Pinecone vs SingleStore: Vector Databases Comparison
Compare Pinecone and SingleStore side by side. Both are tools in the Vector Databases category.
Updated March 1, 2026
The short answer
Choose Pinecone if industry-leading managed vector database with zero infrastructure overhead.
Choose SingleStore if unified platform combining transactional, analytical, and vector workloads.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Pricing | Freemium | — |
| Best For | Engineering teams building production AI applications that need managed, scalable vector search | — |
| Website | pinecone.io | singlestore.com |
| Key Features |
| — |
| Use Cases |
| — |
Pros & Cons: Pinecone vs SingleStore
- +Industry-leading managed vector database with zero infrastructure overhead
- +Sub-10ms query latency at billion-vector scale
- +Hybrid search combining dense and sparse vectors
- +Strong ecosystem integrations with LangChain, LlamaIndex, and more
- +Generous free tier for prototyping and development
- −SaaS-only with no self-hosting option
- −Costs can escalate significantly at large scale
- −Eventual consistency model may not suit transactional systems
- −Free tier limited to US-East region only
- +Unified platform combining transactional, analytical, and vector workloads
- +Co-located vector data with relational data for powerful SQL queries
- +Multiple vector index types (FLAT, IVF_FLAT, IVF_PQ, HNSW_FLAT, HNSW_PQ)
- +MySQL and MongoDB wire protocol compatibility for easy integration
- +Proven at scale with 100+ Fortune 500 customers
- −More complex than dedicated vector databases for pure vector search use cases
- −Pricing not transparently published
- −May have higher learning curve for teams unfamiliar with distributed SQL
- −Recent acquisition may lead to product changes
Pricing: Pinecone vs SingleStore
- · Up to 5 indexes
- · 2 GB storage
- · 2M write units/month
- · 1M read units/month
- · Serverless architecture
- · Multiple regions
- · Higher storage and throughput
- · Pay-per-use pricing
- · Dedicated infrastructure
- · SSO/SAML
- · SOC 2 Type II
- · Custom SLAs
When to Choose Pinecone vs SingleStore
- Production RAG pipelines
- Semantic search at scale
- Recommendation systems
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Pinecone
Pinecone is the most widely used managed vector database, purpose-built for similarity search and retrieval-augmented generation (RAG). Founded in 2019 by Dr. Edo Liberty, former Head of Amazon AI Labs at AWS, Pinecone offers serverless and pod-based architectures supporting billions of vectors with single-digit millisecond query latency.
The platform provides metadata filtering, namespaces, and hybrid search combining dense and sparse vectors. Its managed service eliminates infrastructure complexity, making it the go-to choice for teams building semantic search, recommendation engines, and RAG-powered AI applications.
Headquartered in New York City with 138 employees, Pinecone has raised $138M in total funding including a $100M Series B at a $750M valuation. The company serves over 4,000 customers and is rated 4.7/5 on G2.
About SingleStore
SingleStore is a real-time, unified, distributed SQL database that combines transactional, analytical, and vector data workloads in a single platform. MySQL and MongoDB wire protocol-compatible, SingleStore enables organizations to scale from one to one million customers, handling SQL, JSON, full text, and vector workloads all in one unified platform. Unlike traditional vector databases, SingleStore stores vector data in relational tables alongside other types of data, allowing easy querying of extended metadata and other attributes with the full power of SQL. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ vector indexes, with dot product and Euclidean distance for similarity matching. Founded in 2011 and headquartered in San Francisco, SingleStore serves hundreds of customers including 100+ Fortune 500, Forbes Global 2000, and Inc. 5000 brands. The company was acquired by Vector Capital in September 2025.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
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