After deploying a .NET API that calls external dependencies, what would you monitor?

Monitoring downstream dependencies after release

Explains how to monitor external and internal dependencies after a .NET service release.

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I would monitor each important dependency separately: database, cache, message broker, identity provider, payment provider, storage, and downstream APIs. For each one, I care about latency, error rate, timeout rate, retry count, and saturation where available. This helps identify whether failures come from our code or from a dependency. It also helps validate whether a new release changed dependency behavior.

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Dependency latency, timeout rate, error rate, retries, circuit-breaking, and production validation.

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Many production issues are dependency issues. A release can expose a bad timeout, retry, or integration assumption even when the application code starts successfully.
I would monitor each important dependency separately: database, cache, message broker, identity provider, payment provider, storage, and downstream APIs. For each one, I care about latency, error rate, timeout rate, retry count, and saturation where available. This helps identify whether failures come from our code or from a dependency. It also helps validate whether a new release changed dependency behavior.
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For example, if a release increases calls to a product API, I would monitor downstream request count, p95 latency, 429 responses, and timeout rate. The trade-off is that dependency metrics can create noise if not grouped by endpoint or operation. I prefer dashboards that show dependency health in the context of key business flows like checkout or order creation.

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Monitoring only the API process.
Not separating dependency errors by provider or operation.
Ignoring retry volume.
Treating all dependency failures as generic 500s.

After deploying a .NET API that calls external dependencies, what would you monitor?

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