COMPARISON

The Pinecone alternative for RAG on Postgres

Pinecone is a managed vector database with serverless indexes, hosted embedding models, and Pinecone Assistant for chat over files. Powabase keeps your vectors in pgvector inside each project's own Postgres, right next to your app data, and adds document extraction, auth, storage, and agents. Want one backend for your whole RAG app? Pick Powabase. If a vector index is all you need, pick Pinecone.

Last reviewed: September 24, 2026

THE SHORT VERSION

Which one fits you.

Choose Powabase if…

  • You want embeddings in the same Postgres as your app data, so you can join them in SQL.
  • You want PDFs, Word, PowerPoint, Excel, and scanned images extracted and indexed for you.
  • You want auth, storage, REST APIs, and an agent runtime living in the same project as your search index.
  • You want to self-host the open-source stack, or keep that option open.

Choose Pinecone if…

  • You only need a vector index, and you already run your own app database and pipelines.
  • You need dedicated read capacity for high query volume on a very large index.
  • You want the data plane in your own AWS, GCP, or Azure account, managed by Pinecone.

COMPARISON TABLE

Powabase vs. Pinecone.

  • What it is[1][2][3]

    Powabase: A Postgres backend per project: database, auth, storage, RAG, and agents

    Pinecone: A vector database for AI agents and applications

  • Where vectors live[4][5][6]

    Powabase: pgvector in your project's Postgres, next to your app tables

    Pinecone: Pinecone indexes, apart from your app database

  • Filtering[7][8]

    Powabase: Metadata and source filters on search, plus SQL over the same database

    Pinecone: Metadata filter expressions

  • Document extraction[9][10][11]

    Powabase: Managed: PDF, Word, PowerPoint, Excel, and images via OCR

    Pinecone: Database: you extract text first. Assistant: PDF, DOCX, JSON, Markdown, and text files

  • Chunking and embeddings[4][5][10][12]

    Powabase: Managed on upload, with five indexing strategies

    Pinecone: Hosted embedding models; Assistant chunks and embeds its files

  • Retrieval[4][5][13]

    Powabase: Vector, full-text (BM25), hybrid, and tree search in one API

    Pinecone: Semantic, full-text (BM25), sparse-vector, and hybrid search

  • Agent runtime[12][14][15]

    Powabase: Managed ReAct agents with knowledge-base search, HTTP and MCP tools, sessions, and approval hooks

    Pinecone: Assistant chat grounded in your files, plus context snippets for agents you build

  • Auth, storage, REST APIs[1][2]

    Powabase: GoTrue auth, Storage, PostgREST, and Realtime in every project

    Pinecone: Not in scope; pair it with your own app backend

  • Workflows[16][17]

    Powabase: Block graphs started by API, webhook, or cron, with a copilot that drafts the graph

    Pinecone: Not in scope; the docs show Assistant inside n8n workflows

  • MCP server for coding agents[18][19]

    Powabase: Hosted, OAuth: SQL, auth users, storage, knowledge bases, agents, and workflows

    Pinecone: MCP server to search docs, manage indexes, and query data

  • Self-hosting[2][20]

    Powabase: Apache-2.0 stack, AI layer included, run with Docker Compose

    Pinecone: Managed service; BYOC runs the data plane in your own cloud account (Enterprise)

WHAT POWABASE ADDS

On top of the database.

  • Managed RAG

    Upload PDFs, Word, PowerPoint, Excel, or images. Powabase pulls out the text (with OCR for scans), generates the embeddings, and indexes them using one of five strategies. Search by vector, full-text, hybrid, or tree search, with an optional reranker on top.

    Read the docs →

  • An agent runtime

    Agents run a ReAct loop inside your project with eight built-in tools, your own HTTP tools, and MCP servers. They search your knowledge bases and keep session history, and a hook can hold a step until a person approves it.

    Read the docs →

  • Workflows

    Chain agents and other steps into a block graph, then start it by API, by webhook, or on a cron schedule. You can also describe what you want and let the copilot draft the graph.

    Read the docs →

RIGHT FIT

When Pinecone fits the job.

  • You only need a vector index. Your own app database, auth, and ingestion pipeline are already running, and what you want is a managed service to store and search vectors.
  • You serve a very large index at high query volume. Pinecone's dedicated read nodes give an index its own provisioned read hardware, which keeps latency predictable.
  • Your compliance team wants the data plane inside your own AWS, GCP, or Azure account, with Pinecone still handling upgrades and scaling. Pinecone offers that as BYOC on its Enterprise plan.
  • You want a hosted chat-over-files service with answer evaluation built in, which is what Pinecone Assistant does.

COEXISTENCE & MIGRATION

Use both, or move over.

Keep Pinecone, add Powabase

Keep your Pinecone indexes and run the rest of the backend on Powabase (Postgres, auth, storage, and agents). A Powabase agent can query Pinecone through a custom HTTP tool, alongside its built-in tools and your knowledge bases.

Migrate from Pinecone

There are two paths. The first is to re-index: upload the source documents to a Powabase knowledge base, and we extract, embed, and index them again. The second keeps your vectors. List the record IDs in each namespace, fetch the records, and write them into a pgvector column in your Powabase project over its Postgres connection, then query with the same embedding model so the vectors stay comparable.[9][21][22][23][14]

FAQ

Questions.

Yes, if you want your vectors next to your app data. Powabase stores embeddings with pgvector in each project's own Postgres and adds a BM25 index, hybrid and tree search, and an optional reranker. It also extracts your documents for you. For a standalone vector index and nothing else, Pinecone is built for exactly that.

For many RAG apps, yes. pgvector keeps embeddings in the same Postgres as your app, so you can filter and join them with SQL, and Powabase pairs it with BM25, hybrid search, and reranking. If you serve a very large index at high query volume, Pinecone's dedicated read nodes are built for that workload.

Yes. Pinecone indexes can hold dense vectors, sparse vectors, and full-text fields. To combine keyword and semantic signals, you use a text-match filter, a dense-plus-sparse query, or client-side reciprocal rank fusion. Powabase runs vector, BM25, and hybrid search over the same chunks in one API call.

Yes. Keep your Pinecone indexes and use Powabase for Postgres, auth, storage, and agents. A Powabase agent can call Pinecone through a custom HTTP tool, alongside its built-in tools and your Powabase knowledge bases.

No. Powabase stores embeddings with pgvector in your project's own Postgres, next to a BM25 index over the same chunks. Search by vector, full-text, hybrid, or tree search, and add the optional reranker when you want it. Your app data and your document index live in one database.

Upload your source documents to a Powabase knowledge base and we extract, embed, and index them again. Or keep the vectors you have. List and fetch the records from each Pinecone namespace, write them into a pgvector column over your project's Postgres connection, and keep using the same embedding model for queries.

Sources

  1. https://docs.powabase.ai/concepts/architecture: Each Powabase project gets its own Postgres database and service pods.
  2. https://github.com/powabase-ai/powabase: Powabase runs GoTrue, PostgREST, Storage, and Realtime on Postgres with pgvector, and is Apache-2.0 as a single-project Docker Compose stack that includes the AI service.
  3. https://docs.pinecone.io/guides/get-started/overview: Pinecone is the vector database for AI agents and applications.
  4. https://docs.powabase.ai/concepts/knowledge-bases-indexing: Indexing runs automatically when a source is added; five indexing strategies; embeddings in pgvector with a BM25 index; vector, full-text, hybrid, and tree search; optional cross-encoder reranking.
  5. https://docs.pinecone.io/guides/index-data/indexing-overview: Pinecone indexes hold dense-vector, sparse-vector, and full-text fields in namespaces, with integrated (hosted) embedding and metadata filtering.
  6. https://docs.pinecone.io/guides/index-data/migrate-from-pgvector: Moving from pgvector to Pinecone means copying vectors out of Postgres and syncing incremental changes to the index.
  7. https://docs.powabase.ai/api-reference/knowledge-bases: Powabase knowledge-base search accepts filter_metadata and source_ids.
  8. https://docs.pinecone.io/guides/search/filter-by-metadata: Pinecone narrows search results with metadata filter expressions.
  9. https://docs.powabase.ai/concepts/sources-extraction: Powabase extracts PDF, Word, PowerPoint, Excel, and images (via OCR).
  10. https://docs.pinecone.io/guides/get-started/quickstart/ingest-files: With Pinecone Database, you convert files to text with a parser of your choice outside Pinecone, then chunk, embed, and upsert.
  11. https://docs.pinecone.io/guides/assistant/files-overview: Pinecone Assistant supports PDF, DOCX, JSON, Markdown, and text files.
  12. https://docs.pinecone.io/guides/assistant/overview: Pinecone Assistant manages chunking, embedding, and storage, and answers chat with citations from your files.
  13. https://docs.pinecone.io/guides/search/hybrid-search: Pinecone hybrid search: a text-match filter on a dense search, dense plus sparse vectors in one index, or client-side reciprocal rank fusion.
  14. https://docs.powabase.ai/concepts/agents-tools: Powabase agents: ReAct loop, eight built-in tools, custom HTTP tools, MCP servers, sessions, hooks, and human approval.
  15. https://docs.pinecone.io/guides/assistant/context-snippets-overview: Pinecone Assistant returns context snippets to power your own LLM, RAG application, or agentic workflow.
  16. https://docs.powabase.ai/concepts/workflows-concept: Powabase workflows: block graphs with API, webhook, and schedule (interval or cron) triggers, plus a copilot.
  17. https://docs.pinecone.io/guides/assistant/quickstart/n8n-quickstart: Pinecone's docs show Assistant used inside an n8n workflow.
  18. https://powabase.ai/integrations/: The hosted Powabase MCP server at mcp.powabase.ai/mcp signs in with OAuth; the agent can run SQL, manage auth users and storage, and create and run knowledge bases, agents, orchestrations, and workflows.
  19. https://docs.pinecone.io/guides/operations/mcp-server: The Pinecone MCP server lets agents search docs, manage indexes, and query data.
  20. https://docs.pinecone.io/guides/production/bring-your-own-cloud: Pinecone BYOC deploys the data plane in your own AWS, GCP, or Azure account and requires the Enterprise plan.
  21. https://docs.pinecone.io/guides/manage-data/list-record-ids: List the record IDs in a Pinecone namespace.
  22. https://docs.pinecone.io/guides/manage-data/fetch-data: Fetch Pinecone records, with vector values and metadata, by ID.
  23. https://docs.powabase.ai/guides/user-pgvector: pgvector is preloaded in every Powabase project and can back your own embeddings tables.