The Case Against PGVector
65 comments
·November 3, 2025xfalcox
xfalcox
Also worth mentioning that we use quantization extensively:
- halfvec (16bit float) for storage - bit (binary vectors) for indexes
Which makes the storage cost and on-going performance good enough that we could enable this in all our hosting.
summarity
That's where it's at. I'm using the 1600D vectors from OpenAI models for findsight.ai, stored SuperBit-quantized. Even without fancy indexing, a full scan (1 search vector -> 5M stored vectors), takes less than 40ms. And with basic binning, it's nearly instant.
tacoooooooo
this is at the expense of precision/recall though isn't it?
tacoooooooo
for sure people are running pgvector in prd! i was more pointing at every tutorial
iterative scans are more of a bandaid for filtering than a solution. you will still run into issues with highly restrictive filters. you still need to understand ef_search and max_search_tuples. strict vs relaxed ordering, etc. it's an improvement for sure, but the planner still doesn't deeply understand the cost model of filtered vector search
there isn't a general solution to the pre- vs post-filter problem—it comes down to having a smart planner that understands your data distribution. question is whether you have the resources to build and tune that yourself or want to offload it to a service that's able to focus on it directly
cortesoft
I feel like this is more of a general critique about technology writing; there are always a lot of “getting started” tutorials for things, but there is a dearth of “how to actually use this thing in anger” documentation.
dpflan
What are you using it for? Is it part of a hybrid search system (keyword + vector)?
xfalcox
In Discourse embeddings power:
- Related Topics, a list of topics to read next, which uses embeddings of the current topic as the key to search for similar ones
- Suggesting tags and categories when composing a new topic
- Augmented search
- RAG for uploaded files
antirez
Redis Vector Sets, my work for the last year, I believe address many of such points:
1. Updates: I wrote my own implementation of the HNSW with many changes compared to the paper. The result is that the data structure can be updated while it receives queries, like the other Redis data types. You add vectors with VADD, query for similarity with VSIM, delete with VREM. Also deleting vectors will not perform just a thumbstone deletion. The memory is actually reclaimed immediately.
2. Speed: The implementation is fast, fully threaded reads, partially threaded writes: even for insertion it is easy to stay in the few hundreds of ops/sec, and querying with VSIM is like 50k ops/sec in normal hardware.
3. Trivial: You can reimplement your use case in 10 minutes including learing how it works.
Of course it costs some memory, but less than you may guess: it supports quantization by default, transparently, and for a few millions of elements (most use cases) the memory usage is very low, totally affordable.
Bonus point: if you use vector sets you can ask my help for free. At this stage I support people using vector sets directly.
VoVAllen
We at https://github.com/tensorchord/VectorChord solved most of the pgvector issues mentioned in this blog:
- We're IVF + quantization, can support 15x more updates per second comparing to pgvector's HNSW. Insert or delete an element in a posting list is a super light operation comparing to modify a graph (HNSW)
- Our main branch can now index 100M 768-dim vector in 20min with 16vcpu and 32G memory. This enables user to index/reindex in a very efficient way. We'll have a detailed blog about this soon. The core idea is KMeans is just a description of the distribution, so we can do lots of approximation here to accelerate the process.
- For reindex, actually postgres support `CREATE INDEX CONCURRENTLY` or `REINDEX CONCURRENTLY`. User won't experience any data loss or inconsistency during the whole process.
- We support both pre-filtering and post-filtering. Check https://blog.vectorchord.ai/vectorchord-04-faster-postgresql...
- We support hybrid search with BM25 through https://github.com/tensorchord/VectorChord-bm25
The author simplifies the complexity of synchronizing between an existing database and a specialized vector database, as well as how to perform joint queries on them. This is also why we see most users choosing vector solution on PostgreSQL.
nostrebored
So you’re quantizing and using IVF — what are your recall numbers with actual use cases?
VoVAllen
We do have some benchmark number at https://blog.vectorchord.ai/vector-search-over-postgresql-a-.... It varies on different dataset, but most cases it's 2x or more QPS comparing to pgvector's hnsw at same recall.
VoVAllen
And we do have user hosting 3 Billion vectors with Postgres + VectorChord with sharding. And they're using vectors to save the earth! Check https://blog.vectorchord.ai/3-billion-vectors-in-postgresql-...
tacoooooooo
We actually looked into vectorchord--it looks really cool, but it's not supported by RDS so it is an additional service for us to add anyways.
sgarland
> The problem is that index builds are memory-intensive operations, and Postgres doesn’t have a great way to throttle them.
maintenance_work_mem begs to differ.
> You rebuild the index periodically to fix this, but during the rebuild (which can take hours for large datasets), what do you do with new inserts? Queue them? Write to a separate unindexed table and merge later?
You use REINDEX CONCURRENTLY.
> But updating an HNSW graph isn’t free—you’re traversing the graph to find the right place to insert the new node and updating connections.
How do you think a B+tree gets updated?
This entire post reads like the author didn’t read Postgres’ docs, and is now upset at the poor DX/UX.
ayende
> maintenance_work_mem
That kills the indexing process, you cannot let it run with limited amount of memory.
> How do you think a B+tree gets updated?
In a B+Tree, you need to touch log H of the pages. In HNSW graph - you need to touch literally thousands of vectors once your graph gets big enough.
tacoooooooo
some fair points points on the specifics.
> maintenance_work_mem
sure, but the knob existing doesn't solve the operational challenge of safely allocating GBs of RAM on prod for hours-long index builds.
> REINDEX CONCURRENTLY
this is still not free not free—takes longer, needs 2-3x disk space, and still impacts performance.
> HNSW vs B+tree
it's not that graph updates are uniquely expensive. vector workloads have different characteristics than traditional OLTP, and pg wasn't originally designed for them
my broader point: these features exist, but using them correctly requires significant Postgres expertise. my thesis isn't "Postgres lacks features"—it's "most teams underestimate the operational complexity." dedicated vector DBs handle this automatically, and are often going to be much cheaper than the dev time put into maintaining pgvector (esp. for a small team)
alanwli
I've seen a decent amount of production use of pgvector HNSW from our customers on GCP, but as the author noted is not without some flaws and are typically in the smallish range (0-10M vectors) for the systems characteristics that he pointed out - i.e. build times, memory use. The tradeoffs to consider are whether you want to ETL data into yet another system and deal with operational overhead, eventual consistency, application-logic to join vector search with the rest of your operational data. Whether the tradeoffs are worth it really depends on your business requirements.
And if one needs the transactional/consistency semantics, hybrid/filtered-search, low latencies, etc - consider a SOTA Postgres system like AlloyDB with AlloyDB ScaNN which has better scaling/performance (1B+ vectors), enhanced query optimization (adaptive pre-/post-/in-filtering), and improved index operations.
Full disclosure: I founded ScaNN in GCP databases and currently lead AlloyDB Semantic Search. And all these opinions are my own.
riku_iki
AlloyDb is not opensource, so it is kinda different niche.
jjfoooo4
When using vectors / embeddings models, I think there's a lot of low hanging fruit to be had with non-massive datasets - your support documentation, your product info, a lot of search use cases. For these, the interface I really want is more like a file system than a database - I want to be able to just write and update documents like a file system and have the indexes update automatically and invisibly.
So basically, I'd love to have my storage provider give me a vector search API, which I guess is what Amazon S3 vectors is supposed to be (https://aws.amazon.com/s3/features/vectors/)?
Curious to hear what experience people have had with this.
rudderdev
As others have commented, all the mentioned issues are resolved, I will favour in using the PGVector. If Postgres can be a good choice over Kafka to deliver 100k events/sec [1], then why not PGVector over Chroma or other specialized vector search (unless there is a specific requirement that can't be solved wit minor code/config changes)!
tacoooooooo
how are all of the mentioned issues resolved?
bob1029
I'm still stuck on whether or not vector search (regardless of vendor) is actually the right way to solve the kinds of problems that everyone seems to believe it's great at.
BM25 with query rewriting & expansion can do a lot of heavy lifting if you invest any time at all in configuring things to match your problem space. The article touches on FTS engines and hybrid approaches, but I would start there. Figure out where lexical techniques actually break down and then reach for the "semantic" technology. I'd argue that an LLM in front of a traditional lexical search engine (i.e., tool use) would generally be more powerful than a sloppy semantic vector space or a fine tuning job. It would also be significantly easier to trace and shape retrieval behavior.
Lucene is often all you need. They've recently added vector search capabilities if you think you really need some kind of hybrid abomination.
clickety_clack
My default is basically YAGNI. You should use as few services as possible, and only add something new when there’s issues. If everything is possible in Postgres, great! If not, at least I’ll know exactly what I need from the New Thing.
Fripplebubby
The post is a clear example of when YAGNI backfires, because you think YAGNI but then, you actually do need it. I had this experience, the author had this experience, you might as well - the things you think you AGN are actually pretty basic expectations and not luxuries: being able to write vectors real-time without having to run other processes out of band to keep the recall from degrading over time, being able to write a query that uses normal SQL filter predicates and similarity in one go for retrieval. These things matter and you won't notice that they actually don't work at scale until later on!
simonw
That's not YAGNI backfiring.
The point of YAGNI is that you shouldn't over-engineer up front until you've proven that you need the added complexity.
If you need vector search against 100,000 vectors and you already have PostgreSQL then pgvector is a great YAGNI solution.
10 million vectors that are changing constantly? Do a bit more research into alternative solutions.
But don't go integrating a separate vector database for 100,000 vectors on the assumption that you'll need it later.
Fripplebubby
I think the tricky thing here is that the specific things I referred to (real time writes and pushing SQL predicates into your similarity search) work fine at small scale in such a way that you might not actually notice that they're going to stop working at scale. When you have 100,000 vectors, you can write these SQL predicates (return the 5 top hits where category = x and feature = y) and they'll work fine up until one day it doesn't work fine anymore because the vector space has gotten large. So, I suppose it is fair to say this isn't YAGNI backfiring, this is me not recognizing the shape of the problem to come and not recognizing that I do, in fact, need it (to me that feels a lot like YAGNI backfiring, because I didn't think I needed it, but suddenly I do)
throwway120385
Many of the concerns in the article could be addressed by standing up a separate PG database that's used exclusively for vector ops and then not using it for your relational data. Then your vector use cases get served from your vector DB and your relational use cases get served from your relational DB. Separating concerns like that doesn't solve the underlying concern but it limits the blast radius so you can operate in a degraded state instead of falling over completely.
SoftTalker
I've always tried to separate transactional databases from those supporting analytical queries if there's going to be any question that there might be contention. The latter often don't need to be real-time or even near-time.
esafak
Databases are hard to swap out when you realize you need a different one.
jeffchuber
Good article - the most use cases i see of pg_vector are typically “chat over their technical docs” - small corpus - doesn’t change often / can rebuild the index - no multi-tenancy avoids much of the issues with post-filtering
Chroma implements SPANN and SPFresh (to avoid the limitations of HNSW), pre-filtering, hybrid search, and has a 100% usage-based tier (many bills are around $1 per month).
Chroma is also apache 2.0 - fully open source.
chandureddyvari
Is there a comprehensive leaderboard like ClickBench but for vector DBs? Something that measures both the qualitative (precision/recall) and quantitative aspects (query perf at 95th/99th percentile, QPS at load, compression ratios, etc.)?
ANN-Benchmark exists but it’s algorithm-focused rather than full-stack database testing, so it doesn’t capture real-world ops like concurrent writes, filtering, or resource management under load.
Would be great to see something more comprehensive and vendor-neutral emerge, especially testing things like: tail latencies under concurrent load, index build times vs quality tradeoffs, memory/disk usage, and behavior during failures/recovery
riku_iki
> Is there a comprehensive leaderboard like ClickBench
clickbench has 100m rows of data only, which makes it not comprehensive benchmark at all.
IntrepidPig
> Post-filter works when your filter is permissive. Here’s where it breaks: imagine you ask for 10 results with LIMIT 10. pgvector finds the 10 nearest neighbors, then applies your filter. Only 3 of those 10 are published. You get 3 results back, even though there might be hundreds of relevant published documents slightly further away in the embedding space.
Is this really how it works? That seems like it’s returning an incorrect result.
> Nobody’s actually run this in production
We do at Discourse, in thousands of databases, and it's leveraged in most of the billions of page views we serve.
> Pre- vs. Post-Filtering (or: why you need to become a query planner expert)
This was fixed in version 0.8.0 via Iterative Scans (https://github.com/pgvector/pgvector?tab=readme-ov-file#iter...)
> Just use a real vector database
If you are running a single service that may be an easier sell, but it's not a silver bullet.