pgvector vs Pinecone
Most RAG products do not need a new database. They need an extension on the one they already run.
The verdict
Choose pgvector when your data already lives in Postgres and the corpus is bounded. Choose Pinecone when scale, filtering latency or operational isolation force the issue. Most RAG products should start on pgvector, and many never leave.
We run pgvector in production across several Supabase-backed builds, so the case for starting there is not theoretical. It is the setup we reach for by default.
Pinecone is a real product with a real reason to exist. Past a certain scale, or under filtering-heavy queries at low latency, a dedicated vector database earns its keep. The mistake is reaching for it on day one because it is the name people search.
How we compared these, and our bias
Verified
State the bias first: we run Postgres by default, and pgvector is the extension we reach for on new RAG work. Read the pgvector column knowing that.
Every claim about Pinecone here comes from its own current documentation, not from our habits. Both products are mature and either can carry a serious RAG system. The question is what your corpus and query pattern actually need.
- Corpus size and growth
- How many vectors you store today, and whether that number is bounded or open-ended.
- Query pattern
- Whether searches filter heavily on metadata alongside the vector match.
- Operational burden
- What a small team has to run, monitor and scale, and whether it is one system or two.
- Documented vendor guidance
- What each project recommends for its own product, taken from current docs rather than third-party benchmarks.
The dimensions that change the decision
Checked against both projects' documentation on 14 August 2026.
| Dimension | pgvector | Pinecone |
|---|---|---|
| What it is | An open-source extension that adds vector columns and indexes to Postgres. | A managed, dedicated vector database, built and hosted as its own service. |
| Where your data lives | Beside the rest of your rows. One database, one connection string. | A separate service. Vectors and metadata live apart from your primary database. |
| Filtering alongside the vector search | Ordinary SQL. Join the vector match against any other table in the same query. | Metadata filters are built in and tuned for filtered search at scale. |
| Scaling the index | Scales with your Postgres instance. Large corpora need index tuning and enough memory. | Scales as a managed service. Pinecone's own guidance targets large, growing corpora with low query latency. |
| Operating it | One system to back up, monitor and patch, if you already run Postgres. | A second system: its own billing, its own outages, its own access controls to manage. |
| Getting started | Enable the extension, add a column, write a query. No new account. | New service, new API keys, and a data pipeline to keep it in sync with the source of truth. |
| Getting it wrong | An untuned index gets slow as the table grows and nobody notices until it does. | Paying for dedicated infrastructure a bounded, low-traffic corpus never needed. |
pgvector
Strong at
- No new system. Vectors sit next to the records they describe, in the database you already run.
- Ordinary SQL joins the vector search against permissions, tenants or any other table in one query.
- One thing to back up, monitor and secure instead of two.
- Nothing to sync. The embedding and the row it belongs to can never drift apart.
- Free to try, since it is an extension rather than a new account.
Weak at
- Index tuning is your job as the table grows, and an untuned index degrades quietly.
- Very large corpora put real load on a database that is also doing everything else.
- Scaling the vector search means scaling the whole Postgres instance, not just the search.
- Fewer vector-specific conveniences than a product built around search alone.
Pinecone
Strong at
- Built for one job, so its indexing and query path are tuned for vector search specifically.
- Metadata filtering at scale is a documented, first-class capability, not a workaround.
- Growth in corpus size is the service's problem to absorb, not yours to provision for.
- Isolates vector load from your primary database's performance.
Weak at
- A second system to operate, with its own outages and its own access model.
- Data has to be kept in sync between Pinecone and wherever the source records live.
- Ongoing cost for infrastructure a smaller or bounded corpus may never need.
- Joining a vector match against other business data means two round trips, not one.
- DocumentThe record you want to search.
- pgvectorEmbedding stored as a column, next to the row.
- PineconeEmbedding stored in a separate service, keyed back to the row.
- Filtered queryOne SQL join on pgvector. A tuned metadata filter on Pinecone.
- Corpus growsPostgres carries pgvector's load. Pinecone scales as a managed service.
Neither layout is wrong. The cost shows up later, when the corpus or the query pattern changes.
Rules that settle it
Start from where your data already lives and how big the corpus will realistically get. Vector search speed rarely decides this on its own, because both products are fast enough for most RAG workloads.
- 01Choose pgvector if your data already lives in Postgres and the corpus is bounded, such as one company's documents or one product's catalogue.
- 02Pick pgvector if the search needs to join against permissions, tenants or other business data in the same query.
- 03Choose Pinecone if the corpus is large and growing fast, with no natural ceiling.
- 04Pick Pinecone if queries filter heavily on metadata and that filtering has to stay fast as volume grows.
- 05Choose neither yet if you have not shipped a working RAG pipeline. The database rarely decides whether retrieval actually returns the right answer.
Common questions
01Do I need a vector database at all?
Not necessarily. If your data already lives in Postgres and the corpus has a natural size limit, pgvector answers the question without a new system. A dedicated vector database earns its place once scale or filtering demands outgrow what one Postgres instance can comfortably carry.
02Can you use pgvector and Pinecone together?
It is uncommon and rarely worth the cost. The usual pattern is starting on pgvector and migrating the vector workload to Pinecone later if it outgrows Postgres, not running both at once. Running both means two systems to keep in sync for one search feature.
03Is Pinecone faster than pgvector?
For very large, filter-heavy workloads, Pinecone's documentation targets low latency at a scale an untuned pgvector index will struggle to match. For a bounded corpus with light filtering, a properly indexed pgvector column is fast enough. The difference will not be what you notice in production.
04How hard is it to migrate from pgvector to Pinecone?
Moving the embeddings themselves is straightforward. The real work is a sync pipeline: Pinecone and Postgres no longer share a transaction, so something has to keep the two in step. Plan for that pipeline before the migration, not after.
05What size corpus makes pgvector the wrong choice?
There is no fixed number. It depends on your Postgres instance's resources, how well the index is tuned, and how much capacity your application needs from that same instance. The honest signal is a query pattern that visibly slows the database, not a vector count picked in advance.
Related
- best vector databases for RAG →Where pgvector and Pinecone rank against the rest of the field.
- Supabase, reviewed from production →The Postgres platform pgvector runs on in most of our builds.
- how to add RAG to an existing SaaS product →The pipeline this database decision sits inside.
- vector database →What a vector database is, for the reader arriving cold.
