Pinecone Alternatives That Don't Suck

Started using Pinecone about 3 years ago because it was the path of least resistance. Good docs, worked out of the box, no Docker setup required. Classic build vs buy decision - we had a product to ship, not database expertise to build.

The Bill That Made Me Lose My Shit

So we're trucking along, embedding search is working fine, about 4M vectors in Pinecone. Bill was predictably around $180/month. Then we pushed a big content update, got up to around 10M vectors.

Next billing cycle: $847.32.

I stared at that invoice for like 10 minutes thinking it was a mistake. Called their support - nope, just crossed some magic threshold into "enterprise" pricing. No heads up, no email saying "hey you're about to get fucked," just surprise enterprise prices.

That's when I got stubborn and decided to test every vector database on the planet.

What Actually Drove Me Crazy

Query performance got shittier as we scaled. Started seeing 20-30ms responses at 2M vectors. By 8M vectors, we're hitting 60-100ms consistently. Their "auto-scaling" basically just spins up more pods that seem to hate talking to each other.

The API syntax is from another planet. Look at this garbage: filter={"$and":[{"genre":{"$in":["action","comedy"]}},{"year":{"$gte":2020}}]}. Every other database on earth uses SQL or at least normal REST queries. Who approved this filtering syntax?

Complete black box. When shit breaks, you get error messages like "Request failed with status 500." Cool, thanks. No logs, no metrics you can see, no way to tune anything. It either works or it doesn't.

What I Found After Testing Everything

Spent about 3 weeks going through Qdrant, Weaviate, ChromaDB, pgvector, even tried setting up FAISS from scratch (don't).

Qdrant was the winner - same 8M vector dataset, query times dropped to around 15-20ms. Turns out it wasn't our data or embeddings that were slow, it was Pinecone. Though I did hit a weird bug in Qdrant 1.7.3 where the REST API would randomly return 504s under heavy load. Fixed in 1.7.4 but caused a fun afternoon of debugging.

pgvector surprised me - if you're already running Postgres, just use this. Added it to our existing tables and had semantic search working in maybe an hour. Performance isn't amazing but it's totally fine for most apps. One gotcha: the HNSW index creation will lock your table for like 20 minutes on big datasets. Found that out the hard way during business hours.

ChromaDB works great until it doesn't - incredible developer experience, got a demo running in minutes. Then I tried it with real data and watched it consume 40GB of RAM before dying with some cryptic "index corruption" error. Turns out their persistent storage in version 0.4.x was basically broken with large datasets. They've since released 1.0+ which supposedly fixes a lot of this, but I haven't had time to test it thoroughly in production. Still using it for prototypes only.

The Migration Nightmare (And Happy Ending)

Getting data out of Pinecone is a pain in the ass. They export in some proprietary binary format that took me way too long to figure out. Ended up writing a shitty Python script to convert everything to standard vectors.

The actual migration to Qdrant took most of a weekend - not because Qdrant is hard, but because I kept second-guessing myself and testing different configurations.

Final result: went from ~$800/month to around $60/month in AWS costs. Same search quality, better performance, and I can actually see what's happening when things break.

Alright, let me tell you what I actually use now after trying everything and making some expensive mistakes.

This is the practical reality behind those comparison tables

• what it's actually like to run these things in production for months.

Qdrant: The One That Actually Works

Been running Qdrant in production for about 8 months now with around 10M vectors.

It's the first vector database that hasn't randomly broken on me.

What's good about it:

• Performance is solid
• went from 60-100ms queries with Pinecone to 15-25ms with Qdrant on the same data
• Docker setup was pretty straightforward, though their clustering docs are kind of scattered
• Written in Rust so it doesn't have the memory leaks I've seen with other stuff
• Filtering queries don't completely tank performance like they did with ChromaDB

The annoying parts: Their Python client had some weird async behavior in version 1.7.x that I spent a whole day debugging.

The async context manager would just hang indefinitely on certain operations. Upgrading to 1.8.0 fixed it but there was no warning in their changelog.

Also, their documentation assumes you understand vector databases already.

Good luck if you're new to this stuff. And Docker networking with their clustering setup is a pain

• spent way too long figuring out why node discovery wasn't working in Swarm mode.

Cost reality: Running on an r5.large in AWS, usually costs me around $42/month including EBS storage.

pgvector:

Sometimes Boring is Perfect

Look, if you're already running Postgres, just use pgvector.

Don't overthink it.

I added semantic search to our existing user-generated content by literally just adding a vector column and index.

Took maybe 3 hours including time to figure out the embedding pipeline.

Performance is... fine. Queries take 80-150ms depending on how complex your filters are.

Not amazing, but totally acceptable for most applications. And you get actual ACID transactions, which none of the fancy vector databases give you. Update: pgvector 0.8.0 came out recently with some big performance improvements

• haven't upgraded yet but AWS claims up to 9x faster queries.

The gotcha: PostgreSQL query optimization is basically black magic.

If you don't know how to tune Postgres properly, you're gonna have a bad time.

But if you're already running Postgres in production, you probably have someone who knows this stuff.

ChromaDB: Amazing Until It Isn't

ChromaDB has genuinely the best developer experience I've ever seen. pip install chromadb and you're running vector searches in literal minutes.

I use it for: Prototypes, demos, proving that vector search will work for a use case.

I don't use it for: Anything that matters.

Tried scaling it up to about 4M vectors and watched it consume all 32GB of my laptop's RAM before crashing with some cryptic error about index corruption.

This was on version 0.4.x

• they've since released 1.0+ which claims to fix these issues, but I haven't retested it with large datasets yet.

Their "distributed" mode is better documented now but still feels experimental.

Great for convincing your boss that semantic search is worth building. Terrible for actually building it.

Weaviate: Multi-Modal Magic, Configuration Hell

Weaviate actually has some legitimately cool features.

Their multi-modal search works

• you can search images, text, and audio all in one query.

That's genuinely impressive.

But they use GraphQL for everything, including database queries.

Why? I have no idea. Took me 2 solid weeks to figure out their query syntax, and I'm still not sure I'm doing it right.

Use case: Your app needs multi-modal search and you have time to learn their ecosystem.

I know a couple teams using it successfully but they all have dedicated engineers who just do Weaviate stuff.

Skip if: You want something that works like a normal database.

FAISS:

Speed Demon, Zero Features

FAISS will give you sub-5ms query times.

It's incredibly fast because it's just the core algorithm with no features, no persistence, no API

• nothing.

I spent about 3 months trying to build a production system around FAISS. Built custom indexing, API layers, backup systems, the whole thing.

It worked, but maintaining it was a nightmare.

Use it for: Research, custom applications where you need absolute maximum performance and have dedicated engineers to build everything else.

Don't use it for: Anything with deadlines or where you value your sanity.