Cohere
Enterprise AI platform for private, secure LLM deployment with search and agentic models
FREEMIUM Verified JUL 3, 2026
Pinecone
Managed serverless vector database for semantic search, RAG, and recommendation systems
Pinecone is a fully managed, serverless vector database for semantic search, RAG, and recommendation systems. The Starter tier is free with 2GB storage and capped usage; a $20/month Builder tier raises limits; Standard starts at a $50/month minimum plus usage-based charges; Enterprise starts at $500/month with a 99.95% uptime SLA. Over 9,000 customers use it. Best for teams shipping AI retrieval without managing search infrastructure.
Verified JUL 12, 2026 USAGE-BASED Live
What is Pinecone?
Pinecone is a fully managed, serverless vector database built for AI applications that need to search through billions of items for similar matches in milliseconds. Instead of running and tuning your own search cluster, you push vector embeddings to Pinecone and query them by similarity, with storage separated from compute so indexes scale automatically without capacity planning. Its proprietary Rust query engine supports billions of vectors and thousands of queries per second, and newly written data becomes searchable within seconds.
Beyond raw vector storage, Pinecone bundles the pieces most retrieval systems need: hybrid search that blends dense vectors, sparse keywords, and full-text matching with metadata filtering; hosted inference for embedding and reranking models; namespaces for multitenancy; and integrations across the AI ecosystem, including MCP servers so agents can query it directly. It is a common backbone for retrieval-augmented generation (RAG), semantic search, agent memory, and recommendation engines. Pinecone reports over 9,000 customers and is backed by Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital.
Who is it for?
Pinecone fits teams building AI retrieval features who would rather consume a managed API than operate their own vector search infrastructure. The free Starter tier is enough to prototype a working RAG or semantic-search app, while the usage-based Standard and Enterprise tiers cover production scale, security, and compliance — at the cost of pricing that grows with query and storage volume.
- AI application developers building RAG pipelines who need reliable, low-latency retrieval without standing up and maintaining a search cluster.
- Startups and product teams that want to prototype on the free Starter tier and scale into usage-based billing only when traffic justifies it.
- Enterprises with compliance needs that require SAML SSO, RBAC, audit logs, customer-managed encryption keys, HIPAA, or a 99.95% uptime SLA.
- Agent and chatbot builders who need long-term memory and semantic context retrieval, integrated through SDKs or MCP servers.
How much does Pinecone cost?
Starting price: $20/mo · Free tier: yes · Model: usage-based
Pricing verified JUL 12, 2026
Price history tracked from June 2026
| Plan | Price | Includes |
|---|---|---|
| Starter | Free | Up to 2GB storage, 5 indexes, 100 namespaces per index · 2M write units and 1M read units per month · 1GB egress per month · Inference, Assistant, and dense/sparse/full-text indexes · Community Discord support, no credit card required |
| Builder | $20/mo | Flat monthly fee, everything in Starter · Up to 10GB storage, 10 indexes per project, 1,000 namespaces · 5M write units and 2M read units per month · Multiple projects and users · Prometheus and Datadog monitoring |
| Standard | $50/mo min + usage | $50/month minimum then pay-as-you-go on database, inference, and Assistant · Storage $0.33/GB/month; write units from $4/million; read units from $16/million · Dedicated Read Nodes, backup and restore, RBAC · SAML SSO and optional HIPAA compliance · 3-week trial with $300 in credits |
| Enterprise | $500/mo min + usage | $500/month minimum plus usage; higher per-unit rates than Standard · 99.95% uptime SLA and private networking · Customer-managed encryption keys and audit logs · HIPAA included, service accounts, Admin APIs · Pro support |
| Bring Your Own Cloud (BYOC) | Custom | Pinecone runs inside your own cloud account · Zero-access operations for data isolation · Custom pricing via sales · Pro support |
What are Pinecone's key features?
- Serverless indexes that separate storage from compute and scale automatically without capacity planning
- Hybrid search merging dense vector, sparse, and full-text methods with metadata filtering
- Built-in inference — embedding and reranking models hosted by Pinecone
- Namespaces for multitenancy and metadata TTL support
- Proprietary Rust query engine supporting billions of vectors and thousands of queries per second
- SDKs, REST APIs, and MCP (Model Context Protocol) servers for agent integration
- Backup and restore, RBAC, SAML SSO, and optional HIPAA compliance on paid tiers
What people use Pinecone for
- 01 Powering retrieval-augmented generation (RAG) so LLM apps answer from your own documents instead of hallucinating
- 02 Semantic search across product catalogs, documentation, or support tickets using vector embeddings
- 03 Long-term memory and context retrieval for AI agents and chatbots
- 04 Recommendation engines that surface similar items, articles, or users by embedding similarity
- 05 Hybrid search combining dense vectors, sparse keywords, and metadata filters in a single query
Pros and cons
| Pros | Cons |
|---|---|
| Fully managed and serverless — no clusters, sharding, or capacity planning to operate | Usage-based read/write/storage charges can climb unpredictably as query volume and index size grow |
| Free Starter tier with 2GB storage and no credit card is enough to prototype real RAG apps | Paid tiers carry monthly minimums ($50 on Standard, $500 on Enterprise) that outsized small workloads |
| Scales to billions of vectors with sub-second queries and a 99.95% SLA on Enterprise | Proprietary managed service — no open-source self-hosting; BYOC still runs Pinecone's stack, so there is vendor lock-in |
| Broad ecosystem: SDKs, framework integrations, hosted embedding/reranking, and MCP servers | Read units are the dominant cost driver at scale and are hard to estimate before launch |
What are the best Pinecone alternatives?
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How people make money with Pinecone
- Prototype RAG-powered products such as internal knowledge search or support copilots on the free Starter tier, then graduate to the usage-based Standard plan as query volume grows
- Offer vector-search and RAG implementation consulting — the serverless model lets client builds start on the free tier and scale into Standard or Enterprise billing as they reach production
Frequently asked questions
Is Pinecone free?
Yes. The Starter tier is permanently free with no credit card required. It includes up to 2GB storage, 5 indexes, 2M write units and 1M read units per month, plus inference and Assistant features. It is enough to build and test a real RAG or semantic-search prototype.
How much does Pinecone cost?
There are four self-serve tiers: Starter is free, Builder is a flat $20/month with higher limits, Standard has a $50/month minimum plus usage-based charges, and Enterprise has a $500/month minimum plus usage. Bring Your Own Cloud (BYOC) is custom-priced through sales.
How does Pinecone usage-based pricing work?
On Standard and Enterprise, you pay a monthly minimum and then pay-as-you-go for what you use. Charges are metered on storage (from $0.33/GB/month), write units (from $4/million on Standard), read units (from $16/million on Standard), and egress. Enterprise carries higher per-unit rates in exchange for its SLA and security features.
What is Pinecone used for?
Pinecone is a vector database used to store and search embeddings. Common uses are retrieval-augmented generation (RAG), semantic search over documents or products, agent memory, and recommendation systems where you need to find the most similar items to a query in milliseconds.
Can I self-host Pinecone?
No. Pinecone is a proprietary managed service, not open source. The closest option is Bring Your Own Cloud (BYOC), which runs Pinecone's software inside your own cloud account for data isolation, but it is still operated by Pinecone rather than fully self-hosted.
Does Pinecone support hybrid search?
Yes. Pinecone supports hybrid search that combines dense vector similarity with sparse keyword and full-text methods, along with metadata filtering, so you can blend semantic and exact-match retrieval in one query.
Does Pinecone have an API?
Yes. Pinecone is API-first, with REST APIs and official SDKs for Python, Node.js, and other languages, plus MCP servers for connecting AI agents. There is a web console for managing indexes and monitoring usage.