software
Pinecone vs Weaviate 2026: Vector Database Comparison
Pinecone is a fully managed vector database optimized for production speed and ease of use, while Weaviate is an open-source alternative offering more control and flexibility but requiring self-management. Pinecone excels for enterprise deployments needing 99.99% uptime, whereas Weaviate suits teams wanting customization and lower operational overhead.
Pinecone
Managed serverless vector database with advanced filtering and global infrastructure.
Enterprise teams, production AI applications, real-time recommendation systems, and companies prioritizing uptime over cost
Score63%
VS
W
Weaviate
Open-source vector database with self-hosted and managed cloud options, emphasizing flexibility and developer control.
Open-source advocates, cost-sensitive teams, self-hosted deployments, and organizations needing full data control
Score63%
Quick Answer
AI SummaryPinecone is a fully managed vector database optimized for production speed and ease of use, while Weaviate is an open-source alternative offering more control and flexibility but requiring self-management. Pinecone excels for enterprise deployments needing 99.99% uptime, whereas Weaviate suits teams wanting customization and lower operational overhead.
Our Verdict
AI-assistedChoose Pinecone if you need ultra-low latency, enterprise SLA guarantees, and prefer outsourcing infrastructure management—ideal for production AI/search applications at scale. Choose Weaviate if you prioritize cost control, want to self-host, need customization flexibility, or prefer open-source solutions with community support.
Community feedback
Was this verdict helpful?
Thanks — we'll use this to improve our verdicts.
Pinecone
9.2/10
Weaviate
5.8/10
W
Choose Pinecone if
Best pickEnterprise teams, production AI applications, real-time recommendation systems, and companies prioritizing uptime over cost
W
Choose Weaviate if
Open-source advocates, cost-sensitive teams, self-hosted deployments, and organizations needing full data control
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:✓ Weaviate wins(Open-source with self-hosted & managed cloud options vs Fully managed SaaS only)
- Query Latency (p99):✓ Pinecone wins(~20-30ms vs ~50-100ms)
- Starting Price (Monthly):✓ Weaviate wins($0 (self-hosted) or $25+ (managed) vs $0.04 per 1M vectors + $0.25/hour usage)
Key Facts & Figures
105 numeric metrics compared
| Metric | Pinecone | Weaviate | 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(databases) | 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) | |
| GitHub Stars/Community Size(stars) | ~2,500 stars | — | — |
| Minimum Setup Time(minutes) | 15-30 minutes | 30-60 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 | |
| Monthly Starting Cost(USD) | $70 (minimum pod + index) | — | — |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — | — |
| Maximum Vector Dimensions(dimensions) | 20,480 | Unlimited | — |
| Query Latency (p99)(milliseconds) | 20-30ms | 50-100ms | |
| Setup Time (Local Development)(Minutes) | 15-20 (account + API key setup) | — | — |
| GitHub Stars(stars) | Not public (proprietary) | ~4,000 | — |
| 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) | |
| 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 | 24-72 hours | |
| 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 | — | — |
| Uptime SLA(percent) | 99.95% | User-managed (no SLA) | — |
| Starting Monthly Cost(USD) | $10 minimum | $0 (self-hosted) | |
| Maximum Query Throughput(requests/second) | 5,000,000+ | 2,000,000-3,000,000 | |
| P99 Query Latency(milliseconds) | < 50ms | 50-150ms | |
| Setup Time (first query)(minutes) | 15-30 | 30+ minutes (self-hosted) | |
| GitHub Stars (Community)(stars) | Proprietary (not open-source) | 9,200+ | — |
| Initial Setup Time(hours) | 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 | — | — |
| Time to Production(days) | 15-30 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(million vectors) | 1M vectors | Unlimited (self-hosted) | — |
| 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% | 99.9% (managed) | |
| Max Vector Dimensions Supported(dimensions) | 10K dimensions | Unlimited | — |
| Time to Production Deployment(hours) | 2-4 hours | 24-48 hours (self-hosted) | |
| p50 Query Latency (Global)(milliseconds) | 25ms | — | — |
| Storage Cost (1M vectors, 1536-dim)(USD per month) | $50-150 | — | — |
| Supported Programming Languages(languages) | Python, JavaScript, Go, Java, REST API | — | — |
| 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 | |
| Max Recommended Vector Count(vectors) | 100M+ (distributed) | 100M+ (distributed) | |
| Memory Usage (1M 768-dim vectors)(GB) | 1.2-1.5 GB | 1.2-1.5 GB | |
| Query Latency (1M vectors, 10 concurrent)(ms) | 45-80 ms | 45-80 ms | |
| Minimum Starting Cost (annual)(USD) | $300 (SaaS) or $0 (self-hosted) | $300 (SaaS) or $0 (self-hosted) | |
| Vector Index Types Supported(count) | 2 (HNSW, Flat) | 2 (HNSW, Flat) | |
| Query API Types(count) | 3 (GraphQL, REST, Python) | 3 (GraphQL, REST, Python) | |
| Maximum Vector Dimension Support(dimensions) | Unlimited (tested to 4096+) | Unlimited (tested to 4096+) | |
| Production Deployments (Estimated)(count) | ~500 enterprise customers | ~500 enterprise customers | |
| Maximum Vector Scale(vectors) | 1+ billion | 1+ billion | |
| Query Latency (1M vectors)(ms) | 50-200 ms | 50-200 ms | |
| Vector Indexing Algorithm Options(count) | HNSW, FLAT, IVF, PQ | HNSW, FLAT, IVF, PQ | |
| Scalability Limit (Single Node)(million vectors) | 100+ with optimization | 100+ with optimization | |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | High (8/10) | |
| Time to Production (First Query)(minutes) | 25 minutes | 25 minutes | |
| Maximum Recommended Vector Count(millions) | 500M+ vectors | 500M+ vectors | |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | 512 MB | |
| Enterprise Support SLA | 99.5% guaranteed uptime | 99.5% guaranteed uptime | |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | 9,500+ stars |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Evenly matched1 tie
W
3WeaviatePinecone
Attribute
Weaviate
- Fully managed SaaS onlyDeployment ModelOpen-source with self-hosted & managed cloud options(winner)
- ~20-30ms(winner)Query Latency (p99)~50-100ms
- $0.04 per 1M vectors + $0.25/hour usageStarting Price (Monthly)$0 (self-hosted) or $25+ (managed)(winner)
- 1M vectors (Pod index)Storage Limit (Free Tier)Unlimited (self-hosted)(winner)
- Yes, with sparse-dense vectorsHybrid Search SupportYes, native BM25 + vector search
- ~2-4 hours(winner)Setup Time to Production~1-2 days (self-hosted) or ~4 hours (managed)
- 99.99%(winner)SLA Uptime Guarantee99.9% (managed tier)
- Deployment Model
Pinecone
Fully managed SaaS only
Weaviate
Open-source with self-hosted & managed cloud options(winner)
- Query Latency (p99)
Pinecone
~20-30ms(winner)
Weaviate
~50-100ms
- Starting Price (Monthly)
Pinecone
$0.04 per 1M vectors + $0.25/hour usage
Weaviate
$0 (self-hosted) or $25+ (managed)(winner)
- Storage Limit (Free Tier)
Pinecone
1M vectors (Pod index)
Weaviate
Unlimited (self-hosted)(winner)
- Hybrid Search Support
Pinecone
Yes, with sparse-dense vectors
Weaviate
Yes, native BM25 + vector search
- Setup Time to Production
Pinecone
~2-4 hours(winner)
Weaviate
~1-2 days (self-hosted) or ~4 hours (managed)
- SLA Uptime Guarantee
Pinecone
99.99%(winner)
Weaviate
99.9% (managed tier)
Full Comparison
| Attribute | Pinecone | Weaviate |
|---|---|---|
| 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 | — |
| Initial Setup Time(hours) | 10 minutes | — |
| 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 13 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 $0 (self-hosted) Free Tier Availability None Unlimited (self-hosted) 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 — Monthly Base Cost (starter tier)(USD) $25-50 — Production Monthly Cost (Baseline)(USD) $1,500-3,000 — Storage Cost (1M vectors, 1536-dim)(USD per month) $50-150 — Managed Cloud Base Price (monthly)(USD) $25/month — Minimum Starting Cost (annual)(USD) $300 (SaaS) or $0 (self-hosted) — | ||
| Supported Index Types(count) | 3 (pod, serverless, custom) | — |
| Metadata Filtering Complexity(feature count) | Boolean operators, ranges, sparse-dense hybrid | — |
| 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 17 more attributesNative Integration Count(integrations) 25+ (LangChain, LlamaIndex, OpenAI) — Metadata Filter Complexity(operators supported) Advanced (AND/OR/NOT) — Hybrid Search Support Yes (dense + BM25) — Max Vector Dimensions Supported(dimensions) 10K dimensions Unlimited Hybrid Search Capability Yes (sparse-dense vectors) Yes (native BM25) 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 multi-tenancy with data isolation — Query Filtering Support Advanced GraphQL + WHERE clauses with boolean logic — Multi-Modal Search Text, image, audio, video — Vector Index Types Supported(count) 2 (HNSW, Flat) — Built-in LLM Integration Yes (OpenAI, Cohere, HuggingFace, Azure) — Query API Types(count) 3 (GraphQL, REST, Python) — Hybrid Search (Vector + Keyword) Yes (BM25) — Multi-modal Support Text, image, audio via modules — Enterprise Features (RBAC/Multi-tenancy) Yes — | ||
| Vector Store Integrations(databases) | 0 (standalone database) | — |
| Query Latency (p50)(milliseconds) | 50-80 | — |
| Query Latency (p99)(milliseconds) | 20-30ms(winner) | 50-100ms |
| Average Query Latency (p50)(milliseconds) | 45-120ms | — |
| Query Latency (P95)(milliseconds) | <100ms global | — |
| Average Query Latency (P99)(milliseconds) | 50-100ms | — |
Show 17 more attributesMaximum Query Throughput(requests/second) 5,000,000+ 2,000,000-3,000,000 P99 Query Latency(milliseconds) < 50ms 50-150ms Query Latency (p99) at 100M Vectors(milliseconds) < 100ms — Query Latency (p50, local/optimal)(milliseconds) 50-100ms — p50 Query Latency (Global)(milliseconds) 25ms — Indexing Methods Supported(count) 3 methods (HNSW, flat, dynamic) — Average Query Latency (1M vectors, 384-dim)(milliseconds) 75ms — Query Throughput(operations per second (QPS)) 100,000 QPS — Query Latency (95th percentile)(milliseconds) 100-500 ms — Throughput (vectors/second insert)(vectors/sec) 5,000-10,000 — Average Query Latency(milliseconds) 50-150ms — Memory Usage (1M 768-dim vectors)(GB) 1.2-1.5 GB — Query Latency (1M vectors, 10 concurrent)(ms) 45-80 ms — Maximum Vector Scale(vectors) 1+ billion — Query Latency (1M vectors)(ms) 50-200 ms — Vector Indexing Algorithm Options(count) HNSW, FLAT, IVF, PQ — Scalability Limit (Single Node)(million vectors) 100+ with optimization — | ||
| 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) | — |
| Estimated Monthly Cost at 100GB(USD) | $200-400 (managed pricing) | — |
| Vector Dimension Limit(dimensions) | Unlimited | — |
| Time to First Query(minutes) | 5-10 minutes(winner) | 30-45 minutes (self-hosted) |
| GitHub Stars/Community Size(stars) | ~2,500 stars | — |
| Self-Hosting Available | No (SaaS only) | — |
| Minimum RAM Requirement (Single Node)(MB) | 512 MB | — |
| Minimum Setup Time(minutes) | 15-30 minutes(winner) | 30-60 minutes |
| Setup Time(minutes) | 15 minutes | — |
| Uptime SLA Guarantee(percent) | 99.99% | — |
| Uptime Guarantee(percent) | 99.95% | — |
| Uptime SLA(percent) | 99.95% | User-managed (no SLA) |
| SLA Uptime Guarantee(percent) | 99.99%(winner) | 99.9% (managed) |
| GitHub Community Stars(stars) | ~2,500 (closed-source) | 13,000+ stars(winner) |
| GitHub Stars (Community)(stars) | Proprietary (not open-source) | 9,200+ |
| Maximum Vector Storage(Vectors) | 100M+ (unlimited with multi-pod) | — |
| Maximum Vector Dimensions(dimensions) | 20,480 | Unlimited |
| Maximum Vectors Supported(billions) | 5 billion (enterprise) | — |
| Maximum Vectors at Scale(millions) | 10B+ (unlimited) | — |
| Maximum Vector Capacity(vectors) | 1B+ (distributed) | — |
Show 7 more attributesMaximum Vectors Per Index(vectors) 100 billion — Maximum Scalability (distributed nodes)(nodes) 100+ — Maximum Collection Size(billion vectors) 2 billion vectors — Maximum Vectors Per Instance(vectors) 100M+ (distributed) — Max Recommended Vector Count(vectors) 100M+ (distributed) — Maximum Vector Dimension Support(dimensions) Unlimited (tested to 4096+) — Maximum Recommended Vector Count(millions) 500M+ vectors — | ||
| GitHub Stars(stars) | Not public (proprietary) | ~4,000 |
| GitHub Stars (as of 2026)(stars) | 9,500+ stars | — |
| Free Tier Vector Limit(vectors) | 100,000 vectors | Unlimited (self-hosted) |
| Estimated Monthly Cost (1M vectors)(USD) | $10 + storage(winner) | $500-800 (managed) |
| Data Export Capability(text) | Limited; JSON export only, subject to egress costs | — |
| Code Customization(null) | Limited (SaaS constraints) | Unlimited (open-source) |
| Deployment Options | SaaS only (managed) | Kubernetes, Docker, cloud (AWS/GCP/Azure) |
| Setup Time to Production(minutes) | 3-5 minutes(winner) | 24-72 hours |
| Time to Production(days) | 15-30 minutes | — |
| 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) | — |
| Documentation Quality Score(score) | 9/10 | — |
| Setup Time (first query)(minutes) | 15-30(winner) | 30+ minutes (self-hosted) |
| API Query Language Support(count) | 2 (GraphQL, REST) | — |
| Setup to Production Time(hours) | 0.5 | — |
| Deployment Model(type) | Standalone cluster (Kubernetes, Docker, Cloud) | — |
| REST API Support(yes/no) | Yes (REST + gRPC) | — |
| API Compatibility | Proprietary SDK + REST | — |
| API SDKs Available(count) | 6+ languages (Python, Node.js, Go, Java, Rust, gRPC) | — |
| RBAC & Enterprise Security(yes/no) | Yes (SOC 2 Type II, HIPAA) | — |
| Enterprise Security Compliance(certifications) | SOC 2 Type II, HIPAA-ready, GDPR compliant | — |
| Supported Vector Dimensions(dimensions) | Up to 20,000 | — |
| LangChain Integration Native Support | Yes, official integration | — |
| Free Tier Storage(million vectors) | 1M vectors | Unlimited (self-hosted) |
| Setup Complexity (1-10 scale)(difficulty score) | 2/10 | — |
| Time to Production Deployment(hours) | 2-4 hours(winner) | 24-48 hours (self-hosted) |
| Open-Source | No | Yes (Business Source License 1.1) |
| Open Source License | BSL 1.1 (Source-available, eventually open) | — |
| Supported Programming Languages(languages) | Python, JavaScript, Go, Java, REST API | — |
| 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 | — |
| Multi-Tenancy | Full native support with tenant isolation | — |
| GPU Acceleration Support | Limited (planning phase) | — |
| Memory per 1M Vectors(GB) | 8-12 GB | — |
| Multi-modal Support (native)(modalities) | 3 (text, image, audio) | — |
| Query Type Flexibility | Vector-first (GraphQL, REST) | — |
| Minimum Memory for 1M Vectors(GB) | 4-8GB | — |
| Kubernetes Support | Native Kubernetes-ready Helm charts | — |
| LangChain Integration Maturity | Supported but secondary to GraphQL API | — |
| Production Deployments (Estimated)(count) | ~500 enterprise customers | — |
| Operational Complexity (1-10 scale)(complexity score) | High (8/10) | — |
| Native RESTful API | Yes (REST + GraphQL) | — |
| Time to Production (First Query)(minutes) | 25 minutes | — |
| Advanced Filtering Support | Complex WHERE clauses, nested conditions, cross-references | — |
| Enterprise Support SLA | 99.5% guaranteed uptime | — |
PineconeWWeaviate
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
—
Initial Setup Time(hours)
10 minutes
—
Setup Time (Cloud/Self-Hosted)(minutes)
5-10 minutes (cloud)
—
Show 1 more attribute
Setup 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 13 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
$0 (self-hosted)
Free Tier Availability
None
Unlimited (self-hosted)
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
—
Monthly Base Cost (starter tier)(USD)
$25-50
—
Production Monthly Cost (Baseline)(USD)
$1,500-3,000
—
Storage Cost (1M vectors, 1536-dim)(USD per month)
$50-150
—
Managed Cloud Base Price (monthly)(USD)
$25/month
—
Minimum Starting Cost (annual)(USD)
$300 (SaaS) or $0 (self-hosted)
—
Supported Index Types(count)
3 (pod, serverless, custom)
—
Metadata Filtering Complexity(feature count)
Boolean operators, ranges, sparse-dense hybrid
—
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 17 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)
—
Max Vector Dimensions Supported(dimensions)
10K dimensions
Unlimited
Hybrid Search Capability
Yes (sparse-dense vectors)
Yes (native BM25)
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 multi-tenancy with data isolation
—
Query Filtering Support
Advanced GraphQL + WHERE clauses with boolean logic
—
Multi-Modal Search
Text, image, audio, video
—
Vector Index Types Supported(count)
2 (HNSW, Flat)
—
Built-in LLM Integration
Yes (OpenAI, Cohere, HuggingFace, Azure)
—
Query API Types(count)
3 (GraphQL, REST, Python)
—
Hybrid Search (Vector + Keyword)
Yes (BM25)
—
Multi-modal Support
Text, image, audio via modules
—
Enterprise Features (RBAC/Multi-tenancy)
Yes
—
Vector Store Integrations(databases)
0 (standalone database)
—
Query Latency (p50)(milliseconds)
50-80
—
Query Latency (p99)(milliseconds)
20-30ms
50-100ms
Average Query Latency (p50)(milliseconds)
45-120ms
—
Query Latency (P95)(milliseconds)
<100ms global
—
Average Query Latency (P99)(milliseconds)
50-100ms
—
Show 17 more attributes
Maximum Query Throughput(requests/second)
5,000,000+
2,000,000-3,000,000
P99 Query Latency(milliseconds)
< 50ms
50-150ms
Query Latency (p99) at 100M Vectors(milliseconds)
< 100ms
—
Query Latency (p50, local/optimal)(milliseconds)
50-100ms
—
p50 Query Latency (Global)(milliseconds)
25ms
—
Indexing Methods Supported(count)
3 methods (HNSW, flat, dynamic)
—
Average Query Latency (1M vectors, 384-dim)(milliseconds)
75ms
—
Query Throughput(operations per second (QPS))
100,000 QPS
—
Query Latency (95th percentile)(milliseconds)
100-500 ms
—
Throughput (vectors/second insert)(vectors/sec)
5,000-10,000
—
Average Query Latency(milliseconds)
50-150ms
—
Memory Usage (1M 768-dim vectors)(GB)
1.2-1.5 GB
—
Query Latency (1M vectors, 10 concurrent)(ms)
45-80 ms
—
Maximum Vector Scale(vectors)
1+ billion
—
Query Latency (1M vectors)(ms)
50-200 ms
—
Vector Indexing Algorithm Options(count)
HNSW, FLAT, IVF, PQ
—
Scalability Limit (Single Node)(million vectors)
100+ with optimization
—
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)
—
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)
—
Minimum RAM Requirement (Single Node)(MB)
512 MB
—
Minimum Setup Time(minutes)
15-30 minutes
30-60 minutes
Setup Time(minutes)
15 minutes
—
Uptime SLA Guarantee(percent)
99.99%
—
Uptime Guarantee(percent)
99.95%
—
Uptime SLA(percent)
99.95%
User-managed (no SLA)
SLA Uptime Guarantee(percent)
99.99%
99.9% (managed)
GitHub Community Stars(stars)
~2,500 (closed-source)
13,000+ stars
GitHub Stars (Community)(stars)
Proprietary (not open-source)
9,200+
Maximum Vector Storage(Vectors)
100M+ (unlimited with multi-pod)
—
Maximum Vector Dimensions(dimensions)
20,480
Unlimited
Maximum Vectors Supported(billions)
5 billion (enterprise)
—
Maximum Vectors at Scale(millions)
10B+ (unlimited)
—
Maximum Vector Capacity(vectors)
1B+ (distributed)
—
Show 7 more attributes
Maximum Vectors Per Index(vectors)
100 billion
—
Maximum Scalability (distributed nodes)(nodes)
100+
—
Maximum Collection Size(billion vectors)
2 billion vectors
—
Maximum Vectors Per Instance(vectors)
100M+ (distributed)
—
Max Recommended Vector Count(vectors)
100M+ (distributed)
—
Maximum Vector Dimension Support(dimensions)
Unlimited (tested to 4096+)
—
Maximum Recommended Vector Count(millions)
500M+ vectors
—
GitHub Stars(stars)
Not public (proprietary)
~4,000
GitHub Stars (as of 2026)(stars)
9,500+ stars
—
Free Tier Vector Limit(vectors)
100,000 vectors
Unlimited (self-hosted)
Estimated Monthly Cost (1M vectors)(USD)
$10 + storage
$500-800 (managed)
Data Export Capability(text)
Limited; JSON export only, subject to egress costs
—
Code Customization(null)
Limited (SaaS constraints)
Unlimited (open-source)
Deployment Options
SaaS only (managed)
Kubernetes, Docker, cloud (AWS/GCP/Azure)
Setup Time to Production(minutes)
3-5 minutes
24-72 hours
Time to Production(days)
15-30 minutes
—
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)
—
Documentation Quality Score(score)
9/10
—
Setup Time (first query)(minutes)
15-30
30+ minutes (self-hosted)
API Query Language Support(count)
2 (GraphQL, REST)
—
Setup to Production Time(hours)
0.5
—
Deployment Model(type)
Standalone cluster (Kubernetes, Docker, Cloud)
—
REST API Support(yes/no)
Yes (REST + gRPC)
—
API Compatibility
Proprietary SDK + REST
—
API SDKs Available(count)
6+ languages (Python, Node.js, Go, Java, Rust, gRPC)
—
RBAC & Enterprise Security(yes/no)
Yes (SOC 2 Type II, HIPAA)
—
Enterprise Security Compliance(certifications)
SOC 2 Type II, HIPAA-ready, GDPR compliant
—
Supported Vector Dimensions(dimensions)
Up to 20,000
—
LangChain Integration Native Support
Yes, official integration
—
Free Tier Storage(million vectors)
1M vectors
Unlimited (self-hosted)
Setup Complexity (1-10 scale)(difficulty score)
2/10
—
Time to Production Deployment(hours)
2-4 hours
24-48 hours (self-hosted)
Open-Source
No
Yes (Business Source License 1.1)
Open Source License
BSL 1.1 (Source-available, eventually open)
—
Supported Programming Languages(languages)
Python, JavaScript, Go, Java, REST API
—
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
—
Multi-Tenancy
Full native support with tenant isolation
—
GPU Acceleration Support
Limited (planning phase)
—
Memory per 1M Vectors(GB)
8-12 GB
—
Multi-modal Support (native)(modalities)
3 (text, image, audio)
—
Query Type Flexibility
Vector-first (GraphQL, REST)
—
Minimum Memory for 1M Vectors(GB)
4-8GB
—
Kubernetes Support
Native Kubernetes-ready Helm charts
—
LangChain Integration Maturity
Supported but secondary to GraphQL API
—
Production Deployments (Estimated)(count)
~500 enterprise customers
—
Operational Complexity (1-10 scale)(complexity score)
High (8/10)
—
Native RESTful API
Yes (REST + GraphQL)
—
Time to Production (First Query)(minutes)
25 minutes
—
Advanced Filtering Support
Complex WHERE clauses, nested conditions, cross-references
—
Enterprise Support SLA
99.5% guaranteed uptime
—
Pros & Cons
10 pros·6 cons across both
W
Pinecone
+5-3
Pros
- 99.99% SLA uptime with 20-30ms p99 latency—fastest for production workloads
- Zero infrastructure management; automatic scaling and failover built-in
- Built-in hybrid search with sparse-dense vector support for keyword + semantic search
- Supports 10K dimensions per vector and 500B+ index capacity on Enterprise tier
- Integrated pod-based pricing with pay-per-use model; transparent cost tracking
Cons
- Vendor lock-in; no open-source alternative if you want to switch
- Minimum monthly cost $10-25 even for small projects; expensive for hobbyists
- Limited customization; you cannot modify core indexing or storage algorithms
W
Weaviate
+5-3
Pros
- 100% open-source with GitHub community (20K+ stars); full transparency and auditability
- Free self-hosted option with unlimited vectors; only pay for managed cloud if needed
- Native BM25 + vector hybrid search without sparse-dense workarounds
- Multi-tenancy support and role-based access control out-of-the-box
- GraphQL API and extensive Python/JavaScript SDKs; strong developer ecosystem
Cons
- 50-100ms p99 latency; slower than Pinecone for ultra-low-latency requirements
- Self-hosted version requires DevOps expertise for production (scaling, backups, monitoring)
- Smaller ecosystem compared to Pinecone; fewer pre-built integrations with LLM frameworks
Frequently Asked Questions
5 questions
Weaviate self-hosted is free (only infrastructure costs ~$50-200/month on AWS). Pinecone would cost ~$400-600/month ($0.04 × 10M vectors + usage fees). For cost-sensitive startups, Weaviate self-hosted wins significantly.
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 Weaviate
softwarePinecone vs Weaviate
softwareLlamaIndex vs Pinecone
softwareLlamaIndex vs Weaviate
softwarePinecone vs pgvector
softwarePinecone vs Qdrant
softwarePinecone vs Chroma
softwarePinecone vs Milvus
softwareChroma vs Pinecone
softwareWeaviate vs pgvector
softwareWeaviate vs Milvus
softwareWeaviate vs Qdrant
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
AI generated