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StackGrep Engine vs Pinecone

Pinecone is a managed vector database for semantic search over embeddings. StackGrep Engine is something else: exact text and regex search over your own documents, built for AI agents, with every match counted and your index kept in object storage.

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What StackGrep does differently

Finds exact text and patterns, not passages that are merely similar
No embedding model, chunking or re-embedding when documents change
Counts every match, not just the top k
Quotes the matching line, so agents can check their answers

When Pinecone is the better pick

If your questions are about meaning (“find support tickets like this one”) rather than exact words, names or IDs.

Three calls to try it

POST /api/collections/docs/docs     {"docs": [{"id": "a1", "text": "...", "meta": {"team": "ops"}}]}
GET  /api/collections/docs/search?q=net+30&filter=team:ops
GET  /api/collections/docs/count?q=net+30

Quickstart →

Questions

Should I replace my vector database?

Not necessarily. Vectors are good for “like this”; StackGrep is good for “exactly this” and “how many”. Plenty of agents use both.

Is exact search enough for RAG?

For many lookups, yes: coding agents work this way, searching and reading in a loop. See RAG without embeddings.

Other comparisons

Elasticsearch alternative Algolia alternative Typesense alternative Meilisearch alternative turbopuffer alternative

Use cases

Hosted grep for your AI agents Exact counts for RAG and agents Exact match search for AI agents RAG without embeddings Grep an S3 bucket: search the contents of every file A regex search API that doesn't scan everything A knowledge base MCP server for your documents Find every contract that says exactly this

Give your agents exact search

StackGrep Engine is onboarding teams from the waitlist. Bring your documents or your bucket.

Join the waitlist Read the docs