AI Is Being Held Back By Data Infrastructure

AI is running a three legged race with your infrastructure. 

There’s been a sudden jump in sales. Why? 

Your AI agents should be able to run a thousand queries to investigate. Instead, if you’re lucky it runs 1-3 and gives you its best guess.

This gap between what AI could do to work with enterprise data and what it actually does comes down to infrastructure. Current data systems were built for humans running occasional queries, not agents exploring datasets. They assume batch processing is fine, that text is secondary, and (after a few painfully expensive experiences with data warehouses) that querying large datasets must be heavily limited. 

When your LLM has to work within these constraints, you get exactly what you'd expect: smart systems acting dumb because that's all the infrastructure allows. Like many technologies, AI needs a new kind of infrastructure to deliver on its promise. Just like cars needed a national network of gas stations, AI needs access to data at a scale and volume that we have not previously delivered. Without the ability to access large datasets quickly, and at reasonable cost, it is impossible to effectively leverage AI in the enterprise. 

At Read Time

Back to the previous example. Faced with diagnosing a jump in sales, a human analyst would run 10-20 queries. They would check regions, seasonality, demographics. They would look for anything that stands out. It would probably take them a day, maybe two.

An LLM agent could generate and run a thousand queries in 30 seconds. It could test every hypothesis and check every correlation, exploring every angle. Today no one does this because running those queries against a 10TB data warehouse could easily cost $100,000 or more - and they could easily take hundreds of hours to execute.

So instead you … just don’t. You point the AI at pre-aggregated summaries and cached reports limiting it to telling you only what you already know. You make it dumb to keep it affordable.

At Write Time

Quick - where do you store the conversation history for your LLM application? Postgres? Hope you are prepared to migrate to something scalable in a year or so. Databricks? It’s going to cost  a lot to write, let alone read. DynamoDB? Are you literally made of money?? Wait - you do store it, don’t you?

Sarcasm aside, the prompt/response loop itself consists of extremely valuable data that can be used for iterative training, quality assessment, and as content in some systems. Capturing this data requires specialized solutions that can handle lots of concurrent text and index it for later access. Being able to do this in near real-time also allows for the introduction of access controls to ensure that critical data is not leaking out through LLM interfaces.

Again, most people just punt on this. It is hard and any solution using traditional technology probably involves queues and batch writes; it’s definitely not near real-time! In that case, most people won’t capture it - and will lose out on extremely valuable functionality. 

We didn’t originally build MinusOneDB for AI

We built it because we were sick of the per-query tax. But it turns out when we eliminated per-query pricing and made everything fast and near real-time, we built near-perfect infrastructure for LLM applications.

• Capacity-based pricing: Pay for infrastructure, not queries. Let your LLM run 100 queries or 100,000. Same price.

• Real-time everything: Updates visible in ~1 second. No more batch updates!

• Any scale: Store gigabytes of conversation history per user. Query it instantly. Update it continuously.

• Text is a first-class citizen: We’re built on a text search engine. Your LLM’s text operations are native, not bolted on.

What This Actually Means

Your LLM can be curious. Let it explore. Let it dig. Let it test thousands of hypotheses. The marginal cost of curiosity is zero.

Your sessions can be stateful. Store everything. The full conversation. The user’s entire history. Every preference. Every interaction. Query it all in seconds.

Your logs become useful. Don’t sample 1% of interactions. Store everything. Run analytics on every conversation. Find patterns across millions of sessions.

Your agents can be agents. Not scripts that follow predetermined paths, but actual agents that explore, investigate, and discover

MinusOneDB makes AI applications work the way they were supposed to.

When queries don't have individual costs, your agents can actually investigate problems instead of just sampling them. When updates are visible in a second, your applications can learn from conversations as they happen. When text search is native, not bolted on, your LLM operations run at the speed they should.

We built MinusOneDB to eliminate per-query pricing because it was a bad model for how people actually need to work with data. It just so happens that this same change - from metered queries to capacity-based infrastructure - is exactly what AI needs to be deeply useful instead of just interesting.

The next time someone shows you an AI demo on enterprise data, ask them what happens when it needs to run 1000 queries on data to find the real answer. If they hesitate, you'll know what it can’t do - and what yours will.