Pinecone
FreemiumThe vector database for machine learning.
Published 27 September 2026
Scores
Popularity4/5
The default managed vector database referenced in nearly every RAG tutorial and framework integration guide, though vector databases themselves remain a specialty most teams outside AI/ML work never need to touch.
Learning Curve2/5
Creating a serverless index and querying it via the API or SDK requires minimal setup, with Pinecone Inference removing the need to separately wire up an embedding provider for a first RAG pipeline.
Flexibility3/5
Supports dense, sparse, and full-text indexes, Dedicated Read Nodes for read-heavy workloads, and Bring Your Own Cloud, though it remains a single-purpose vector database rather than a general-purpose one.
Performance4/5
The serverless architecture and Dedicated Read Nodes are built for production-scale retrieval workloads, with high-availability SLAs on paid tiers backing that up.
Portability2/5
Closed-source, fully managed SaaS with no self-hosted option, meaningful vendor lock-in compared to open-source vector databases that can run anywhere.
About Pinecone
Pinecone is a fully managed, serverless vector database for storing and querying high-dimensional embeddings at scale. Serverless is the default for every new index: instead of provisioning and paying for idle compute pods, billing is based on read units, write units, and storage actually consumed, letting a project store billions of vectors without any server management. It supports dense, sparse, and full-text indexes side by side, with namespaces for multi-tenant isolation.
Beyond raw vector storage, Pinecone bundles Pinecone Inference, hosted embedding and reranking models built into the query pipeline so a separate embedding provider is not required, and Pinecone Assistant, a higher-level toolkit for building chat and RAG (retrieval-augmented generation) applications on top of an index. Dedicated Read Nodes serve read-heavy production workloads, and a Bring Your Own Cloud option runs the data plane inside a customer's own cloud account for data-residency requirements.
Pricing has four tiers. The free Starter tier is usable for real prototypes, with a few gigabytes of storage, several indexes, and Inference and Assistant allowances included. Builder is a flat monthly plan for solo developers and small teams, Standard adds pay-as-you-go usage above a monthly minimum with all cloud regions, SSO, and RBAC, and Enterprise adds an uptime SLA, private endpoints, customer-managed keys, and audit logs. Pinecone is closed source with no self-hosted deployment, unlike open-source vector database alternatives.
Key Features
- Serverless indexes billed on read units, write units, and storage
- Dense, sparse, and full-text indexes side by side
- Pinecone Inference: hosted embedding and reranking models in the query pipeline
- Pinecone Assistant: toolkit for chat and RAG applications
- Dedicated Read Nodes for read-heavy production workloads
- Bring Your Own Cloud deployment on Enterprise
- Available across AWS, Azure, and GCP regions
Pros
- Usable free tier with Inference and Assistant included, not a short trial
- Serverless architecture removes idle-compute cost and capacity planning
- Bundled Inference and Assistant reduce the number of services a RAG pipeline needs
- Broad integration coverage across LangChain, LlamaIndex, and other AI frameworks
- Flat-priced Builder tier gives small teams a predictable bill
Cons
- Closed source with no self-hosted deployment option
- Usage-based pricing on Standard and above is harder to predict than flat-rate plans
- SLA, private endpoints, and audit logs require the Enterprise minimum spend
- Single-purpose vector store, so application data still needs a separate primary database
Pinecone Pricing
Freemium- · 2GB storage, up to 5 indexes with 100 namespaces each
- · On-demand database, Inference, and Assistant included
- · 2M writes/month, 1M reads/month, community Discord support
- · Flat monthly price, 10 indexes per project, 1,000 namespaces per index
- · 10GB storage, 5M writes/month, 2M reads/month
- · Up to 5 projects and 5 users, Prometheus/Datadog monitoring
- · $50/month minimum with pay-as-you-go usage, 20 indexes per project
- · All cloud regions, Dedicated Read Nodes, backup and restore, RBAC, SSO
- · Storage at $0.33/GB/month; HIPAA available as an add-on
- · $500/month minimum, 200 indexes per project
- · 99.95% uptime SLA, Bring Your Own Cloud, private endpoints
- · Customer-managed encryption, audit logs, HIPAA, pro support
Tech Stacks with Pinecone
Python RAG App
ProjectA retrieval-augmented generation (RAG) app in Python. A framework such as LlamaIndex or LangChain loads, splits, and indexes your documents, a vector store such as PostgreSQL with pgvector or Qdrant finds the passages that match a question, FastAPI serves the answers, and a model such as Claude or OpenAI writes them from what was retrieved.
Self-Hosted AI with Ollama and Open WebUI
InfrastructureA private, ChatGPT-style assistant on hardware you own. Ollama runs open-weight models such as Google Gemma or Qwen, and Open WebUI gives them a chat interface in the browser with accounts, document search, and model management. Both run in Docker, and nothing leaves your machine.
n8n AI Agent
ProjectBuild an AI agent in n8n's visual editor. The AI Agent node connects a model such as Claude or OpenAI to tools, memory, and a chat window, and the agent decides which tool to call for each request. The tools are n8n's own integrations, your other workflows, and MCP servers, so there is no backend code to write.
Tools Related to Pinecone
Works well with Pinecone(1)
AnythingLLM documents Pinecone as a supported vector database backend for its per-workspace RAG configuration.
Integrates with Pinecone(2)
LangChain's langchain-pinecone partner package wraps Pinecone as a vector store, a common managed retrieval backend for LangChain RAG applications.
LlamaIndex's llama-index-vector-stores-pinecone package stores and queries embeddings in Pinecone, a common managed retrieval backend for LlamaIndex RAG applications.
Alternatives to Pinecone(3)
Both are vector databases used for RAG and semantic search. Pinecone is closed-source, fully managed SaaS only, while Qdrant offers a genuine self-hosted, open-source (Apache 2.0) option alongside its managed cloud — a common deciding factor between the two.
Both are vector databases used for RAG and semantic search. Pinecone is closed-source, fully managed SaaS only, optimized for production scale, while Chroma is open-source and self-hostable with a minimal API optimized for fast prototyping.
Pinecone is a fully managed vector database with no infrastructure to run; PostgreSQL with pgvector stores vectors beside relational data in a database you operate yourself. Pinecone scales further with less work, PostgreSQL avoids a second system.
Vendor
Pinecone
Website →Tags
Details
- Maintained
- Yes
- DB model
- Vector
- Query language
- Custom
- Hosting
- Cloud managed
- ACID compliant
- No
- Replication
- Yes