Pinecone is a hosted database that stores embeddings (numeric representations of text, images or other data) and finds the most similar items quickly. Developers use it to give AI apps searchable memory, such as retrieving relevant documents before asking a model to answer. It also offers hosted embedding and reranking models and an Assistant product.
Searching large collections by meaning rather than exact keywords, without running your own vector infrastructure.
Why use Pinecone?
Practical benefits
No servers to manage
Free Starter plan
Built-in embedding, reranking and Assistant features
Enterprise SLA available
Who it suits
Developers building RAG apps
Teams adding semantic search
Companies wanting managed infrastructure
When another tool may fit better
Teams that must self-host open-source software
Very cost-sensitive large workloads that could self-host
Offline use
Learning curve
Some experience helps
Skills needed
Programming and understanding of embeddings.
Example workflows
Embed documents, upsert vectors into an index, query the index and pass results to an LLM
What can it do?
Serverless indexesUsage-based storage, reads and writes.
Hosted inferenceEmbedding and bge-reranker models; Starter includes 5M embedding tokens and 500 rerank requests monthly.
Pinecone AssistantDocument Q&A service with its own storage and token allowances.
Dedicated Read NodesDepends on planProvisioned read capacity on Standard and Enterprise.
Bring Your Own CloudDepends on planDeploy in your own cloud account; via sales.
Good for
RAG chatbots over company documents
Semantic product search
Recommendation systems
Deduplication
Where it runs
Web consoleREST API
Pricing and plans
Free plan + paid Starter is free (5 indexes, 2 GB storage, 2M write and 1M read units per month). Builder is $20/month flat. Standard has a $50/month minimum and Enterprise $500/month, with pay-as-you-go storage ($0.33/GB/month), writes and reads above that.