That manual list approach sounds like a nightmare to maintain, honestly. You'd have to update it with every single deploy or new microservice.
We haven't tried suppressing them entirely, but we did route the alerts to a separate, low-priority channel that mostly gets ignored. It's still noise, just contained noise. Has that approach made the problem worse by making us ignore the channel altogether? I worry we might miss something real in there.
>Consider the following example of the current YAML configuration
I'd be very interested to see that example, because your core point about "not all services are created equal" is exactly what got us stuck. We tried to solve it by creating multiple alert definitions, each with its own static list of service names for a different severity.
What we discovered was the problem of transitive dependencies. Say you have a `checkout-api` service with a high threshold, but it calls `inventory-service` which you've assigned a lower threshold. A latency spike in the inventory service bubbles up to the user-facing API, but only triggers the lower severity alert based on the origin. The configuration can't capture that critical path context.
This forced us to map every service's role and its upstream consumers manually, which became its own fragile meta-system.
ship early, test often
Your transitive dependency example is a fantastic illustration of the core issue. The alerting model is built for a static, flat service catalog, but that's not how modern platforms work.
That manual mapping you had to build is what their product *should* be doing automatically. It's essentially a service graph with operational context. The cost of maintaining that external meta-system is exactly the "operational burden" user1193 mentioned earlier.
You've highlighted another hidden cost: alert inconsistency. The lower-severity alert on inventory-service might get auto-resolved before anyone checks the higher impact on checkout-api, creating a false sense of security. Have you seen that happen?
Every dollar counts.
Yeah, the auto-resolve part is a real trap. If the inventory alert clears on its own, the dashboard goes green but the user impact is still there, buried in checkout latency.
So you're basically forced to build that service graph externally and then hardcode all the relationships into the alert logic? That sounds... like it would break every Friday deploy.
Exactly. We saw that false-green scenario happen last week and it took us ages to trace.
You're right about the external graph breaking - that's the real killer. We tried a hacky solution using tags from our service registry, but even those tags need constant manual updates. It's like building a second, even more fragile alerting system on the side.
Has anyone found a platform that actually handles this dependency mapping, or is this just how it is everywhere now?
>building a second, even more fragile alerting system on the side
That's the inevitable outcome. You automate the manual tags, and now you're responsible for the reliability of both the main platform and your meta-system. When it breaks, which it will, you own two failures.
No platform handles this well because they all start with the same flawed assumption: a service is an island. They add tags and graphs later as an afterthought, never baked into the alert logic.
The only thing worse than a noisy alert is a silent one. Your external mapping creates both.
Don't panic, have a rollback plan.
You're dead right about the core issue. It's the same everywhere, not just Traceloop.
They build these tools for the neat, simplified diagrams on their marketing site. Real production is a messy graph where everything depends on something else. Slapping a global threshold on that is just lazy engineering.
The "identical severity" point is what kills you. It trains everyone to ignore the channel.
CRM is a necessary evil
Routing to a separate channel you mostly ignore is a classic, and dangerous, trap. You've correctly identified the risk.
You've just moved the alert fatigue problem from one channel to another, without solving the signal-to-noise ratio. That channel becomes a reliability blind spot. It trains the team to ignore alerts, which defeats the entire purpose of having them.
The operational cost becomes a risk of missing an actual incident because "it's just that noisy channel." Has anyone on your team actually checked that channel in the last 24 hours, or is it just scrolled past?
>the condition is applied globally across all services and traces
This is the exact pain point. We tried to work around it by routing alerts via Zapier to filter and bucket them by service type before Slack. But that just shifts the tuning problem to an external script you now have to maintain.
The real missing piece is an API endpoint or webhook that surfaces the full trace context in the alert payload. If the alert included the service graph and role metadata, you could at least build your own logic. But right now, it's just a flat JSON blob with a service name, which is useless for the dependency problems others are talking about.
Webhooks or bust.
Agreed, and the YAML config example would be super helpful.
What drives me crazy is that this is exactly the kind of problem a good PR workflow in GitOps could catch. If alert definitions lived in a repo with a required review from the service team, you'd at least force a conversation about the threshold per service. Right now it's too easy to just set a global rule and forget it.
Have you considered managing the alert configs as code in your service repos, and using something like a pull request template to mandate filling out the "service criticality" field? It's a manual layer, but it creates a forcing function.
git push and pray
Oh, I feel this so much from my email marketing days. It's the same fundamental problem of audience segmentation applied to alerting.
>not all services are created equal
Exactly! This is why segmentation is so critical. You wouldn't send the same email campaign to every single person in your database. High-value customers get one set of messages, trial users another, and so on. Applying a global latency threshold is like blasting a "50% off" email to your entire list, including people who signed up yesterday. It creates fatigue and makes people ignore the genuinely important stuff.
Your YAML configuration example would probably show that lack of segmentation fields, right? You need to be able to set alerts based on service *role* (user-facing vs background) and *criticality* (revenue-impacting vs informational). Without those filters, every alert feels like a five-alarm fire, and soon none of them are.
Have you tried creating separate alerts for each service type as a workaround, or does the volume just make that impractical?
test everything twice
Oh, the dreaded "we've seen the backlog" comment. It's always the same story - shiny new telemetry sources over fixing the broken fundamentals. It's like watching a restaurant add a dessert menu while the kitchen is on fire.
That separate pre-deploy job to sync with your service registry is the perfect, depressing example of workaround tax. You're not building features anymore, you're building scaffolding to keep a flawed system from collapsing. And you're absolutely right, it's absurd. The config churn becomes a full-time job, and the alerting system becomes the single point of failure for... your alerting system.
Demos are just theater. Show me the real workflow.
This is a crucial distinction that gets lost in a lot of monitoring tools. Your YAML example would probably highlight that there's no field for "business impact" or "blast radius." The same latency threshold for an internal cron job and the checkout service just tells you something's slow, not whether the business is on fire.
It reminds me of early SaaS vendor negotiations, where everything had a single per-user price, regardless of usage or role. You had to fight to get tiered pricing for read-only users vs. admins. Alerting feels like that now: a one-size-fits-all model applied to a system where context is everything.
Trust the data, not the demo.
Right, that comparison to SaaS pricing tiers is spot on. It's the same mindset. We treat alerts like a commodity feature instead of a core design parameter.
We tried to hack around this by using tags for "blast radius" in our config, but then you're just manually maintaining a lookup table that drifts from reality. It becomes another brittle meta-system.
Until alerting systems bake this classification in at the foundational level, we're all just paying the tax of manual context-shifting.
Keep automating!
Yeah, that Prometheus comparison hits home. We went down a similar path with VWO's early alert system for experiment anomalies. You'd end up copying the same rule for each traffic segment, and heaven forbid you needed to change the confidence interval. One tweak and you're updating twenty near-identical YAML blocks.
The programmatic API idea is a good workaround, but it's still just treating the symptom. Even with templating, you're still the one manually defining the segmentation. The system itself doesn't *understand* the different contexts, so you're just building a smarter config generator. It helps with the bloat, but you still have to know and maintain all the service roles yourself.
✌️