Why my team and I are building yet another API monitoring tool (and why everyone else got pricing and config wrong)

A few months ago, my team and I went looking for an API monitoring tool. We didn't need anything exotic—just something to tell us when our API is slow, when it's erroring, and to give us enough context so we don't have to stare at a dashboard all day to figure out why.

We looked at the usual suspects: Datadog, New Relic, Sentry, Grafana Cloud. Almost immediately, we ran into the same three roadblocks:

Usage-based pricing that feels like a trap. It scales terrifyingly with traffic. The month your product actually gets popular is exactly the month you get hit with a massive surprise bill—when you can least afford it.

Enterprise bloat. They are built for massive corporations, packing 40 features we'd never touch and requiring a setup process that eats up half a day.

Alerts with zero context. They’re great at yelling that something broke, but terrible at telling us why, leaving us to grep logs at 2 AM like it's 2015.

So, we started building our own. Not because the world needs another monitoring tool, but because we couldn't find one built for a team our size, with fair pricing and modern tooling.

Here are the four core bets we’re making:

1. Zero-Redeploy Remote Config Have you ever needed to change your monitoring config, adjust a sampling rate, or enable debug mode during a massive traffic spike? If so, you know the terror of having to redeploy a backend that is already under heavy load just to push a config change.

We built Remote SDK Configuration so you don't have to do that. You can toggle sampling rates, change alert thresholds, or adjust payload capture directly from our dashboard, and the SDK updates instantly in your live environment without a single restart or deployment.

1. AI-Powered Root Cause Analysis
   An alert that says "Error rate up 40% on /checkout" tells me something is wrong, but it doesn't tell me what. Our system does a first pass automatically. When an error spikes, it pulls recent error samples, feeds them to an LLM, and surfaces a root cause + a suggested fix alongside the alert. It isn't magic and it won't always be 100% right, but it turns a blind "go investigate" into "here's a starting point." On a bad day, that’s a meaningful difference.

2. Flat Pricing (No Usage-Based Surprises)
   I get why usage-based pricing exists, but for a small-to-mid team, it means you can't budget accurately. You shouldn't be financially punished for a successful marketing launch. We are building flat monthly tiers. You know your bill in January, and you know it in June. No surprises.

3. Setup should be one line, not a project
   Most SDKs make you configure transports, exporters, and a dozen other things before you see a single data point.

What we actually want from this post
We aren't looking for signups (there's nothing to sign up for yet!). We want to know if this problem is real for other people, or if we're just building a tool for a "us problem."

If you run a small-to-mid engineering team, we'd love your pushback:

What do you currently use for API monitoring, and does the pricing actually make sense at your scale?

Have you ever hesitated to change monitoring configs because you didn't want to redeploy under load? Is remote config actually a game-changer for you?

Has an alert ever told you that something broke without telling you why, and how much time did that cost you?

Genuinely, we want the critical feedback here more than the encouragement. If this is a solved problem, we'd rather find out now before we build the next ten features. What are we missing?