software
Pinecone vs Qdrant 2026: Cost, Features, Comparison
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.
Pinecone
Fully managed cloud vector database with serverless architecture and enterprise features.
Startups, teams without DevOps expertise, enterprises prioritizing rapid deployment and simplicity over cost optimization.
Score63%
VS
Q
Qdrant
Open-source vector database with flexible deployment options and advanced query filtering.
Teams with DevOps capabilities, enterprises with strict data residency requirements, cost-sensitive organizations scaling to millions of vectors.
Score63%
125 attributes7 differences16 pros/cons
Quick Answer
AI SummaryPinecone 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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Pinecone
6.9/10
Qdrant
8.1/10
Q
Choose Pinecone if
Startups, teams without DevOps expertise, enterprises prioritizing rapid deployment and simplicity over cost optimization.
Q
Choose Qdrant if
Best pickTeams 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
- Deployment Model:✓ Qdrant wins(Open-source + managed cloud option vs Fully managed SaaS only)
- Starting Price (Monthly):✓ Qdrant wins($0 (self-hosted), $25+ (managed cloud) vs $0 (starter), scales to $100+)
- Setup Time:✓ Pinecone wins(< 5 minutes vs 5 minutes (managed) to 2-4 hours (self-hosted))
Key Facts & Figures
94 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) | 3 (pod, serverless, custom) | — | — |
| Vector Store Integrations(integrations) | 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 | |
| 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 | Unlimited (100K+ tested) | |
| Query Latency (p99)(milliseconds) | <100 ms | <50 ms (self-hosted) | |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — | — |
| GitHub Stars(stars) | Not public (proprietary) | 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(minutes) | 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(score) | 9/10 | — | — |
| Metadata Filter Complexity(operators supported) | Advanced (AND/OR/NOT) | — | — |
| Free Tier Capacity(hits per month) | 100,000 free vectors | — | — |
| Production Starter Cost(USD/month) | $70 | — | — |
| Average Query Latency (P99)(milliseconds) | 50-100ms | — | — |
| Setup to Production Time(hours) | 0.5 | — | — |
| Starting Monthly Cost(USD) | $10 minimum | — | — |
| 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) | 1B+ | |
| 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(days) | 15-30 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 | — | — |
| Supported Vector Dimensions(dimensions) | Up to 20,000 | — | — |
| Free Tier Storage(GB) | 1M vectors | — | — |
| Production Monthly Cost (Baseline)(USD) | $1,500-3,000 | — | — |
| Setup Complexity (1-10 scale)(difficulty score) | 2/10 | — | — |
| API SDKs Available(count) | 6+ languages (Python, Node.js, Go, Java, Rust, gRPC) | — | — |
| SLA Uptime Guarantee(percent) | 99.99% | Varies by self-hosted setup | — |
| Max Vector Dimensions Supported(dimensions) | 10K dimensions | — | — |
| Time to Production Deployment(days) | 2-4 hours | — | — |
| p50 Query Latency (Global)(milliseconds) | 25ms | — | — |
| Storage Cost (1M vectors, 1536-dim)(USD per month) | $50-150 | — | — |
| Supported Programming Languages(count) | Python, JavaScript, Go, Java, REST API | — | — |
| Cost for 1M Vectors/Month(USD) | $150-300 | — | — |
| Uptime SLA(percent) | 99.95% | User-dependent (self-hosted); 99.9% (managed) | |
| Minimum Monthly Cost (Production)(USD) | $150-300 | — | — |
| Setup Time to First Query(minutes) | 5-10 minutes | — | — |
| Maximum Recommended Vectors(millions) | 100M+ | Unlimited (billions with clustering) | |
| Query Latency (1M vectors)(milliseconds) | 50-100ms | — | — |
| Metadata Filter Operators(count) | 50+ | — | — |
| GitHub Stars (Community)(stars) | ~5,200 | — | — |
| Time to First Production Query(minutes) | ~15 minutes | ~20-120 minutes | |
| Cost for 1M Daily Queries + 100GB Storage/Month(USD) | $500-800 | $0 (self-hosted); $400-600 (managed) | |
| Maximum Vector Dimension Support(dimensions) | 20,000 dimensions | Unlimited (limited by memory) | — |
| 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(petabytes) | 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 | |
| Query Latency (1M vectors, p99)(milliseconds) | ~75ms | ~75ms | |
| Setup Time (local environment)(minutes) | 15-20 minutes (with Docker) | 15-20 minutes (with Docker) | |
| Supported Embedding Dimensions(max dimensions) | Up to 65536 | Up to 65536 | |
| Language/SDK Support(number of SDKs) | Python, JavaScript, TypeScript, Go, Rust, Java, .NET | Python, JavaScript, TypeScript, Go, Rust, Java, .NET | |
| Query Throughput (QPS)(queries/second) | 10,000+ QPS | 10,000+ QPS | |
| Memory Overhead per Vector(bytes) | 50-100 bytes | 50-100 bytes | |
| Latency at 100M Vectors(milliseconds) | 50-150ms | 50-150ms |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Qdrant leads
Q
5QdrantPinecone
Attribute
Qdrant
- Fully managed SaaS onlyDeployment ModelOpen-source + managed cloud option(winner)
- $0 (starter), scales to $100+Starting Price (Monthly)$0 (self-hosted), $25+ (managed cloud)(winner)
- < 5 minutes(winner)Setup Time5 minutes (managed) to 2-4 hours (self-hosted)
- Unlimited(winner)Vector Dimension SupportUp to 65,536 dimensions
- Basic filtering with payload supportMetadata FilteringAdvanced filtering with complex queries(winner)
- ~2,500 starsGitHub Stars~18,000+ stars(winner)
- Hosted on Pinecone serversData Sovereignty/PrivacyFull control with self-hosting option(winner)
- Deployment Model
Pinecone
Fully managed SaaS only
Qdrant
Open-source + managed cloud option(winner)
- Starting Price (Monthly)
Pinecone
$0 (starter), scales to $100+
Qdrant
$0 (self-hosted), $25+ (managed cloud)(winner)
- Setup Time
Pinecone
< 5 minutes(winner)
Qdrant
5 minutes (managed) to 2-4 hours (self-hosted)
- Vector Dimension Support
Pinecone
Unlimited(winner)
Qdrant
Up to 65,536 dimensions
- Metadata Filtering
Pinecone
Basic filtering with payload support
Qdrant
Advanced filtering with complex queries(winner)
- GitHub Stars
Pinecone
~2,500 stars
Qdrant
~18,000+ stars(winner)
- Data Sovereignty/Privacy
Pinecone
Hosted on Pinecone servers
Qdrant
Full control with self-hosting option(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 | — |
| Setup Complexity (1-10 scale)(difficulty score) | 2/10 | — |
| Setup Time (local environment)(minutes) | 15-20 minutes (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 16 more attributesMonthly Cost (1M vectors, 1K queries/day)(USD) $45-80 — Starting Cost (Annual)(USD) $50 (Starter tier minimum) — Production Starter Cost(USD/month) $70 — Starting Monthly Cost(USD) $10 minimum — Free Tier Availability(boolean) 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 — Free Tier Storage(GB) 1M vectors — Production Monthly Cost (Baseline)(USD) $1,500-3,000 — Storage Cost (1M vectors, 1536-dim)(USD per month) $50-150 — Cost for 1M Vectors/Month(USD) $150-300 — Minimum Monthly Cost (Production)(USD) $150-300 — Cost for 1M Daily Queries + 100GB Storage/Month(USD) $500-800 $0 (self-hosted); $400-600 (managed) Managed Cloud Base Price (monthly)(USD) $10/month — | ||
| Supported Index Types(count) | 3 (pod, serverless, custom) | — |
| Metadata Filtering Complexity(syntax level) | Boolean operators, ranges, sparse-dense hybrid | 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 10 more attributesNative Integration Count(integrations) 25+ (LangChain, LlamaIndex, OpenAI) — Metadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — Hybrid Search Support Yes (dense + BM25) Yes (dense + sparse) Max Vector Dimensions Supported(dimensions) 10K dimensions — Hybrid Search Capability Yes (sparse-dense vectors) — Metadata Filtering Support Native, advanced filtering on metadata — Metadata Filter Operators(count) 50+ — Supported Embedding Dimensions(max dimensions) Up to 65536 — Filtering Query Support(complexity level) Complex nested, geo, range, and boolean queries — Native Hybrid Search Yes (BM25 included) — | ||
| Vector Store Integrations(integrations) | 0 (standalone database) | — |
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | <100 ms | <50 ms (self-hosted)(winner) |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — |
| Query Latency (P95)(milliseconds) | <100ms global | — |
| Uptime Guarantee(percent) | 99.95% | — |
Show 11 more attributesAverage Query Latency (P99)(milliseconds) 50-100ms — Maximum Query Throughput(requests/second) 5,000,000+ — P99 Query Latency(milliseconds) < 50ms — Query Latency (p99) at 100M Vectors(milliseconds) < 100ms — Query Latency (p50, local/optimal)(milliseconds) 50-100ms — p50 Query Latency (Global)(milliseconds) 25ms — Query Latency (1M vectors)(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 — Query Latency (1M vectors, p99)(milliseconds) ~75ms — | ||
| Free Tier Vector Capacity(millions of vectors) | 1 | — |
| Free Tier Capacity(hits per month) | 100,000 free vectors | — |
| 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)(winner) |
| Setup Time to Production(minutes) | 3-5 minutes | — |
| Time to Production(days) | 15-30 minutes(winner) | 30-120 minutes |
| Startup Time (empty instance)(seconds) | 2-5 seconds | — |
| Minimum Setup Time(minutes) | 15-30 minutes | — |
| Documentation Quality Score(score) | 9/10 | — |
| Setup Time (first query)(minutes) | 15-30 | — |
| Setup Time(minutes) | <5 minutes(winner) | 15-30 minutes |
| Setup Time to First Query(minutes) | 5-10 minutes | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| SLA Uptime Guarantee(percent) | 99.99% | Varies by self-hosted setup |
| Uptime SLA(percent) | 99.95%(winner) | User-dependent (self-hosted); 99.9% (managed) |
| GitHub Community Stars(stars) | ~2,500 (closed-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 Vectors Per Index(vectors) | 100 billion | — |
| Maximum Recommended Vectors(millions) | 100M+ | Unlimited (billions with clustering)(winner) |
Show 1 more attributeMaximum Practical Dataset Size(petabytes) Billions+ — | ||
| Maximum Vector Dimensions(dimensions) | 20,000 | Unlimited (100K+ tested)(winner) |
| Supported Vector Dimensions(dimensions) | Up to 20,000 | — |
| Supported Indexing Algorithms(count) | Proprietary optimized (HNSW variant) | — |
| GitHub Stars(stars) | Not public (proprietary) | 28,000+ stars |
| Free Tier Vector Limit(vectors) | 100,000 vectors | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | — |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | — |
| Code Customization(null) | Limited (SaaS constraints) | — |
| Setup to Production Time(hours) | 0.5 | — |
| Infrastructure Required | None (fully managed) | — |
| Initial Setup Time(minutes) | 10 minutes | — |
| Maximum Vector Capacity(vectors) | 1B+ (distributed) | 1B+ |
| Query Throughput (QPS)(queries/second) | 10,000+ QPS | — |
| Latency at 100M Vectors(milliseconds) | 50-150ms | — |
| REST API Support(yes/no) | Yes (REST + gRPC) | — |
| API Compatibility | Proprietary SDK + REST | OpenAI API compatible + REST |
| API SDKs Available(count) | 6+ languages (Python, Node.js, Go, Java, Rust, gRPC) | — |
| Language/SDK Support(number of SDKs) | Python, JavaScript, TypeScript, Go, Rust, Java, .NET | — |
| RBAC & Enterprise Security(yes/no) | Yes (SOC 2 Type II, HIPAA) | — |
| Enterprise Security Compliance(certifications) | SOC 2 Type II, HIPAA-ready, GDPR compliant | — |
| Multi-tenant RBAC Support | Full RBAC + OAuth2 | — |
| Deployment Options | SaaS only (managed) | Self-hosted + managed cloud |
| LangChain Integration Native Support | Yes, official integration | — |
| Time to Production Deployment(days) | 2-4 hours | — |
| Open-Source | No | — |
| Open Source License | AGPL-3.0 (with commercial license) | — |
| License Model | BUSL-1.1 + Cloud/Enterprise | — |
| Supported Programming Languages(count) | Python, JavaScript, Go, Java, REST API | — |
| Number of Supported Languages(languages) | Python, JavaScript, Go, Java, Rust, C++, .NET | — |
| GitHub Stars (Community)(stars) | ~5,200 | — |
| Memory Footprint (Installed)(megabytes) | Cloud-managed | — |
| Time to First Production Query(minutes) | ~15 minutes(winner) | ~20-120 minutes |
| Maximum Vector Dimension Support(dimensions) | 20,000 dimensions | Unlimited (limited by memory) |
| Memory per 1M Vectors(GB) | 2-4 GB | — |
| Memory Footprint (at rest, 1M vectors)(MB) | ~200MB | — |
| Built-in LLM Integrations(count) | 0 (custom only) | — |
| Multi-modal Support (native)(modalities) | 1 (vectors only) | — |
| Kubernetes-Native Deployment | Yes; Helm charts, StatefulSet support | — |
| Complex Metadata Filtering Support | Nested fields, range, AND/OR/NOT, geo-spatial | — |
| Primary Indexing Algorithm(algorithm type) | HNSW, IVF-Flat, Product Quantization | — |
| Memory Overhead per Vector(bytes) | 50-100 bytes | — |
PineconeQQdrant
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
—
Setup Complexity (1-10 scale)(difficulty score)
2/10
—
Setup Time (local environment)(minutes)
15-20 minutes (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 16 more attributes
Monthly Cost (1M vectors, 1K queries/day)(USD)
$45-80
—
Starting Cost (Annual)(USD)
$50 (Starter tier minimum)
—
Production Starter Cost(USD/month)
$70
—
Starting Monthly Cost(USD)
$10 minimum
—
Free Tier Availability(boolean)
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
—
Free Tier Storage(GB)
1M vectors
—
Production Monthly Cost (Baseline)(USD)
$1,500-3,000
—
Storage Cost (1M vectors, 1536-dim)(USD per month)
$50-150
—
Cost for 1M Vectors/Month(USD)
$150-300
—
Minimum Monthly Cost (Production)(USD)
$150-300
—
Cost for 1M Daily Queries + 100GB Storage/Month(USD)
$500-800
$0 (self-hosted); $400-600 (managed)
Managed Cloud Base Price (monthly)(USD)
$10/month
—
Supported Index Types(count)
3 (pod, serverless, custom)
—
Metadata Filtering Complexity(syntax level)
Boolean operators, ranges, sparse-dense hybrid
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 10 more attributes
Native Integration Count(integrations)
25+ (LangChain, LlamaIndex, OpenAI)
—
Metadata Filter Complexity(operators supported)
Advanced (AND/OR/NOT)
—
Hybrid Search Support
Yes (dense + BM25)
Yes (dense + sparse)
Max Vector Dimensions Supported(dimensions)
10K dimensions
—
Hybrid Search Capability
Yes (sparse-dense vectors)
—
Metadata Filtering Support
Native, advanced filtering on metadata
—
Metadata Filter Operators(count)
50+
—
Supported Embedding Dimensions(max dimensions)
Up to 65536
—
Filtering Query Support(complexity level)
Complex nested, geo, range, and boolean queries
—
Native Hybrid Search
Yes (BM25 included)
—
Vector Store Integrations(integrations)
0 (standalone database)
—
Query Latency (p50)(milliseconds)
50-80
—
Query Latency (p99)(milliseconds)
<100 ms
<50 ms (self-hosted)
Average Query Latency (p50)(milliseconds)
45-120ms
—
Query Latency (P95)(milliseconds)
<100ms global
—
Uptime Guarantee(percent)
99.95%
—
Show 11 more attributes
Average Query Latency (P99)(milliseconds)
50-100ms
—
Maximum Query Throughput(requests/second)
5,000,000+
—
P99 Query Latency(milliseconds)
< 50ms
—
Query Latency (p99) at 100M Vectors(milliseconds)
< 100ms
—
Query Latency (p50, local/optimal)(milliseconds)
50-100ms
—
p50 Query Latency (Global)(milliseconds)
25ms
—
Query Latency (1M vectors)(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
—
Query Latency (1M vectors, p99)(milliseconds)
~75ms
—
Free Tier Vector Capacity(millions of vectors)
1
—
Free Tier Capacity(hits per month)
100,000 free vectors
—
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)
Setup Time to Production(minutes)
3-5 minutes
—
Time to Production(days)
15-30 minutes
30-120 minutes
Startup Time (empty instance)(seconds)
2-5 seconds
—
Minimum Setup Time(minutes)
15-30 minutes
—
Documentation Quality Score(score)
9/10
—
Setup Time (first query)(minutes)
15-30
—
Setup Time(minutes)
<5 minutes
15-30 minutes
Setup Time to First Query(minutes)
5-10 minutes
—
Uptime SLA Guarantee(percent)
99.99%
—
SLA Uptime Guarantee(percent)
99.99%
Varies by self-hosted setup
Uptime SLA(percent)
99.95%
User-dependent (self-hosted); 99.9% (managed)
GitHub Community Stars(stars)
~2,500 (closed-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 Vectors Per Index(vectors)
100 billion
—
Maximum Recommended Vectors(millions)
100M+
Unlimited (billions with clustering)
Show 1 more attribute
Maximum Practical Dataset Size(petabytes)
Billions+
—
Maximum Vector Dimensions(dimensions)
20,000
Unlimited (100K+ tested)
Supported Vector Dimensions(dimensions)
Up to 20,000
—
Supported Indexing Algorithms(count)
Proprietary optimized (HNSW variant)
—
GitHub Stars(stars)
Not public (proprietary)
28,000+ stars
Free Tier Vector Limit(vectors)
100,000 vectors
—
Estimated Monthly Cost (1M vectors)(USD)
$10 + storage
—
Data Export Capability(text)
Limited; JSON export only, subject to egress costs
—
Code Customization(null)
Limited (SaaS constraints)
—
Setup to Production Time(hours)
0.5
—
Infrastructure Required
None (fully managed)
—
Initial Setup Time(minutes)
10 minutes
—
Maximum Vector Capacity(vectors)
1B+ (distributed)
1B+
Query Throughput (QPS)(queries/second)
10,000+ QPS
—
Latency at 100M Vectors(milliseconds)
50-150ms
—
REST API Support(yes/no)
Yes (REST + gRPC)
—
API Compatibility
Proprietary SDK + REST
OpenAI API compatible + REST
API SDKs Available(count)
6+ languages (Python, Node.js, Go, Java, Rust, gRPC)
—
Language/SDK Support(number of SDKs)
Python, JavaScript, TypeScript, Go, Rust, Java, .NET
—
RBAC & Enterprise Security(yes/no)
Yes (SOC 2 Type II, HIPAA)
—
Enterprise Security Compliance(certifications)
SOC 2 Type II, HIPAA-ready, GDPR compliant
—
Multi-tenant RBAC Support
Full RBAC + OAuth2
—
Deployment Options
SaaS only (managed)
Self-hosted + managed cloud
LangChain Integration Native Support
Yes, official integration
—
Time to Production Deployment(days)
2-4 hours
—
Open-Source
No
—
Open Source License
AGPL-3.0 (with commercial license)
—
License Model
BUSL-1.1 + Cloud/Enterprise
—
Supported Programming Languages(count)
Python, JavaScript, Go, Java, REST API
—
Number of Supported Languages(languages)
Python, JavaScript, Go, Java, Rust, C++, .NET
—
GitHub Stars (Community)(stars)
~5,200
—
Memory Footprint (Installed)(megabytes)
Cloud-managed
—
Time to First Production Query(minutes)
~15 minutes
~20-120 minutes
Maximum Vector Dimension Support(dimensions)
20,000 dimensions
Unlimited (limited by memory)
Memory per 1M Vectors(GB)
2-4 GB
—
Memory Footprint (at rest, 1M vectors)(MB)
~200MB
—
Built-in LLM Integrations(count)
0 (custom only)
—
Multi-modal Support (native)(modalities)
1 (vectors only)
—
Kubernetes-Native Deployment
Yes; Helm charts, StatefulSet support
—
Complex Metadata Filtering Support
Nested fields, range, AND/OR/NOT, geo-spatial
—
Primary Indexing Algorithm(algorithm type)
HNSW, IVF-Flat, Product Quantization
—
Memory Overhead per Vector(bytes)
50-100 bytes
—
Pros & Cons
10 pros·6 cons across both
Q
Pinecone
+5-3
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
Q
Qdrant
+5-3
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
5 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
Curated sources to dive deeper
Where to Buy
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Wikipedia
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