Milvus vs Pinecone Vector Database Vector DB

Milvus vs Pinecone Vector Database Specs
  • Free Tier Allowance
  • Rate Limit Cap
  • Session Lifetime
  • Overage Fee per 1k MAU
Comparison chart between Milvus and Pinecone Vector Database raw data
MetricMilvusPinecone Vector Database
Free Tier AllowanceOpen Source / Lite1 Serverless Index
Rate Limit CapUnlimited100 RPS
Session Lifetime24 Hours24 Hours
Overage Fee per 1k MAU$00 USD/write unit
Deploy Managed Milvus
👉 Free Tier Cluster • Built for Billion-Scale Vectors
Deploy Pinecone Serverless
👉 1 Permanent Free Index • No Card Required
Milvus
Free Tier Allowance:
Open Source / Lite
Rate Limit Cap:
Unlimited
MFA Support:
Enforced via Zilliz Cloud console MFA and organization access policies
Enterprise SSO SAML:
Available on Enterprise with SAML SSO and dedicated clusters
Custom Domain Support:
Private cloud and hybrid deployments on Enterprise tiers
Supported SDKs:
Python, JavaScript, Go, Java, REST, gRPC, LangChain, LlamaIndex
Social Providers:
GitHub OAuth for Zilliz Cloud account login
Compliance Standards:
SOC 2 Type II, GDPR; HIPAA on Enterprise agreements
Webhook Fallback Policy:
Change data capture and bulk import with configurable retry on failed batches
Pinecone Vector Database
MFA Support:
Enforced via Pinecone console MFA and organization access policies
Enterprise SSO SAML:
Available on Enterprise with SAML SSO and private networking
Custom Domain Support:
Private endpoints and AWS PrivateLink on Enterprise deployments
Supported SDKs:
Python, Node.js, Java, Go, REST, LangChain, LlamaIndex integrations
Social Providers:
Google and GitHub OAuth for Pinecone console authentication
Compliance Standards:
SOC 2 Type II, GDPR, HIPAA BAA on Enterprise agreements
Webhook Fallback Policy:
Event-driven upserts via application queues with idempotent vector IDs
Milvus
Run Milvus Lite locally for prototyping, then migrate to Zilliz Cloud or self-managed Kubernetes for production scale.
Pinecone Vector Database
Partition namespaces per tenant or document collection to isolate RAG contexts and simplify bulk deletes during re-embedding jobs.