AI infrastructure for AI startups

If your product is AI, you've probably already drawn the architecture diagram, and it has the same five boxes everyone's has: a memory layer, a vector database for search, file storage, a document extraction pipeline, and something to run background jobs. None of those boxes is your product. They're the table stakes you have to build and operate before you get to the thing that's actually yours, and every week spent wiring them together is a week not spent on what makes your startup different.

This page is for founders and early teams building AI products. Exabase is that whole stack behind one API key: Memory, Deep Search, Resources for storage, Extract, and Workers for background jobs, with Bases giving you multi-tenancy from day one. You ship your product; the infrastructure is handled.

What you can build

Whatever the AI product is, it tends to sit on the same foundation, and Exabase is that foundation.

An agent that remembers its users, the long-term memory layer, which is the single most common thing AI products need and the one most often hacked together badly.

A RAG product that retrieves from a corpus and improves with use rather than returning the same chunks forever, RAG pipelines that actually remember.

A multi-tenant AI app where every customer has isolated memory, files, and search from day one, multi-tenant memory for SaaS.

A document-processing product that ingests anything and makes it searchable, document extraction at scale.

Most AI startups are some combination of these, which is exactly why assembling the pieces separately is wasted motion, the combination is what Exabase already is. The internal tools use case spells out the all-in-one foundation in detail.

Startup problems, solved

The problems early AI teams run into are consistent, and each has an answer.

Assembling five services. Choosing, integrating, and operating a memory layer, a vector database, storage, extraction, and a job runner is weeks of undifferentiated work, and then it's yours to keep running. Exabase is all of it behind one API key, designed to work together, so you skip the assembly and the seams.

Multi-tenancy on day one. You'll need per-customer isolation eventually, and retrofitting it is painful. Bases give it to you from the first call, structurally, so you're multi-tenant before you have your first customer rather than rebuilding for your tenth. It's the multi-tenant SaaS pattern.

Memory that's actually hard to build. The reason teams hack memory together badly is that doing it well, contradiction resolution, retrieval that holds at scale, coherence over time, is genuinely hard. Memory handles it, and the quality is measurable: state-of-the-art on the LongMemEval benchmark with a smaller model, because precise memory beats brute-force context. The reasons a vector database isn't a memory system are exactly the reasons the DIY version disappoints.

Burning runway on infrastructure. Every engineer-week on the stack is a week off your actual product. Building on infrastructure means your small team's effort compounds into the thing that differentiates you rather than the plumbing beneath it.

The full stack behind one API key

Five capabilities, one platform. Memory for agents that remember. Deep Search for retrieval that holds at scale. Resources for storage. Extract for turning any document into searchable text. Workers for background jobs. Bases wrapping all of it in multi-tenancy from day one. One API key, designed to work together, so the architecture diagram collapses from five boxes to one.

Ship product, not infrastructure

The case for building on this is sharpest for a startup, where time and focus are the scarce resources. A stack you assemble yourself gets more expensive to operate as you grow and as each service's quirks accumulate, and it competes for the engineering attention you need on your product. Building on one platform means the undifferentiated foundation stays handled while your effort compounds into what's actually yours. Multi-tenancy is there from day one, so you don't hit the rebuild that catches teams who deferred it, and the memory and search get better with use rather than becoming maintenance burdens. If you're comparing approaches, the agent memory platform comparison is a useful read.

Get started

Start with the getting started guide, then the use-case pages closest to your product: long-term memory for any agent, RAG pipelines that actually remember, multi-tenant memory for SaaS, and internal tools powered by AI for the full-stack pattern. There's a free tier to build against.

FAQs

What does Exabase replace in my architecture?

Typically a memory layer, a vector database, file storage, a document extraction pipeline, and a background-job runner, plus the integration glue between them. Instead of five services you operate, you build against one platform where those capabilities work together.

Do I get multi-tenancy without building it?

Yes. Bases give per-customer isolation from a single API call, structurally, so you're multi-tenant from day one rather than retrofitting it later. It's the multi-tenant SaaS pattern.

Why not just build memory on a vector database?

Because a vector database finds similar passages but has no contradiction resolution, no notion of one fact superseding another, and no mechanism to keep an evolving picture current, which is why a vector database isn't a memory system. Building that layer well is hard enough to be its own product, which is what Memory is.

How good is the memory, really?

Exabase reaches state-of-the-art on LongMemEval, a long-term-memory benchmark, with a smaller model, because precise retrieval beats loading more context. Reliable memory has also been shown to cut hallucinations by around 28%.

Is there a free tier to start on?

Yes. There's a free tier to build against, so you can validate the foundation before committing.

Is this a finished product or something I build on?

Something you build on. Exabase is the infrastructure stack, memory, search, storage, extraction, jobs, and multi-tenancy, and you build your AI product on top of it.