The three seconds that almost convinced me I’d built the wrong thing

For about a day I thought AgentRAM was slow in a way I couldn’t fix.

The whole pitch of the thing is that it’s simple. A memory API for AI agents, one call to store, one to recall, no vector database, no embeddings, no model sitting in the request path adding half a second every time. The entire reason to pick it over something heavier is that it should feel instant. So when a plain store-then-recall started taking three seconds, it wasn’t just a bug. It felt like the premise was wrong.
Three seconds. For a key-value read. I want to walk through how I chased it, because I went after the wrong suspect twice before I found the real one, and the real one was almost boring.

First suspect: my own code
The obvious thing to blame is your own code, and usually you’re right. So I read the request path line by line. Every step that touched the database was a single operation. Nothing looped. Nothing waited on anything it didn’t need. There was no query hiding inside a helper that ran ten more, no accidental full scan, no work being done twice. I counted the trips to the database on one hand, and every one of them had a reason to be there.
I added timing around each step anyway, because reading code and measuring code are different sports and the second one is the only one that counts. And the timings said the handler logic was fine. Single-digit milliseconds for everything I actually wrote. The three seconds were happening somewhere I wasn’t looking, which is the worst place for three seconds to be.

Second suspect: the database
If it’s not my code and the time is going somewhere between my code and the data, the database is the natural next place to point. Maybe the query planner was doing something dumb. Maybe I was missing an index and the two rows in my test table were lying to me about how a real query would behave.
So I went and looked at the queries directly, ran them against the database with timing on. They came back in a couple of milliseconds. Of course they did, there were barely any rows. The database wasn’t slow. The database was sitting there wondering why I was accusing it.
This is the part of debugging nobody tells you about. You eliminate two suspects and instead of feeling closer you feel worse, because the time is definitely being spent, you can see it on the clock, and you’ve now proven it isn’t being spent in either of the two places it obviously should be. The slowness was real and it had no home.

The thing I wasn’t measuring
I had been timing the work. I had not been timing the distance.

My API runs on one managed platform. My database runs on another. Both are good and I’d pick both again. But I had set them up at different times, on different days, thinking about different things, and I had never once stopped to ask where each of them physically was. They were in different regions. Every single query my API made was leaving one part of the world, crossing to another, and coming back. Not once per request. Once per query, and a request makes several.
A few milliseconds of actual database work, wrapped in a round trip that went most of the way around the planet and back, several times over, per request. That was the three seconds. Not my code, not the query, just geography I had never looked at because latency between two services feels like it should be zero until the day it very loudly isn’t.
There was a second, quieter offender sitting next to the first. The very first request after a period of quiet was always the worst, noticeably worse than the ones after it. A cold connection. Nothing was keeping the path warm, so the first visitor of any quiet stretch paid to wake the whole thing up.

The fix was smaller than the hunt
I moved the API to the same region as the database. That was most of it gone immediately. The queries still do the same tiny amount of work, they just no longer take a world tour to do it.
Then I gave the health check a real job. There’s a monitor pinging the service to confirm it’s up, and I let that ping keep the path warm, so the first real user of a quiet morning isn’t the one who pays to start the engine. The monitor pays it instead, on a schedule, and never complains.
Three seconds went to about three quarters of one. Same code. Same database. Same two rows. All I changed was where things sat relative to each other and whether the path stayed awake. I didn’t optimize a single query. I didn’t rewrite anything. I moved two things closer together and kept a light on.

What stayed with me
Here’s the part I didn’t expect to matter.
When it was fixed and I sent the test request and watched it come back in under a second, it was very late and I was the only one awake. And that fast response came back from a service I had built by myself, that other people, developers I will probably never meet, were going to call from their own agents. That landed harder than the fix did. The number on the screen was small. The distance it had just closed, between me and whoever ends up using this, was the part I sat with.
Then I closed the laptop, because it was late.
If you’re running two managed services and something feels mysteriously slow, before you tear your own code apart: check where they physically are, and check whether the first request of a quiet stretch is the slow one. Distance and cold starts don’t show up in your code review, because they aren’t in your code. They’re in the space between the boxes, which is the one place you never think to read.
I almost rewrote a thing that was never the problem. The problem was that I’d never asked my two services if they lived in the same city.

What's the one that got you? The bug you spent hours accusing your own code over, before you found out the real culprit was somewhere you'd never thought to look. I want to hear it.

(I hit this one building AgentRAM, a memory API for AI agents, in the open. The story's the point here though, tell me yours.)