Pinecone vs Milvus
Pinecone
Managed cloud vector database with fast similarity search, advanced metadata filtering, and enterprise reliability.
Startups, AI teams building MVP products, enterprises avoiding infrastructure overhead, projects with <10B vectors
Milvus
High-performance open-source vector database optimized for massive-scale similarity search and cost-efficient deployment.
Enterprise teams with DevOps capacity, cost-sensitive organizations at scale, regulated industries, projects >50B vectors
Short Answer
Pinecone is a managed cloud vector database service emphasizing ease of use and minimal operational overhead, while Milvus is an open-source vector database requiring self-hosting that offers greater customization and cost control for large-scale deployments.
Our Verdict
AI-assistedChoose Pinecone if you prioritize rapid deployment, minimal DevOps overhead, and integration simplicity for startups and mid-market teams with <10B vectors. Choose Milvus if you need cost efficiency at scale, full customization, data residency control, or operate in resource-constrained environments where open-source flexibility justifies infrastructure management complexity.
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Choose Pinecone if
Startups, AI teams building MVP products, enterprises avoiding infrastructure overhead, projects with <10B vectors
Choose Milvus if
Enterprise teams with DevOps capacity, cost-sensitive organizations at scale, regulated industries, projects >50B vectors
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Key Differences at a Glance
Key Facts & Figures
| Metric | Pinecone | Milvus | 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 | — | — |
| 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% | Self-managed (no SLA) | — |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | 31,000+ stars | -92% |
| 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 | 25,600 | — |
| 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 | $20-150 (infrastructure dependent) | -26% |
| Maximum Vectors Supported(billions) | 5 billion (enterprise) | Unlimited (hardware-constrained) | — |
| Average Query Latency (p50)(milliseconds) | 45-120ms | 15-80ms | +74% |
| Setup Time (production-ready)(hours) | 0.25 hours | 4-8 hours | -96% |
| Native Integration Count(frameworks) | 25+ (LangChain, LlamaIndex, OpenAI) | 40+ (includes Spark, Kafka, Airflow) | -38% |
| 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 Throughput(operations per second (QPS)) | 500,000 QPS | 500,000 QPS | — |
| Maximum Collection Size(billion vectors) | 4+ billion vectors | 4+ billion vectors | — |
| Setup Time (Cloud/Self-Hosted)(minutes) | 30+ minutes (Docker/K8s) | 30+ minutes (Docker/K8s) | — |
| Number of Native LLM Integrations(integrations) | 0 (external required) | 0 (external required) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Pinecone
Fully managed SaaS cloud service
Milvus
Open-source self-hosted or cloud deployment🏆
Pinecone
$0.40 per 100k vectors (starter) + query costs
Milvus
Free to deploy; infrastructure costs only🏆
Pinecone
5-10 minutes, API key only🏆
Milvus
2-8 hours for production deployment
Pinecone
Up to 5 billion vectors (enterprise)
Milvus
Unlimited (constrained by hardware)🏆
Pinecone
50-200ms average (p99)
Milvus
10-100ms (varies by configuration)🏆
Pinecone
10,000+ GitHub stars, 500+ enterprise customers
Milvus
25,000+ GitHub stars, 1000+ adopters🏆
Pinecone
Minimal; REST API, straightforward documentation🏆
Milvus
Moderate; requires Kubernetes/infrastructure knowledge
Full Comparison
| Attribute |  Pinecone |  Milvus |
|---|---|---|
| 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 | 4-8 hours |
| Setup Time (Cloud/Self-Hosted)(minutes) | 30+ minutes (Docker/K8s) | — |
| 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 2 more attributesMonthly Cost (1M vectors, 1K queries/day)(USD) $45-80 $20-150 (infrastructure dependent) Starting Cost (Annual)(USD) $50 (Starter tier minimum) — | ||
| 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 | — |
Show 3 more attributesMetadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — Built-in Hybrid Search Support Requires external tools — Number of Native LLM Integrations(integrations) 0 (external required) — | ||
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | 50-100ms | — |
| Average Query Latency (p50)(milliseconds) | 45-120ms | 15-80ms |
| Query Latency (p95)(milliseconds) | <100ms global | — |
| Query Throughput(operations per second (QPS)) | 500,000 QPS | — |
Show 1 more attributeGPU Acceleration Support Full CUDA/GPU support — | ||
| 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 | — |
| 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% | Self-managed (no SLA) |
| 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) | Unlimited (hardware-constrained) |
| Maximum Vectors at Scale(millions) | 10B+ (unlimited) | — |
| Maximum Collection Size(billion vectors) | 4+ billion vectors | — |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | 31,000+ stars |
| Maximum Vector Dimensions(dimensions) | 20,000 dimensions | — |
| GitHub Stars | Not open-source | 25,600 |
| Free Tier Vector Limit(vectors) | 100,000 vectors | — |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage | — |
| Native Integration Count(frameworks) | 25+ (LangChain, LlamaIndex, OpenAI) | 40+ (includes Spark, Kafka, Airflow) |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | Full; supports Parquet, Arrow, SQL dumps, zero egress cost |
| Setup Time to Production(days) | 3-5 minutes | — |
| Documentation Quality Score(out of 10) | 9/10 | — |
| Licensing Cost(USD) | $0 (open-source) | — |
PineconeMilvusShow 2 more attributes
Show 3 more attributes
Show 1 more attribute
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Pinecone
Pros
- Zero infrastructure management; fully serverless deployment
- Sub-100ms query latency with built-in index optimization
- Native integrations with LangChain, LlamaIndex, and OpenAI SDK
- Single API endpoint handles 99.99% uptime SLA
- Pod-based pricing allows vertical and horizontal scaling
Cons
- Vendor lock-in risk; proprietary API and data format
- Data egress costs and limited export capabilities add 15-30% to total cost
- Regional restrictions; data must reside in US, EU, or Asia-Pacific regions only
Milvus
Pros
- 100% open-source; full code transparency and community-driven development
- Supports 10x-50x larger datasets than Pinecone for equivalent cost
- Flexible storage backends: DuckDB, PostgreSQL, or Minio S3-compatible
- HNSW, IVFFLAT, and GPU-accelerated indexing algorithms for optimization
- On-premise deployment enables HIPAA, GDPR, and sovereign data residency compliance
Cons
- Requires Kubernetes/Docker expertise and 20-60 hours initial setup and tuning
- No managed backups; users responsible for disaster recovery and high availability
- Community support only (no SLA); enterprise support from Zilliz costs $5K-50K/year
Frequently Asked Questions
Use Pinecone if your vector dataset is <5B vectors, you want zero infrastructure overhead, and require <100ms p50 latency with SLA guarantees. Choose Milvus if your dataset exceeds 10B vectors, you have DevOps capacity, or need compliance control (HIPAA/GDPR). Pinecone's managed service accelerates time-to-market; Milvus offers 60-80% cost savings at scale and eliminates vendor lock-in.
Resources & Learn More
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Where to Buy
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