Pinecone vs pgvector
Keep Postgres. This is about one workload, not your database. pgvector adds a vector type and vector indexes to Postgres, and where that stops being enough shows up in three places: how much memory the index needs, what happens to results when you filter, and how recall holds up as data changes.
Pinecone wins on filtered-query correctness, capacity that does not have to be predicted, and recall that holds as data changes. pgvector is the better choice when the corpus is small and mostly static, the index fits the memory you already pay for, and you would rather not run a second system.
The short version
pgvector is a reasonable choice when your vector workload is small, mostly static, and lives next to relational data you already keep in Postgres. Keeping one system is a real advantage, and for a prototype or a modest corpus it works.
Pinecone is the better fit once the workload grows, changes continuously, or depends on metadata filters returning complete results. pgvector's HNSW index has to fit in memory to build and query at speed, its filtering runs after the search rather than inside it, and its IVFFlat index loses recall as data drifts. Those are architectural properties of bolting vector search onto a row store, not tuning mistakes.
One thing pgvector has that Pinecone does not, and it is worth weighing honestly: Postgres brings decades of operational maturity with it. Point-in-time recovery, streaming replication, mature backup tooling, a deep access-control surface, and a hiring pool that already knows how to run all of it. Moving retrieval out means running a second system that has its own answers to those questions. For some teams that trade is not worth making, and the rest of this page is the case for when it is.
When to pick which
Both systems have workloads they suit. This is where each one is the better answer.
Choose Pinecone when
- Your corpus grows past the point where an in-memory index is affordable, or you cannot predict its final size.
- You filter on metadata and need a full set of top_k results every time.
- Vectors are inserted, updated, and deleted continuously rather than loaded once.
- You would rather not own index sizing, rebuilds, and query-plan tuning.
- You need sparse and dense retrieval, reranking, or embedding generation in one system.
Choose pgvector when
- You want retrieval inside the database that already holds your data, with the backups, point-in-time recovery, replication, and access control you already run and already trust. That is a real advantage and Pinecone does not offer it.
- You need vectors and rows in the same transaction. A vector query cannot join against your relational tables from outside the database, and if that join has to be transactional, pgvector is the correct answer.
- Your corpus is small and mostly static, and the index fits the memory you already pay for. HNSW on Postgres performs well under those conditions and the operational simplicity is genuine.
- Vector search is a secondary feature next to relational queries and you would rather run one database than two.
- Your team already operates Postgres well, and the cost of a second system to run outweighs what better retrieval would buy you.
Feature by feature
How Pinecone and pgvector differ across the capabilities that usually decide the choice.
| Capability | Pinecone | pgvector |
|---|---|---|
| Deployment model | PineconeYou create an index and write to it. Storage sits on object storage, queries run on executors Pinecone operates, and there is no instance, node count, or cluster topology to choose. | pgvectorAn extension you install into a Postgres instance you run or rent. You size it. |
| Index memory sizing | PineconeNot a user concern. You pay for data stored and operations run. | pgvectorThe HNSW index should fit in working memory. In Pinecone's April 2024 benchmark across four public datasets, index memory ran between 1.2x and more than 5x the raw dataset size. |
| Behavior when the index exceeds memory | PineconeNot applicable. Vectors live on object storage and are cached by query executors. | pgvectorThe build spills to disk. In the same benchmark, build throughput dropped by more than 10x past that point. |
| Metadata filtering | PineconeFilters are applied as part of retrieval. A top_k request comes back filtered. | pgvectorApplied after the index scan by default. A selective filter can therefore return fewer results than requested. Since 0.8.0 an opt-in iterative scan keeps scanning until it has enough, up to a tuple limit. |
| Query path predictability | PineconeOne retrieval path. | pgvectorThe Postgres planner chooses between the vector index, a GIN index, and a sequential scan. Similar-looking queries can take different paths. |
| Recall under changing data | PineconeBackground compaction re-optimizes continuously. No re-indexing step. | pgvectorIVFFlat partitions are built once. In the same benchmark, recall fell as data arrived after the index was built. |
| Scaling model | PineconeBillions of vectors. | pgvectorBounded by instance memory. Growing past it means a bigger instance or sharding you implement. |
| Embedding dimensions | PineconeUp to 20,000 dimensions for dense vectors, with no customer-side quantization needed to get there. | pgvectorHNSW indexes stop at 2,000 dimensions. That is a fixed constant in the source rather than a setting. A 3,072-dimension model cannot be indexed at full precision. The routes past it are halfvec, binary quantization, or reducing the dimensions. |
| Multi-tenancy | PineconeNamespaces inside one index, with no per-tenant provisioning. | pgvectorA tenant column and a filter, a table per tenant, or a schema per tenant. Each is a design you choose and index yourself, and the filtering behavior above applies to the first. |
| Running it in your own cloud | PineconeBring your own cloud on AWS, Azure, and GCP. | pgvectorAlready there. pgvector runs inside the Postgres you operate, which is the strongest version of this and a real reason to stay. |
| Hybrid and sparse retrieval | PineconeNative dense and sparse indexes, plus reranking models. | pgvectorA sparsevec type and a bit type alongside dense vectors, plus Postgres full-text search for the keyword side. Combining them into one ranked result is a fusion step you write. |
| Licensing and source | PineconeCommercial managed service. | pgvectorOpen source under the PostgreSQL License, running inside a database that is also open source. |
| Embeddings and reranking | PineconePinecone Inference hosts embedding and reranking models alongside the index. | pgvectorBring your own models and pipeline. |
| Pricing model | PineconeUsage based. You pay for storage and the reads and writes you run. | pgvectorInstance and storage you provision, billed whether or not it is busy. |
What we measured
We ran Postgres with pgvector against four public datasets and published the results. The numbers below come from that benchmark.
1.2x to 5x+
index memory relative to raw dataset size, varying by dataset, which makes capacity planning hard to predict up front
10x+
drop in index build throughput once the HNSW graph no longer fits in working memory
1 of 10
results returned by a filtered query in one tested case even after raising the candidate count to its maximum, because HNSW filtering is applied after the search. At the default setting the same query returned nothing
1.5x to 2.9x
lower ongoing monthly cost for Pinecone Serverless across the four tested datasets, or 1.1x to 2.2x including the one-off initial upsert
Datasets: mnist, nq-768-tasb, yfcc (10M), and cohere (10M). Cost modeled as a full upsert, an average of 10 queries per minute, and 10 percent of the dataset modified each month. The pgvector side is priced as the EC2 instance needed to hold p95 query latency under 100ms, plus EBS. These runs date from April 2024 and predate pgvector 0.8.0, which added iterative index scans and better cost estimation for filtered queries. The section below on filtering covers what that changed. The claims on this page were last checked against pgvector 0.8.6, the current release at the time of review.
Source: Pinecone vs. Postgres pgvector: For vector search, easy isn't so easy (April 2024). Results depend on dataset, query pattern, and configuration. Run your own workload before making a decision on these numbers.
Index memory is the constraint you cannot see coming
pgvector builds its HNSW graph in memory. To keep builds and queries fast, that graph needs to fit in the memory you have provisioned. The required amount depends on the data, not just its size on disk.
In Pinecone's benchmark the ratio of index memory to raw dataset size ranged from about 1.2x on one dataset to more than 5x on others. There is no simple formula that tells you which one you have before you load the data. Guess low and the index spills to disk, where measured build throughput fell by more than 10x.
The failure mode this produces is delayed. An application ships with acceptable latency, data accumulates, the index crosses the memory line, and query latency degrades sharply. The fix at that point is a larger instance or sharding the database.
pgvector ships two ways to shrink that footprint, and its own scaling guidance names both. The halfvec type stores each dimension in two bytes instead of four. An expression index over binary_quantize() cuts it further, with a reranking pass against the original vectors to recover the recall that quantization costs. Both are worth trying before concluding the index will not fit, and the benchmark numbers above were measured on the full-precision vector type rather than either of them.
What neither changes is who does the work. You are choosing the representation, tuning how deep the reranking goes, and re-validating recall as the data drifts. Pinecone selects and revises quantization internally during compaction.
Filtering after the search changes what you get back
Most retrieval applications filter. Restrict to a tenant, a document set, a date range, a language.
pgvector's HNSW index does not apply those predicates inside the index traversal. Postgres searches the vector index first and applies the WHERE clause to what comes back. When the filter is selective, most candidates are discarded and the query returns fewer than the requested top_k. In one query from Pinecone's 2024 test set it returned nothing at all, and raising the candidate count to its maximum recovered a single result out of ten.
Postgres offers a GIN index as a way to filter first and then rank, which works when the planner chooses it. In the same test set the planner sometimes used the vector index instead and returned partial results, and in another case skipped both indexes and ran a sequential scan that took several seconds. Similar-looking queries took different paths.
pgvector has since added a mitigation. Version 0.8.0, released in October 2024 after the benchmark above, introduced iterative index scans: when a filtered query comes up short, pgvector can keep scanning the index until it has enough matches. It is off by default and enabled per session with the hnsw.iterative_scan setting, it stops at a configurable tuple limit rather than guaranteeing a full result set, and the relaxed-ordering mode trades exact distance ordering for recall. The same release also improved cost estimation for index selection when filtering.
That narrows the gap and is worth knowing about if you are staying on pgvector. It does not remove the underlying difference. Filtering is still applied outside the index traversal, getting complete results is still a setting you have to know exists and tune, and the ceiling is a tuple budget rather than a guarantee. In Pinecone, filters are part of retrieval and a filtered query returns a full set of top_k results with no configuration.
One caveat on our own side, since this page holds competitors to their published numbers. The Pinecone claim above is about mechanism, not measurement: we publish no recall-versus-selectivity curve. Treat it as a description of how the search works rather than as a result you can hold us to. Our filter surface also has its own limits, with $in and $nin capped at 10,000 values and metadata capped at 40 KB per record. Run your own filter distribution against both before deciding.
IVFFlat trades recall for memory, and drift takes more of it
pgvector's other index type, IVFFlat, needs far less memory than HNSW. It clusters vectors into partitions when the index is built.
Those partitions reflect the data that existed at build time. Vectors added afterward are assigned to partitions that were drawn without them, and Pinecone's tests measured recall dropping across every dataset once data had drifted from the initial build. Recovering it means rebuilding the index.
That constraint rules IVFFlat out for most production retrieval, where documents are added and updated continuously.
Most vector benchmarks do not look like production
Published vector benchmarks tend to query a frozen corpus one client at a time. HNSW does well on that test and the results are real. Most applications run a different workload: concurrent traffic against a corpus that is still being written to.
Two things change once traffic is concurrent and the corpus is still being written to. Inserting into an HNSW graph is itself a sequence of approximate searches across the graph layers, which puts ingest in competition with queries for the same memory and the same CPU. On a single Postgres instance, queries, ingest, index rebuilds, and autovacuum all contend for that one machine. Read replicas move some read traffic off the primary, at the cost of operating them and of the lag they carry.
The effect shows up in the tail rather than the median. A p50 that looks healthy can sit in front of a p99 that has climbed into seconds, and the tail is what users feel. Pinecone separates storage from compute and isolates reads onto their own capacity. Ingest does not compete with the query path.
What the operational difference costs
The comparison is often framed as one system versus two. That framing undercounts what running pgvector at scale involves: predicting index memory, monitoring for the point where it stops fitting, choosing between two index types with different failure modes, adding GIN indexes to make filters behave, and checking query plans when results look wrong.
Pinecone's benchmark also modeled direct cost across the four datasets. Pinecone Serverless came out between 1.5x and 2.9x cheaper on ongoing monthly cost, and between 1.1x and 2.2x cheaper once the one-off cost of the initial upsert is included. Those figures exclude the cost of operating Postgres itself.
Idle capacity is the other half of the bill. A Postgres instance sized for peak index memory is paid for around the clock, whether or not anyone is querying it. Pinecone charges for what you query by default, and where traffic is sustained enough to justify provisioned capacity, Dedicated Read Nodes trade per-query cost for fixed read capacity. Pinecone published three production workloads that moved onto that path: 77 percent lower cost on a billion-vector index at low query volume, 83 percent on a latency-sensitive workload, and 97 percent on a sustained high-QPS one.
Moving from pgvector
The path is a read out of Postgres and an upsert into Pinecone. Pull the vectors and the metadata alongside them, then write them into an index. For a large table, bulk import from object storage avoids pushing everything through the API batch by batch. The guide covers the whole path, including how a pgvector column and its metadata map onto a Pinecone index and namespace.
Talk to an engineer or read how Pinecone works.
Frequently asked questions
Usually yes. Postgres stays the system of record for relational and transactional data. Pinecone holds the vectors and the metadata you filter on at query time, and returns identifiers your application resolves back to Postgres rows.
For the retrieval workload, yes, and the honest scope is narrower than the question implies. Pinecone replaces the vector search path rather than Postgres. If your corpus is small and mostly static and the index fits the memory you already pay for, pgvector is a good answer and staying put is reasonable. The case for moving is the three things this page covers: index memory you have to predict in advance, filtering applied after the search rather than inside it, and recall that drifts as data changes.
For vector search, yes. Postgres has no native vector type. pgvector is the extension that adds one, and it is what managed Postgres providers mean when they advertise vector support. Comparing Pinecone against Postgres for retrieval is a comparison against Postgres running pgvector, which is what this page covers.
It can be, for small and mostly static vector workloads that sit next to relational data. The limits show up as the corpus grows and changes. The HNSW index needs to fit in memory to stay fast, metadata filters are applied after the search so they can return fewer results than requested, and the IVFFlat index loses recall as data drifts from the point the index was built.
pgvector's HNSW index does not evaluate metadata predicates during the index traversal. Postgres retrieves nearest neighbors first, then applies the WHERE clause to that candidate set. If the filter is selective, most candidates are removed and you get fewer than top_k results. There are two mitigations. A GIN index on the filtered column can make Postgres filter first, when the planner chooses that path. Since version 0.8.0 you can also enable iterative index scans with the hnsw.iterative_scan setting, which keeps scanning until it finds enough matches, up to a configurable tuple limit. Both are opt-in and neither guarantees a complete result set.
It depends on the dataset, not only its size. In Pinecone's published benchmark the index needed roughly 1.2x the raw dataset size on one dataset and more than 5x on others. Because the ratio varies, the requirement is difficult to predict before loading the data.
Yes. Read the vectors and their metadata out of Postgres and upsert them into a Pinecone index. Pinecone also supports bulk import from object storage for large datasets. Your Postgres database stays in place for relational data. The migration covers the retrieval path only.
They help, and they are worth trying before you migrate anything. StreamingDiskANN reduces the memory pressure that breaks plain HNSW at scale, and quantization buys headroom at some cost to recall. Check pgvectorscale's commit history before you make it a production dependency, though: its last functional release was November 2025, the commits since have been a copyright-year bump and a style fix, and its own README still describes the project as early stage. What none of them changes is the shape of the problem. You are still choosing an algorithm, tuning its parameters, sizing an instance around it, and re-tuning as the data drifts, while queries, ingest, and reindexing compete for the same machine.
Not in a single statement. Pinecone is reached over an API rather than SQL. The usual pattern is to retrieve IDs and scores from Pinecone and hydrate the rest from Postgres. Most applications already work that way, because the embedding rarely sits beside everything the response needs. If your retrieval genuinely requires a transactional join across relational and vector data in one statement, keep it on pgvector.
The two most-cited ones are from October 2023 and June 2024, and both ran against Pinecone's pod-based indexes, which are a retired product line. Neither tested serverless, and neither could have tested Dedicated Read Nodes, which reached general availability in April 2026. The question worth asking of any benchmark in this category is which product it measured and when. That standard applies to ours as well: the figures on this page are from April 2024 and predate pgvector 0.8.0, which is why the method note says so.
In Pinecone's published benchmark across four datasets, Pinecone Serverless was 1.5x to 2.9x cheaper on ongoing monthly cost, and 1.1x to 2.2x cheaper including the one-off initial upsert. The pgvector side was priced as the instance required to keep p95 query latency under 100ms, plus storage. That comparison excludes the staff cost of operating Postgres. Your own result depends on your data volume and query rate. The larger variable is usually idle capacity, because an instance sized for peak index memory is paid for around the clock. Separately, and this is a Pinecone-to-Pinecone figure rather than a comparison against pgvector, three published production workloads that moved from on-demand querying onto Dedicated Read Nodes cut cost by 77, 83, and 97 percent. Two constraints belong in the same breath, because they are easy to miss when the savings are quoted alone: Dedicated Read Nodes support one namespace per index today, which means the multi-tenancy argument elsewhere on this page and this pricing tier do not currently combine, and their shards and replicas are sized by hand rather than automatically.
Sources
- Pinecone vs. Postgres pgvector: For vector search, easy isn't so easy
- pgvector on GitHub, including iterative index scans and changelog
- Dedicated Read Nodes: now generally available
- How Pinecone works
Claims about pgvector last checked against its own documentation on . Competitors ship changes we do not control. If something here is out of date, tell us and we will correct it.
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