Save Money on Vector DBs: PQ, Quantization & Hybrid Indexes Explained

Vector databases are amazing for search, AI, and recommendation systems. But they can get expensive.

If you’ve ever looked at your cloud bill and thought, “Wait… why is storage eating my budget?” — you’re not alone.

The good news? You can keep performance high while paying much less.

In this guide, we’ll talk about quantization, Product Quantization (PQ), and hybrid indexes in a simple, step-by-step way.

Perfect for small teams who want big savings without hiring a full-time data engineer.

Why Vector DB Costs Add Up So Fast

Before we jump into optimization tricks, let’s understand the problem.

A vector database stores information as vectors — lists of numbers that represent data like images, text, or audio.

Modern AI models often create large vectors (think 768 or 1024 dimensions).

That means:

Each vector is big → takes more space.
Billions of vectors = huge storage bills.
More storage → more memory needed for search.
Larger indexes → slower queries without extra CPU/GPU power.

And if you’re using cloud hosting for your DB? Costs can grow even faster.

So… what’s the plan?

We reduce the size of the data without hurting search accuracy too much.

Strategy #1: Vector Quantization (Shrink Without Breaking)

Quantization is just a fancy word for compressing numbers.

Instead of storing each number as a 32-bit float, you store it as something smaller — like 8-bit integers.

How it Works

Original vector: [0.23124, 0.89231, 0.12111, ...] (each number takes 4 bytes).
After quantization: [59, 202, 44, ...] (each number takes 1 byte).

You map the small integer values back to approximate floating values during search.

Result: Storage drops by 75% or more.

Pros

Big storage savings.
Still fast to search.
Simple to set up in many vector DBs (Milvus, Weaviate, FAISS, etc.).

Cons

Slight loss of accuracy in similarity search.
Needs tuning to find the right compression level.

Example

If you store 100 million 768-dimensional vectors in 32-bit floats:

Size = 100M × 768 × 4 bytes = ~307 GB.
With 8-bit quantization:
Size = 100M × 768 × 1 byte = ~76 GB.

That’s over 230 GB saved — instantly.

Strategy #2: Product Quantization (PQ) — The Smart Compression

Think of PQ as “quantization, but smarter.”

Instead of shrinking the whole vector in one go, PQ splits it into chunks and compresses each chunk separately.

How PQ Works

Split vector into parts — e.g., a 768-D vector into 8 parts of 96-D each.
Learn a small dictionary for each part (a set of typical sub-vectors).
Store only the dictionary index for each part, instead of the actual numbers.

It’s like compressing a long sentence by replacing common words with short codes.

Why PQ Works Well for Cost Optimization

Smaller storage size than regular quantization.
Search can use approximate distances for speed.
Works well for billions of vectors.

Example Storage Impact

Let’s say your vector DB uses:

1,000,000 vectors, 512 dimensions
Each value is 32-bit float (4 bytes).

Original size:

1,000,000 × 512 × 4 bytes ≈ 2 GB.

With PQ (8×8-bit):

Each chunk: 1 byte index.
Size: 1,000,000 × 8 bytes = 8 MB (plus small dictionaries).
Yes, from 2 GB to ~8 MB for the data part.

That’s massive.

PQ Drawbacks

More accuracy loss than standard quantization.
Best for large datasets where small accuracy trade-offs are okay.
Queries may be slightly slower if you use complex decoding.

Strategy #3: Hybrid Indexes — Balance Speed & Storage

Sometimes, the cheapest way isn’t to store everything in a high-speed format.

That’s where hybrid indexes come in.

What is a Hybrid Index?

You combine vector search with metadata filtering or other search types.

For example:

Store only the latest / most popular vectors in a fast, memory-heavy index.
Keep the rest in slower, cheaper storage (disk-based index).
Use metadata filters (e.g., category, tags) to narrow the search before vector lookup.

Why This Saves Money

Memory (RAM) is expensive in cloud databases.
By keeping only part of the data in RAM, you can use smaller instances.
Old or rarely accessed vectors can live on cheaper disks.

Real-Life Example

Imagine you run a product search:

Top 100K products searched daily → in RAM, fast HNSW index.
The other 5M products → in disk-based index with PQ compression.
Users still get fast results most of the time, and you save on high-RAM servers.

How to Combine These Strategies

The magic happens when you mix these methods:

Quantization + Hybrid Index

Store hot data in RAM (quantized for speed + space).
Store cold data on disk with stronger compression.

PQ + Metadata Filters

Use PQ for storage savings.
Add filters to reduce candidate vectors before final scoring.

Quantization + PQ

Apply standard quantization first.
Then split into chunks for PQ to squeeze size even more.

Tips for Small Teams

If you don’t have a dedicated infra engineer, here’s the low-maintenance approach:

Start with basic quantization — most vector DBs have it built-in.
Measure recall (accuracy) before and after.
Use hybrid indexes to split hot/cold data.
Move to PQ only if your dataset is very large (hundreds of millions of vectors).

Common Mistakes to Avoid

Over-compressing too early — always test on a sample first.
Ignoring recall scores — you might save money but lose search quality.
Forgetting backups — compressed indexes can still get corrupted.

The Mindset Shift

Cost optimization isn’t just about compression.

It’s about storing the right data in the right place.

Ask yourself:

Do all vectors need to be in fast storage?
Can old data be compressed harder?
Can I use filters to cut search space?

Final Thoughts

Vector DBs are powerful, but they don’t have to be expensive.

By using quantization, Product Quantization, and hybrid indexes, even a small team can handle millions (or billions) of vectors without breaking the bank.