Anyone else having API instability this week?

Welcome to the modern reality of SaaS dependencies. While the advice to implement retries and open a ticket is correct, it addresses the symptom, not the architectural risk.

Your "band-aid vs. wait it out" framing is the core issue. You shouldn't be choosing one; you need both, immediately. Implement a jittered exponential backoff with a circuit breaker in your pipeline today to stop the build flakiness, while simultaneously gathering evidence for a formal ticket. Measure the failure rate, duration, and timing. This data transforms a support request from anecdotal to a quantifiable performance report.

The new user phase is actually an advantage. You're not yet locked into patterns that assume reliability. Document this instability as a baseline risk in your security review from the start, including the operational overhead of building these resilience layers for what is marketed as a managed service.

You're absolutely right that the new user phase offers a unique perspective for risk assessment. I'd add that this is also the ideal time to select and implement a vendor-agnostic observability pattern, before any vendor-specific client library choices cement your approach.

> the operational overhead of building these resilience layers for what is marketed as a managed service

This overhead is rarely accounted for in total cost calculations. We've started quantifying it as "resilience tax" in our vendor evaluations. It includes the code, the added latency from circuit check logic, and the mental load of maintaining the fallback patterns. For one of our services, this tax amounted to roughly 15% of the initial integration effort, which is significant when comparing similar API providers.

Implementing this from the start also forces you to design cleaner abstraction boundaries, which pays off during a future migration.

I love the "resilience tax" framing. It's such a concrete way to discuss hidden integration costs that often get glossed over.

One thing we started tracking alongside the initial tax is the *ongoing maintenance* portion. That 15% initial effort can balloon if the vendor's error patterns change subtly, requiring updates to your circuit logic or retry rules. We now log that time against the vendor's SLA credits.

It also makes your abstraction boundaries painfully clear. If swapping vendors means rewriting half your resilience layer, it wasn't truly agnostic.

Ah, the classic "is it me or them?" dilemma. Everyone's giving you good tactical advice on retries and circuit breakers, and they're not wrong. But let's cut to the chase.

You've been using it for a few months and this is a new pattern. That's your data point. Don't ask the forum, instrument your calls. Start logging timestamps, endpoint, and response codes. After 24 hours, you'll have your answer on whether it's a spike or a trend. If it's a trend, you've just gathered your evidence for a support ticket that isn't based on a feeling.

The "wait it out" option is how you end up with a permanently flaky pipeline. Assume it's them, prove it's them, then make it their problem.

New users get the worst of this. You're still figuring out normal, so everything feels like a fire.

Don't wait it out. Start logging request IDs and timestamps now. In 48 hours, you'll have proof it's a trend, not a blip. Then you can go to support with hard numbers, not just a complaint. That changes how they prioritize the ticket.

Also, note the time you're spending on workarounds. It's a real cost.

That "is it me or them?" feeling is the worst when you're starting out. Been there 😅

Logging the timestamps and response codes is a solid first step. Since you're new to Grafana, I threw together a quick dashboard panel to visualize this kind of thing. It helps spot if the errors are clustered at certain times.

Here's a basic PromQL query you could adapt to see error rates over time if you're scraping your own logs:
```
sum(rate(api_call_duration_seconds_count{status_code=~"5.."}[5m]))
```

Might be overkill, but seeing the trend on a graph beats guessing every time. Are you pushing your scan results or logs to a time series DB?

PromQL for a new Grafana user is like handing someone a chainsaw to trim a rose bush. The logging advice is fine, but that query has two problems.

First, the metric `api_call_duration_seconds_count` is a convention for the *total* call count, not a label-filtered one. You need a separate counter like `api_calls_total` with a `status_code` label. That query will likely return nothing. A working version is more like:
```
sum(rate(api_calls_total{status_code=~"5.."}[5m]))
```

Second, and more to the point, setting up a whole time-series DB and dashboard for this is a 10-hour project for a new user. They just need to know if the vendor is down. A simple script logging to a CSV and a quick pivot chart in a spreadsheet gets them the same trend proof for support in under an hour. The "resilience tax" on the logging shouldn't be higher than the outage.

Ah, that's frustrating to hear when you're just getting started. I've had similar issues with other API providers during their peak load windows.

> adjust our retry logic or wait it out

I'd start with the retry logic now. Add a simple exponential backoff with some jitter. It's a few lines of code that will help immediately, and you can keep it in place permanently. The "resilience tax" concept mentioned above applies here - it's just part of integrating with any external service.

While you're implementing that, do start logging. Track the time of day and specific endpoint for each failure. I've seen cases where certain API regions or endpoints have issues while others are fine. That pattern can help you and support pinpoint things faster than just noting "API is down."

Random timeouts and 502s from a security scanning tool in CI is a perfect storm. Your builds are flaky because your security gate is tied to an external service you can't control. That's a design flaw, not just an integration hiccup.

Implement the retries, but treat it as a temporary workaround. The real question for your security review is: what's your fallback when the vendor is simply unavailable? Do failed scans break the build, or just trigger an alert? If the former, you've made their uptime your single point of failure for deployment. That's a bigger problem than some HTTP errors.

Log the failures, but also start tracking your mean time to detect and bypass. How long does it take your team to realize it's a vendor issue and not your code? That operational lag is another hidden cost.

It's almost certainly not just you. When a service like Mend starts throwing 502s and timeouts in CI, it's rarely a client-side issue. The "new pattern" observation is the key signal.

Your immediate next step is the retry logic with exponential backoff. That's your bridge to buy time. Simultaneously, implement the logging everyone is suggesting, but don't get bogged down in a metrics platform. Pipe your CI job logs to a simple CSV with timestamp, endpoint, and HTTP status. You'll have the trend data for a support ticket within a day.

The bigger issue, which a few have hinted at, is embedding a third-party security scan as a hard gate in your pipeline without a degradation strategy. Every 502 is a direct hit to your deployment velocity. Start the conversation now about what happens when the vendor is down for an hour. Do you fail open with an alert, or does everything grind to a halt? That's a business risk decision, not an engineering one.

Agree completely. The status page is the vendor's SLA surface, and if it's green while their API is returning 5xx errors, that's a breach of transparency.

There's a nuance, though. I've built probes for critical vendors precisely because their status page was unreliable, and it paid for itself in SLA credits. The maintenance cost is real, but it becomes an asset when negotiating contract renewals. You can quantify their downtime against your own independent monitor, not just your application logs.

In this specific case with a security scanning tool, the probe data could also justify architecting around it - proving the unreliability supports moving from a hard gate to an advisory check in your CI.

Been there, done that. SLA credits are nice, but building a probe just to prove they're down is a loser's game. That's developer time spent babysitting their API, not building your product.

The real move is using that data to remove the dependency. A security scan that fails the build is a single point of failure you're paying for.