Pinecone
The leading managed vector database for AI applications
Pinecone is the most widely used managed vector database, purpose-built for AI similarity search. It handles billions of embeddings with sub-50ms latency and requires zero infrastructure management.
Editor rating is our overall editorial judgment of the tool, out of 5. It is not a test score and not a user-review average. Ratings have not been re-scored since the tool was listed here. The reasons are in Best for and Pros & cons below.
You can use this without coding, but there is some setup before it is useful.
Best for
Pros & cons
✓ Pros
✗ What this won't do
Use cases
Retrieval-augmented generation
Product recommendation engines
Semantic document search
Image similarity matching
Get an API key
Create a Pinecone account, copy your API key and store it in an environment variable named PINECONE_API_KEY. Then install the Python library with pip install pinecone.
Create an index
Call create_index_for_model with the embedding model llama-text-embed-v2. An index is where your searchable records live, and Pinecone turns your text into vectors for you.
Add records and search
Add a few records with upsert_records, each with an _id and some text. Then call search with a question in plain words, and it returns the closest records.
If search misses new records
Pinecone is eventually consistent, so new records take a few seconds to become searchable. Wait a moment and search again.
Compare alternatives
| Tool | Category | Editor rating | Pricing |
|---|---|---|---|
| Pinecone (current) | Vector DB | 4.6 | Free tier / from $20 mo |
| Weaviate | Vector DB | 4.5 | Free (open-source) / cloud from $45 mo |
| Qdrant | Vector DB | 4.5 | Free (open-source) / cloud from $25 mo |
| LangChain | Agent Framework | 4.6 | Free (open-source) / LangSmith from $39 mo |