Pinecone vs Qdrant 2026 | Vector Database Comparison
Pinecone is a fully managed vector database with zero-ops hosting and pay-as-you-go pricing, while Qdrant is an open-source alternative offering self-hosted deployment and lower operational costs for teams with infrastructure expertise.
Pinecone
Managed cloud vector database for production AI/ML applications at scale.
Startups, enterprises, and teams prioritizing operational simplicity and willing to pay premium for managed services and guaranteed uptime.
Qdrant
Open-source vector database with flexible deployment options (self-hosted or managed cloud) and advanced search capabilities.
Teams with infrastructure expertise, cost-conscious projects, applications requiring high-dimensional vectors, and organizations prioritizing data sovereignty and avoiding vendor lock-in.
Quick Answer
AI SummaryPinecone is a fully managed vector database with zero-ops hosting and pay-as-you-go pricing, while Qdrant is an open-source alternative offering self-hosted deployment and lower operational costs for teams with infrastructure expertise.
Our Verdict
AI-assistedChoose Pinecone if you need zero-ops deployment, enterprise SLAs (99.95% uptime), and don't want to manage infrastructure—ideal for startups and enterprises prioritizing speed to market. Choose Qdrant if you need cost efficiency, complete data control, high-dimensional vectors, or want the flexibility of open-source with optional managed hosting—ideal for teams with DevOps capacity or privacy-sensitive applications.
Was this verdict helpful?
Choose Pinecone if
Startups, enterprises, and teams prioritizing operational simplicity and willing to pay premium for managed services and guaranteed uptime.
Choose Qdrant if
Best pickTeams with infrastructure expertise, cost-conscious projects, applications requiring high-dimensional vectors, and organizations prioritizing data sovereignty and avoiding vendor lock-in.
Track this comparison
Get notified when prices change, new specs ship, or our verdict updates.
Triggers: price change new spec verdict update
No spam. Stop anytime.
Key Differences at a Glance
- Deployment Model:✓ Qdrant wins(Open-source + managed cloud option vs Fully managed SaaS only)
- Pricing Structure:✓ Qdrant wins(Free self-hosted + managed plans starting $25/month vs $0.40 per 100K vectors monthly + API calls)
- Setup Time (managed service):✓ Pinecone wins(5 minutes, zero infrastructure required vs 30+ minutes for cloud setup, hours for self-hosted)
Key Facts & Figures
64 numeric metrics compared
| Metric | Pinecone | Qdrant | Ratio |
|---|---|---|---|
| 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) | |
| 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 | |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | Varies by self-hosted setup | — |
| 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(%) | 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 | Unlimited (100K+ tested) | |
| Query Latency (p99)(milliseconds) | 50-100ms | 20-40ms (self-hosted) | |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — | — |
| GitHub Stars(stars) | 11,200+ | 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(integrations) | 25+ (LangChain, LlamaIndex, OpenAI) | — | — |
| Setup Time to Production(hours) | 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(%) | 99.95% | — | — |
| Documentation Quality Score(out of 10) | 9/10 | — | — |
| Metadata Filter Complexity(operators supported) | Advanced (AND/OR/NOT) | — | — |
| Starting Monthly Cost(USD) | $25 | — | — |
| Maximum Query Throughput(requests/second) | 5,000,000+ | — | — |
| P99 Query Latency(milliseconds) | < 50ms | — | — |
| Setup Time (first query)(minutes) | 15-30 | — | — |
| Initial Setup Time(minutes) | 10 minutes | — | — |
| Minimum Monthly Cost(USD) | $0 (free tier with limits) | — | — |
| Production Plan Cost(USD/month) | $84 (Pro plan, 5M vectors) | — | — |
| Maximum Vector Capacity(vectors) | 1B+ (distributed) | — | — |
| Query Latency (p99) at 100M Vectors(milliseconds) | < 100ms | — | — |
| Monthly Cost (1M vectors, 768 dims)(USD) | $4.00 + query fees | $0 (self-hosted) or $25 (managed) | |
| Time to Production(minutes) | 5 minutes | 30-120 minutes | |
| Maximum Vectors Per Index(vectors) | 100 billion | — | — |
| Query Latency (p50, local/optimal)(milliseconds) | 50-100ms | — | — |
| Monthly Base Cost (starter tier)(USD) | $25-50 | — | — |
| Uptime SLA(percent) | 99.95% | Self-hosted (varies), Managed 99.5% | |
| Supported Vector Dimensions(dimensions) | Up to 20,000 | — | — |
| 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 |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Fully managed SaaS onlyDeployment ModelOpen-source + managed cloud option(winner)
- $0.40 per 100K vectors monthly + API callsPricing StructureFree self-hosted + managed plans starting $25/month(winner)
- 5 minutes, zero infrastructure required(winner)Setup Time (managed service)30+ minutes for cloud setup, hours for self-hosted
- Up to 20,000 dimensionsVector Dimension SupportUnlimited dimensions (tested to 100K+)(winner)
- Unlimited (scales with pay-as-you-go)Maximum Index SizeUnlimited (self-hosted limited by server RAM)
- ~50-100ms averageQuery Latency (p99)~20-40ms average (self-hosted)(winner)
- Pinecone-specific SDK + RESTAPI CompatibilityOpenAI embedding API compatible + REST(winner)
- Deployment Model
Pinecone
Fully managed SaaS only
Qdrant
Open-source + managed cloud option(winner)
- Pricing Structure
Pinecone
$0.40 per 100K vectors monthly + API calls
Qdrant
Free self-hosted + managed plans starting $25/month(winner)
- Setup Time (managed service)
Pinecone
5 minutes, zero infrastructure required(winner)
Qdrant
30+ minutes for cloud setup, hours for self-hosted
- Vector Dimension Support
Pinecone
Up to 20,000 dimensions
Qdrant
Unlimited dimensions (tested to 100K+)(winner)
- Maximum Index Size
Pinecone
Unlimited (scales with pay-as-you-go)
Qdrant
Unlimited (self-hosted limited by server RAM)
- Query Latency (p99)
Pinecone
~50-100ms average
Qdrant
~20-40ms average (self-hosted)(winner)
- API Compatibility
Pinecone
Pinecone-specific SDK + REST
Qdrant
OpenAI embedding API compatible + REST(winner)
Full Comparison
| Attribute | Pinecone | Qdrant |
|---|---|---|
| Setup Time (Basic)(minutes) | 5-10 | — |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — |
| Setup Time (production-ready)(hours) | 0.25 hours | — |
| Time to Production(minutes) | 5 minutes(winner) | 30-120 minutes |
| 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 9 more attributesMonthly Cost (1M vectors, 1K queries/day)(USD) $45-80 — Starting Cost (Annual)(USD) $50 (Starter tier minimum) — Starting Monthly Cost(USD) $25 — Free Tier Availability(null) None — Minimum Monthly Cost(USD) $0 (free tier with limits) — Production Plan Cost(USD/month) $84 (Pro plan, 5M vectors) — Monthly Cost (1M vectors, 768 dims)(USD) $4.00 + query fees $0 (self-hosted) or $25 (managed) Monthly Base Cost (starter tier)(USD) $25-50 — 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 2 more attributesMetadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — Hybrid Search Support Yes (dense + BM25) Yes (dense + sparse) | ||
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | 50-100ms | 20-40ms (self-hosted)(winner) |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — |
| Query Latency (p95)(milliseconds) | <100ms global | — |
| Maximum Query Throughput(requests/second) | 5,000,000+ | — |
Show 6 more attributesP99 Query Latency(milliseconds) < 50ms — Query Latency (p99) at 100M Vectors(milliseconds) < 100ms — Query Latency (p50, local/optimal)(milliseconds) 50-100ms — Query Latency (95th percentile)(milliseconds) 10-50 ms — Throughput (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)(winner) |
| Vector Dimension Limit(dimensions) | Unlimited | 65,536 |
| Time to First Query(minutes) | 5-10 minutes(winner) | 20 minutes |
| GitHub Stars/Community Size(stars) | ~2,500 stars | 18,000+ stars(winner) |
| Self-Hosting Available | No (SaaS only) | Yes (open-source) |
| SLA Uptime Guarantee(%) | 99.95% (enterprise tier) | Varies by self-hosted setup |
| Uptime SLA Guarantee(%) | 99.99% | — |
| Uptime SLA(percent) | 99.95%(winner) | Self-hosted (varies), Managed 99.5% |
| Minimum Setup Time(minutes) | 15-30 minutes | — |
| Startup Time (empty instance)(seconds) | 2-5 seconds | — |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | — |
| GitHub Stars(stars) | 11,200+ | 28,000+ stars(winner) |
| GitHub Stars (Community)(stars) | Proprietary (not open-source) | — |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — |
| Maximum Vectors Supported(billions) | 5 billion (enterprise) | — |
| Maximum Vectors at Scale(millions) | 10B+ (unlimited) | — |
| Maximum Vector Capacity(vectors) | 1B+ (distributed) | — |
| Maximum Vectors Per Index(vectors) | 100 billion | — |
Show 1 more attributeMaximum Practical Dataset Size(vectors) Billions+ — | ||
| Maximum Vector Dimensions(dimensions) | 20,000 | Unlimited (100K+ tested)(winner) |
| Multi-modal Support (native)(modalities) | 1 (vectors only) | — |
| Free Tier Vector Limit(vectors) | 100,000 vectors | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | — |
| Native Integration Count(integrations) | 25+ (LangChain, LlamaIndex, OpenAI) | — |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | — |
| Code Customization(null) | Limited (SaaS constraints) | — |
| Setup Time to Production(hours) | 3-5 minutes | — |
| Uptime Guarantee(%) | 99.95% | — |
| Documentation Quality Score(out of 10) | 9/10 | — |
| Setup Time (first query)(minutes) | 15-30 | — |
| Initial Setup Time(minutes) | 10 minutes | — |
| REST API Support(yes/no) | Yes (REST + gRPC) | — |
| API Compatibility | Proprietary SDK + REST | OpenAI API compatible + REST |
| RBAC & Enterprise Security(yes/no) | Yes (SOC 2 Type II, HIPAA) | — |
| Deployment Options | SaaS only (managed) | Self-hosted + managed cloud |
| Supported Vector Dimensions(dimensions) | Up to 20,000 | — |
| LangChain Integration Native Support | Yes, official integration | — |
| Memory per 1M Vectors(GB) | 2-4 GB | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | — |
| Built-in LLM Integrations(count) | 0 (custom 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 9 more attributes
Show 2 more attributes
Show 6 more attributes
Show 1 more attribute
Pros & Cons
10 pros·6 cons across both
Pinecone
Pros
- 5-minute setup with no infrastructure management required
- 99.95% SLA uptime with enterprise-grade support
- Integrated with OpenAI embeddings API for seamless workflow
- Automatic scaling and indexing without manual tuning
- Hybrid search combining dense vectors + sparse BM25 retrieval
Cons
- Highest per-vector cost at $0.40/100K vectors monthly
- Vendor lock-in with proprietary API and no self-hosted option
- Limited to 20,000 dimensions, insufficient for some large language models
Qdrant
Pros
- 100% free open-source deployment with no per-vector fees
- Supports unlimited vector dimensions (tested to 100K+)
- 20-40ms query latency on self-hosted (faster than SaaS alternatives)
- Compatible with OpenAI embedding API without vendor lock-in
- Advanced filtering with payload-based metadata and range queries
Cons
- Self-hosted requires DevOps expertise and ongoing infrastructure maintenance
- Managed cloud tier less mature than Pinecone with smaller customer base
- No built-in multi-tenancy or enterprise compliance features in open-source version
Frequently Asked Questions
5 questions
Qdrant is significantly cheaper at scale. For 10M vectors with typical query volume, Pinecone costs ~$40/month plus query fees, while Qdrant self-hosted costs ~$50/month for cloud infrastructure (AWS t3.large). Qdrant managed cloud starts at $25/month. Self-hosted Qdrant becomes cost-effective after 2-3 months if you're paying per-vector with Pinecone.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
Related Comparisons
12 more to explore
Pinecone vs Qdrant
softwarePinecone vs Chroma
softwareLlamaIndex vs Pinecone
softwarePinecone vs pgvector
softwarePinecone vs Chroma
softwarePinecone vs Weaviate
softwarePinecone vs Milvus
softwareChroma vs Pinecone
softwareWeaviate vs Qdrant
softwareChroma vs Qdrant
softwarePinecone vs Weaviate
softwareChroma vs Pinecone
software
Related Articles
5 articles
- technology
Best Streaming Services in 2026: Top Picks for Every Budget & Interest
Navigating the crowded streaming landscape in 2026 can be overwhelming. We've tested and ranked the best streaming services that offer the most value, from Netflix's massive library to budget-friendly options like Tubi, helping you cut cable and find your perfect entertainment solution.
Read article - technology
Best Live TV Streaming Services & Plans for Spring 2026: Complete Buyer's Guide
Tired of overpaying for cable? Discover the best live TV streaming services and plans for Spring 2026, including YouTube TV's new genre-based packages starting at $55/month. Our comprehensive guide breaks down pricing, channels, and features to help you cut the cord.
Read article - technology
Philo in 2026: Streaming TV Service Review, Pricing & Reddit Community Insights
Explore Philo's evolution heading into 2026, including pricing tiers, channel lineup, and how it compares to competitors like Sling TV. Discover what the r/PhiloTV Reddit community thinks about the service's current offerings and future prospects.
Read article - technology
Best US Fighter Jets 2026: Top American Combat Aircraft Ranked
Discover the most advanced US fighter jets dominating the skies in 2026. From the legendary F-22 Raptor to the versatile F-35 Lightning II, we rank America's best combat aircraft based on performance, stealth, and air superiority capabilities.
Read article - technology
Philo in 2026: Pricing, Lineup & How It Compares to Sling TV
As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.
Read article
Explore More
Related comparisons and categories