Options And Comparison

What Kinds Of Vector Databases Exist?

Vector search now appears in search engines, relational databases, in-memory stores, graph engines, and object storage. Each family started from a different job and added vector support on top, so each one is strongest where that original job overlaps with yours.

Family	Started as	Typical strength
Managed search service	Enterprise search	Quick setup, ready-made connectors and language understanding
Search engine cluster	Distributed search and analytics	Full-text search and vector search in one place, high query rates
Relational database with a vector extension	SQL database	Vectors and relational data queried together, ACID transactions
Document database	JSON document store	Flexible documents next to vector search, familiar APIs
In-memory database	Cache and key-value store	Very low latency for real-time workloads
Graph engine	Graph analytics	Vector search combined with relationship traversal
Object-storage-native vector store	Object storage	Large collections at low cost, no cluster to manage
Managed knowledge base	RAG platform	Ingestion, chunking, embedding, and retrieval handled for you

AIOZ Storage belongs to the object-storage-native family. It keeps vectors in the storage layer and speaks the AWS S3 Vectors API.

How Does An Object-Storage-Native Store Differ?

Most vector databases run as a cluster or an instance that you provision, size, and pay for while it sits idle. An object-storage-native store works the other way around. Vectors live in storage like any other data, and you interact with them through an API. There is nothing to provision before your first write.

That design suits collections that grow large, are queried at a steady but modest rate, and need to be kept for a long time, such as document archives, knowledge bases, and agent memory. It is less suited to workloads that need the lowest possible response time on every request.

How Do The Families Compare?

	Object-storage-native (AIOZ Storage)	Search engine cluster	Relational with vector extension	In-memory database
Architecture	Storage layer with a vector API	Distributed cluster	Database server	Memory-resident database
Scaling	Grows with the storage layer, no capacity planning	Add nodes and rebalance	Bigger instance or read replicas	Bigger nodes and replicas
Setup	Create a bucket and an index	Size and configure a cluster	Install and tune an extension	Size and configure nodes
Team skills	S3 and basic vector concepts	Search engine operations	SQL and PostgreSQL	Redis-style operations
Programming model	S3 Vectors API and SDKs	REST and query DSL	SQL	Key-value commands
Best fit	Large, long-lived collections and RAG	High-frequency search with full-text	Mixed SQL and vector queries	Real-time, ultra-low-latency lookups

AIOZ Storage supports float32 vectors, a distance metric chosen per index, metadata stored with each vector, and top-K similarity queries. It provides SDKs for Python, JavaScript, and Go, plus an MCP server for AI assistants. It does not provide SQL, full-text search, or graph traversal.

When Should You Choose Something Else?

AIOZ Storage is not the best choice for every workload.

If you need single-digit-millisecond responses on every request, an in-memory database is built for that.
If you want keyword search and vector search in the same query, a search engine cluster combines both.
If your vectors must be joined with relational tables in one transaction, a relational database with a vector extension keeps them together.
If your data is a knowledge graph and relationships matter as much as similarity, a graph engine fits better.

When Does AIOZ Storage Fit Well?

You already use S3-compatible tools and want vectors without adopting a new database and a new query language.
Your collection is large or growing and you want storage-style economics instead of paying for a cluster around the clock.
Your access pattern is retrieval for RAG, semantic search, or agent memory, where a well-ranked result matters more than shaving the last few milliseconds.
You want to keep embeddings on the same platform as the source files they were made from.
You need replication and availability without operating them yourself, which the AIOZ DePIN network provides by default through replication.

A common setup uses both kinds of store. A fast, expensive store holds the vectors queried constantly, and an object-storage-native store holds the long tail. AIOZ Storage works well as that second tier.

Build Your Own Pipeline Or Use A Managed One?

Consideration	Run the pipeline yourself	Use a managed knowledge base
Control	Full control over chunking, embedding, and retrieval	Standard patterns handled for you
Time to first result	Longer, you build each step	Shorter, one service does it all
Team focus	Data and retrieval engineering	Application logic
Flexibility	Swap any model or component	Bound to what the service supports

With AIOZ Storage you choose your own embedding model and your own retrieval logic, which is the first column. If you would rather not build the embedding step at all, Super Intelligence Memory lets you save and recall plain-language statements without creating embeddings or indexes yourself.

Where To Go Next

Cost, Use Cases And Next Steps explains what drives vector database cost and where teams apply vectors.
Vectors And Vector Databases covers the fundamentals if you skipped them.