Datadog is unveiling Updog, a free web dashboard designed to solve for one of the most persistent questions during an incident response: is it us, or is it them? The tool monitors the health of leading cloud and SaaS services and indicates when popular providers — including AWS, Cloudflare, OpenAI, Slack and dozens more — are experiencing outages so that engineers can quickly diagnose disruptions.
Unlike those paid-for observability suites, Updog is free and meant for quick, high-signal checks by anyone. Datadog states the service uses AI to filter through telemetry and public signals for signs of imminent problems, with the goal of surfacing these issues before official status pages are modified.
What Updog Does for Monitoring Cloud and SaaS Health
Serves as a status superhub for the modern app stack. It watches dozens of popular tools and services to see whether they’re underperforming or inoperative, which allows on-call teams to quickly decide whether they need to escalate the issue up their organization’s chain of command or sit tight while a provider fixes it. It’s come in handy for systems that depend on many external APIs, cloud regions, or identity services where even a single dependency can slow down an experience.
The difference, according to Datadog’s team, is speed. The company wrote in a recent blog post that Updog AI flagged an Amazon DynamoDB degradation 32 minutes before AWS posted on its own status page. During an event, 30 minutes is an eternity (it can be the difference between customer notice and a cascade across microservices).
Why Early Signals Matter During Outages and Incidents
Outage expenses have soared as architectures become more distributed. Outage Analysis reports published by the Uptime Institute have indicated that incidents with six-figure price tags are on a steady rise; in a meaningful number of cases, they exceed $100,000, and in a not inconsequential minority (so to speak) top more than $1 million. Even when revenue is not immediately affected, downtime burns engineering time, results in SLA penalties, and degrades trust.
Think of sweeping cloud outages recently that have affected banks, payment processors, and public services. If operators can identify issues quickly, they don’t drop that root-cause ball to a third-party provider on sight; and if the issue is with such a party, operators can shift traffic, throttle back nonessential experiences, or toggle static ones (all while communicating clearly with customers and execs). Early clarity cuts through the noise, shrinks mean time to mitigation, and keeps teams working on meaningful actions.
How It Compares to Status Pages And Crowd Reports
Provider status pages are authoritative but tend to be conservative and sometimes lag; teams won’t typically post until they’ve confirmed scope and cause. Crowd-sourced trackers like Ookla’s Downdetector can be quick off the mark but are often noisy, spiking on regional ISP problems or news-driven chatter rather than provider faults.
Updog attempts to strike a middle ground, combining telemetry-derived signals and public indicators with anomaly detection. The goal is fewer false alarms than pure crowd data and faster alerts than updates from manual providers. It won’t replace official statements or a team’s own observability stack, but it can serve as a first look when the pager goes off and dashboards become murky.
AI in the World of Observability and Incident Response
Most contemporary observability platforms have pivoted toward AIOps—using machine learning to detect patterns across metrics, logs, and traces, and to correlate signals that humans might be unable to catch while frazzled. Here, AI is able to identify small, correlated drifts across endpoints or regions which may suggest a provider issue well before it becomes headline news. The challenge, of course, is to strike that balance between sensitivity and precision so that teams aren’t whipsawed by false positives.
Datadog has been adding AI on top of its offerings, and with Updog, that takes a lightweight, publicly available front end. The upside is accessibility: teams who haven’t homed in on one observability vendor can still glean a reliable read on the larger ecosystem of services they run on top of.
What This Says About Modern Software Engineering Teams
For incident commanders and SREs, Updog is a practical amendment to the runbook: look at internal health, check Updog, triage playbook as necessary. If Updog shows an issue will be widespread among providers, teams can pivot to mitigation and comms instead of burning cycles investigating a phantom regression. If it’s muted, the hunt goes on inside.
The upshot: as reliance on third-party services grows, the capacity to quickly answer which apps are down is no longer a nice-to-have—it’s critical for resilience. Updog alone won’t make outages vanish, but if it consistently provides a handful of extra minutes of warning time to most teams, that’s an edge many will gleefully embrace.
