Pinecone vs Qdrant
Pinecone
Managed cloud vector database with fast similarity search, advanced metadata filtering, and enterprise reliability.
Startups, teams without DevOps expertise, enterprises prioritizing rapid deployment and simplicity over cost optimization.
Qdrant
High-performance, production-grade vector search engine written in Rust with enterprise-class reliability and scalability.
Teams with DevOps capabilities, enterprises with strict data residency requirements, cost-sensitive organizations scaling to millions of vectors.
Short Answer
Pinecone is a fully managed vector database with zero infrastructure overhead and pay-as-you-go pricing, while Qdrant is an open-source vector database offering complete control, self-hosting flexibility, and lower operational costs for teams with DevOps resources.
Our Verdict
AI-assistedChoose Pinecone if you prioritize speed-to-market, want zero infrastructure management, and don't mind vendor lock-in with pay-as-you-grow pricing. Choose Qdrant if you need cost control at scale, require self-hosting for compliance/privacy, or want the flexibility of open-source software with an optional managed tier.
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Choose Pinecone if
Startups, teams without DevOps expertise, enterprises prioritizing rapid deployment and simplicity over cost optimization.
Choose Qdrant if
Teams with DevOps capabilities, enterprises with strict data residency requirements, cost-sensitive organizations scaling to millions of vectors.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Pinecone | Qdrant | 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) | $25-100 (managed cloud) | +400% |
| Vector Dimension Limit(dimensions) | Unlimited | 65,536 | — |
| Time to First Query(minutes) | 5-10 minutes | 20 minutes | -65% |
| GitHub Stars/Community Size(stars) | ~2,500 stars | 18,000+ stars | -86% |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | Varies by self-hosted setup | — |
| 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) | — | — |
| Monthly Starting Cost(USD) | $70 (minimum pod + index) | — | — |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — | — |
| Maximum Vector Dimensions(dimensions) | 20,000 dimensions | — | — |
| Query Latency (p99)(milliseconds) | 50-100ms | — | — |
| Uptime SLA(percent) | 99.99% | — | — |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — | — |
| GitHub Stars | Not open-source | 28,000+ stars | — |
| Cost at 10M Vectors/Month(USD) | ~$150-200 (pod + index + compute) | — | — |
| Free Tier Vector Limit(vectors) | 100,000 vectors | — | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | — | — |
| 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) | — | — |
| Query Latency (95th percentile)(milliseconds) | 10-50 ms | 10-50 ms | — |
| Memory per 1M Vectors(GB) | 2-4 GB | 2-4 GB | — |
| Startup Time (empty instance)(seconds) | 2-5 seconds | 2-5 seconds | — |
| Built-in LLM Integrations(count) | 0 (custom only) | 0 (custom only) | — |
| Managed Cloud Base Price (monthly)(USD) | $10/month | $10/month | — |
| Throughput (vectors/second insert)(vectors/sec) | 50,000-100,000 | 50,000-100,000 | — |
| Query Latency (1M vectors, single query)(milliseconds) | 10-50ms | 10-50ms | — |
| Maximum Practical Dataset Size(vectors) | Billions+ | Billions+ | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | ~200MB | — |
| Number of Supported Languages(languages) | Python, JavaScript, Go, Java, Rust, C++, .NET | Python, JavaScript, Go, Java, Rust, C++, .NET | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Pinecone
Fully managed SaaS only
Qdrant
Open-source + managed cloud option🏆
Pinecone
$0 (starter), scales to $100+
Qdrant
$0 (self-hosted), $25+ (managed cloud)🏆
Pinecone
< 5 minutes🏆
Qdrant
5 minutes (managed) to 2-4 hours (self-hosted)
Pinecone
Unlimited🏆
Qdrant
Up to 65,536 dimensions
Pinecone
Basic filtering with payload support
Qdrant
Advanced filtering with complex queries🏆
Pinecone
~2,500 stars
Qdrant
~18,000+ stars🏆
Pinecone
Hosted on Pinecone servers
Qdrant
Full control with self-hosting option🏆
Full Comparison
| Attribute |  Pinecone | Qdrant |
|---|---|---|
| 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 | — |
| 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) $10/month — | ||
| Supported Index Types(count) | 1 (vector-only) | — |
| Vector Store Integrations(count) | 0 (standalone database) | — |
| Metadata Filtering Complexity | Basic payload filtering | Advanced boolean/range queries |
| 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 | — |
Show 1 more attributeMetadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — | ||
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | 50-100ms | — |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — |
| Query Latency (p95)(milliseconds) | <100ms global | — |
| Query Latency (95th percentile)(milliseconds) | 10-50 ms | — |
Show 2 more attributesThroughput (vectors/second insert)(vectors/sec) 50,000-100,000 — Query Latency (1M vectors, single query)(milliseconds) 10-50ms — | ||
| Free Tier Vector Capacity(millions of vectors) | 1 | — |
| Pricing Model | Pay-per-usage (storage + queries) | Self-hosted free or managed from $25/mo |
| Estimated Monthly Cost at 100GB(USD) | $200-400 (managed pricing) | $25-100 (managed cloud) |
| Vector Dimension Limit(dimensions) | Unlimited | 65,536 |
| Time to First Query(minutes) | 5-10 minutes | 20 minutes |
| GitHub Stars/Community Size(stars) | ~2,500 stars | 18,000+ stars |
| Self-Hosting Available | No (SaaS only) | Yes (open-source) |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | Varies by self-hosted setup |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Uptime SLA(percent) | 99.99% | — |
| 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 Practical Dataset Size(vectors) | Billions+ | — |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | — |
| Maximum Vector Dimensions(dimensions) | 20,000 dimensions | — |
| GitHub Stars | Not open-source | 28,000+ stars |
| Free Tier Vector Limit(vectors) | 100,000 vectors | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | — |
| Native Integration Count(frameworks) | 25+ (LangChain, LlamaIndex, OpenAI) | — |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | — |
| Setup Time to Production(days) | 3-5 minutes | — |
| Documentation Quality Score(out of 10) | 9/10 | — |
| Memory per 1M Vectors(GB) | 2-4 GB | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | — |
| Startup Time (empty instance)(seconds) | 2-5 seconds | — |
| Built-in LLM Integrations(count) | 0 (custom only) | — |
| Multi-modal Support (native)(modalities) | 1 (vectors only) | — |
| Number of Supported Languages(languages) | Python, JavaScript, Go, Java, Rust, C++, .NET | — |
| Open Source License | AGPL v3 (copyleft with commercial option) | — |
| Kubernetes-Native Deployment | Yes; Helm charts, StatefulSet support | — |
| Complex Metadata Filtering Support | Nested fields, range, AND/OR/NOT, geo-spatial | — |
PineconeShow 3 more attributes
Show 1 more attribute
Show 2 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Pinecone
Pros
- Zero infrastructure setup—deploy in under 5 minutes with SaaS model
- Unlimited vector dimensions for complex AI/ML models
- Automatic scaling and backup without DevOps overhead
- Built-in integrations with LangChain, OpenAI, and LlamaIndex
- Strong documentation and enterprise support tier
Cons
- Vendor lock-in with proprietary API and no self-hosting option
- Higher costs at scale (storage and query pricing can exceed $1,000/month)
- Limited metadata filtering compared to competitors
Qdrant
Pros
- 100% open-source with 18,000+ GitHub stars and active community
- Self-hosting option eliminates vendor lock-in and vendor-imposed costs
- Advanced metadata filtering with complex boolean queries
- Managed cloud tier starting at $25/month for cost-conscious teams
- Lower total cost of ownership at scale for self-hosted deployments
Cons
- Self-hosting requires Docker/Kubernetes expertise and DevOps resources
- Vector dimension limit of 65,536 (vs unlimited for Pinecone)
- Smaller ecosystem of pre-built integrations vs Pinecone
Frequently Asked Questions
Qdrant is substantially cheaper. Pinecone's free tier covers testing but costs $0.40-1.00 per 1M vectors monthly. Qdrant's self-hosted option is completely free, and the managed tier starts at $25/month regardless of data size for small workloads.
Resources & Learn More
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Where to Buy
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