Cost, Use Cases And Next Steps

What Drives The Cost Of A Vector Database?

The price of a vector workload is rarely one line item. Before comparing services, list the parts that add up:

Storage: the vectors, their metadata, and any indexes built on them. It grows with every item you add.
Writes: loading and updating vectors.
Queries: every similarity search, and how much data each one has to look through.
Embedding: turning your content into vectors, either with your own model or a hosted one.
Operations: sizing, patching, monitoring, and scaling a cluster, plus the engineer time behind it.
Data transfer: moving results and source data in and out of the platform.

The way these are charged matters as much as the amounts. Provisioned services bill for capacity around the clock, whether or not anyone is searching, so a quiet index costs the same as a busy one. Usage-based services bill for what you store and what you ask, so cost follows activity.

Why Can Object-Storage-Native Vector Storage Cost Less?

A dedicated vector database is built to answer queries as fast as possible, and that speed is paid for in memory, compute, and nodes that run all the time. Many real workloads do not need it. A knowledge base may be searched only now and then, an agent memory is read when the agent needs to remember something, and an archive is queried rarely but kept for years.

For those workloads, keeping vectors in the storage layer removes the always-on cluster from the bill. Storage and compute scale on their own, so growing the collection does not force you to buy more query capacity, and buying query capacity does not force you to store more. You also avoid running one system for your files and another for the embeddings made from them.

On AIOZ Storage, replication and availability come from the AIOZ DePIN network instead of a cluster you operate. For current rates and how usage is charged, see Billing.

What Are The Common Use Cases?

Semantic search

People search by meaning instead of exact words. Embed your documents once, store the vectors with a title and source in the metadata, then embed each search phrase and call QueryVectors with includeMetadata turned on. The top results are the documents closest in meaning, with everything needed to display them.

Retrieval-augmented generation

A language model answers from your own content. Store chunk embeddings with the chunk text or an ID in the metadata. At question time, query for the closest chunks and pass them to the model as context. Because storage is cheap and durable, you can keep a large knowledge base without pruning it to save money.

Memory for AI agents

An agent remembers earlier conversations and decisions across sessions. You can build this yourself with PutVectors and QueryVectors, or use Super Intelligence Memory, which lets an agent save statements with Record and ask questions with Recall without managing embeddings or indexes. The MCP server lets an MCP-compatible assistant work with vector buckets and indexes directly.

Recommendations and similar items

Store a vector for each product, article, or user profile, then query with the vector of the item someone is looking at to find its nearest neighbors. Metadata such as category or language stays attached to each result for filtering in your application.

Tiered search

Keep the small set of vectors that are searched constantly in a low-latency store, and keep the rest in AIOZ Storage. Your application checks the fast tier first and falls back to AIOZ Storage for everything else, so you pay for speed only where it changes the result.

How Do You Get Started?

Write down your requirements. Estimate how many vectors you will hold, the dimension of your embedding model, how often you will query, and how much response time matters to your users.
Run a small proof of concept. Create an AIOZ Storage account, generate access keys in the dashboard, create a vector bucket and an index, put a few hundred vectors, and query them. The SDK guides take you through it in your language:
- Python
- JavaScript
- Golang
Check the results against your content. Run real questions and see whether the closest matches are the ones a person would pick. If they are not, the embedding model or the chunking usually needs work before the database does.
Plan for production. Decide how new content gets embedded and stored, how deleted content gets removed with DeleteVectors, and how you will monitor retrieval quality over time.