ stars |
| Maximum Vector Dimensions(dimensions) | 20,000 dimensions | Unlimited |
| GitHub Stars | Not open-source | ~9,500 stars (as of 2026) |
| Free Tier Vector Limit(vectors) | 100,000 vectors | Unlimited (self-hosted) |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | $500-800 (managed) |
| Native Integration Count(frameworks) | 25+ (LangChain, LlamaIndex, OpenAI) | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | — |
| Setup Time to Production(days) | 3-5 minutes | — |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup Time (First Query)(minutes) | 30-60 minutes | — |
| Documentation Quality Score(out of 10) | 9/10 | — |
| Deployment Model | Cloud-managed SaaS + Self-hosted Docker/Kubernetes | — |
| Integrated LLM Providers(count) | 20+ providers (OpenAI, Anthropic, Cohere, Hugging Face) | — |
| Built-in LLM Integrations(count) | 15+ providers | — |
| Minimum Monthly Infrastructure Cost (Self-hosted Production)(USD) | $800 | — |
| Licensing Cost(USD) | $0-5000+/month (SaaS) | — |
| Native Multi-tenancy Support | Yes, with built-in tenant isolation | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Startup Time (empty instance)(seconds) | 20-30 seconds | — |
| Supported Deployment Modes | Docker, Kubernetes, Cloud (AWS/GCP/Azure) | — |
| Minimum Setup Infrastructure | Docker/Kubernetes cluster (4GB+ RAM minimum) | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
PineconeShow 1 more attribute
Show 3 more attributes
Show 7 more attributes
Show 6 more attributes
Show 3 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Pinecone
Pros
- Instant production setup with zero infrastructure management required
- Automatic scaling handles billions of vectors without configuration
- Native metadata filtering and sparse-dense hybrid search
- 99.99% uptime SLA with multi-region replication
- Integrated with 50+ LLM frameworks (LangChain, LlamaIndex, etc.)
Cons
- Pricing scales quickly for high-volume applications (can exceed $10K/month at 10B vectors)
- Closed-source limits customization for specialized ML requirements
- Limited to 20,000 vector dimensions (constraint for some research use cases)
Weaviate
Pros
- Full source code transparency enables deep customization and auditing
- Native BM25 keyword search combined with vector search in single query
- Supports unlimited vector dimensions for advanced ML models
- Generative module enables in-database LLM inference (RAG integration)
- Active open-source community with 11,000+ GitHub stars
Cons
- Self-hosted deployment requires Kubernetes expertise and ongoing maintenance overhead
- Managed cloud pricing ($500/month minimum) rivals Pinecone for most use cases
- Steeper learning curve with WDSL query language vs Pinecone's simpler REST API
Frequently Asked Questions
Pinecone is significantly cheaper for small projects—its free tier covers 100K vectors at no cost, and 1M vectors costs only ~$10/month. Weaviate's self-hosted option is free but requires infrastructure costs; its managed service starts at $500/month minimum, making it expensive for small scale.
Resources & Learn More
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Where to Buy
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