Automation Watchdog vs. Datadog for RPA
Understanding how business-level RPA monitoring complements infrastructure observability
Datadog excels at infrastructure and application observability (logs, metrics, traces). It shows how systems behave.
Automation Watchdog (AW) is a privacy‑first, RPA‑native monitoring layer that verifies whether business work got done, when it ran, and whether it met business expectations without ingesting sensitive bot data or code.
TL;DR:
Keep Datadog for deep technical telemetry. Add AW for business‑grade run integrity with minimal footprint and far less alert noise.
Side‑by‑Side Comparison
| Dimension | Datadog | Automation Watchdog |
|---|---|---|
| Primary Model | Telemetry ingestion (logs, metrics, traces) + pattern‑based alerts | Heartbeat & run‑state model: "Did the workflow check in on time? Finish within tolerance?" |
| Data Needed | App/robot logs, custom metrics, traces, synthetic probes | Only timing & minimal run context (privacy‑first). No code/data access. |
| Answers Best | "What's breaking under the hood?" CPU, memory, errors, latency, dependencies | "Did the business process run as expected?" Run windows, SLAs, queue outcomes |
| RPA Semantics | Custom build: map runs to services, parse logs, tune patterns | Built‑in: Designed for Dispatcher / Performer / Reporter patterns |
| Silent Failure Detection | Harder, requires negative‑log detection or synthetics | Native: missed heartbeat/run windows trigger "no‑run" incidents with grace |
| Alert Fatigue | Can be noisy unless finely tuned | Low‑noise by design: alert only when business conditions are violated |
| Privacy Posture | Ingests telemetry (may include sensitive context unless scrubbed) | Privacy‑first: never reads customer data or code; one‑way, outbound signals only |
| Stakeholder Fit | SRE, platform, DevOps | Ops managers, COE leads, business owners (SLA & outcome focus) |
Alerting Philosophy
Alert on telemetry anomalies.
Great for root cause and performance trends.
Alert on broken business promises.
If a workflow doesn't start, doesn't finish within a window, or fails to meet throughput targets then page the human.
This is why teams can keep Datadog for technical depth and add AW for run integrity.
Adopt Automation Watchdog as an overlay to Datadog for RPA. Use AW to codify run windows, tolerances and processing expectations per workflow. Continue to rely on Datadog for deep technical investigation.
The combined approach delivers lower operational noise, faster business‑impact detection, and stronger privacy posture with minimal implementation effort.
Ready to Complement Your Datadog Setup?
See how Automation Watchdog can reduce alert noise and improve business-level visibility for your RPA workflows.