Vector databases are exploding in popularity. They help machines understand things like meaning, similarity, and context. From powering AI searches to making chatbots smarter, vector databases are a key part of the tech stack. But how do you figure out what they really cost?
When people talk about cost, they often talk about total cost of ownership—or TCO. TCO includes more than just the price tag of software. It also includes storage, compute, maintenance, and even data movement.
In this article, we’ll break it down into three parts:
- Egress
- Storage
- Queries
We’ll make it simple and a little fun. Ready?
What’s a Vector Database, Again?
A vector database stores data in a way that lets machines “search by meaning.” Regular databases search exact matches. Vector databases let you say, “Find me something similar to this.”
Imagine uploading thousands of product descriptions. Now you want to find items that are about the same thing. A vector DB can do that in milliseconds.
Cool, right? But that speed and power come with hidden costs.
1. Egress: What’s Leaving the House?
Egress is the data leaving your database or cloud provider. Think of it like streaming movies. When you download or send out data, someone has to pay.
Some vector DB providers charge for this. Some don’t. But it can really add up! Here’s why:
- AI apps pull lots of data during each search
- Vector embeddings are big!
- More users = more queries = more data leaving
Let’s say your app does a million queries a month. Each returns 100KB of data. That’s 100 gigabytes of egress!
Now imagine your provider charges $0.09 per GB. You’re paying $9 just for data leaving the building. Not horrible—but multiply that as you scale.
Tip: Choose a provider with free or discounted egress tiers if possible.
2. Storage: Where Do Your Vectors Live?
Storage seems simple. You store data. You pay per gigabyte. Done, right?
Not quite! With vector databases, you’re not just storing one thing. You’re storing:
- Raw data: like product info or document text
- Vector embeddings: large floating-point arrays
- Indexes: special files that make search fast
A single vector might take up 512 bytes or more. Multiply that by billions of images or sentences, and it gets big fast.
Plus, indexes can be even larger than the vectors themselves! That’s because they track relationships and distances between vectors.
Let’s pretend you have 10 million vectors. Each is 1KB. That’s 10GB. If the index is twice the size, now you’re storing 30GB total.
Your provider might charge something like $0.10 per GB/month. So you’re spending about $3/month. Scale that to billions of vectors… and you get the idea.
Pro tip: Compress vectors when possible. Some models offer smaller embeddings with decent performance.
3. Queries: The Silent Cost Monster
Query pricing is like paying to ask a question. Every time you ask the database to find similar stuff, you’re using CPU—even GPU in some cases.
Some providers charge per query. Others charge based on compute time. A few just bundle it in. Here’s what affects pricing:
- Number of queries: 1,000 vs 1,000,000 searches
- Search depth: Top 10 results vs Top 1,000
- Filter complexity: “Find similar ones… but only for blue shirts in size Large.”
More filters = more work = more dollars.
Also, real-time queries cost more. If you want sub-second latency, you’re paying for speed.
A quick example:
- 100,000 queries per month
- Each query costs $0.0005
Total query cost = $50/month.
Combine that with storage and egress, and your seemingly “cheap” vector DB might now cost hundreds—or thousands—per month.
Wait… What About Open Source?
Great catch. Open-source vector DBs like FAISS, Weaviate, and Qdrant are free to run… technically. But they come with their own costs:
- You need to host them (cloud servers cost money)
- You manage backups, security, scaling
- High performance = high compute bills
It’s like buying a puppy for free. The vet bills and food come later!
Hosted versions like Pinecone, Milvus Cloud, or PGVector on a managed Postgres make life easier—but with monthly bills.
How to Calculate TCO Smartly
Here’s a checklist to help you calculate the real cost of your vector database:
- Storage: Estimate vector + index size
- Egress: Multiply response size × queries/month
- Queries: Estimate cost/query × traffic
- Infra: For open-source, add compute, maintenance
Also think long-term. What happens as your user base triples?
Saving Hacks: Spend Less, Store Smart
Here are a few ways to reduce TCO without losing performance:
- Use lighter embeddings: Try 128-dims instead of 768
- Filter early: Shrink the search scope with metadata
- Batch simplifies: Group similar queries together
- Compress intelligently: Quantization can save space
Some tools let you store the vectors in cheap storage and load them into memory only when needed. That’s like taking a bike out of the garage instead of always leaving it in the driveway.
Final Thoughts: All About Trade-Offs
Vector DBs aren’t expensive—they just behave differently. Cost is tied to performance and scale. Fast, flexible searches mean you’re paying for compute, storage, and bandwidth.
The key is balance. Know your usage patterns. Predict your growth. And pick a provider or setup that matches your real-world needs.
Don’t just go for the shiniest option. Go for the one that makes sense—for your users, your data, and your wallet.
Now that you’ve read the fine print, you can start planning your vector-powered future—without sticker shock.
