Justifying Second Tool to Datadog Users

This is a distribution test more than a product test. If a team already stores metrics, logs, traces, incidents, and deployment context inside Datadog, Datadog can turn on AI investigation inside the workflow engineers already use, which makes a separate tool harder to justify unless Traversal finds root causes that Datadog misses because the evidence lives across several systems, not just one telemetry stack.1 2 3 4

• Datadog already sells an AI investigator as part of its platform. Bits Investigation can be launched from the Datadog app or incident channel, and Datadog says it works from the telemetry and service context already in the account. That bundled path removes a new vendor review, new budget line, and new workflow for the buyer.1 2
• Traversal is strongest when the incident trail is fragmented. Its pitch is that humans waste time hopping between monitors, alerts, code changes, and other systems before they can fix anything. In that setup, a diagnosis layer that reasons across tools can save time because the bottleneck is not collecting more Datadog data, it is stitching scattered evidence together.4 5
• This same cross vendor argument helped create companies like Cribl. Cribl won by sitting between tools and helping customers route data across Datadog, Splunk, and others. The lesson for Traversal is that second products can work in observability, but they usually need to deliver a clear operational gain that the system of record does not provide on its own.6 7

The next step is proving that multi tool diagnosis is not just nicer, but measurably better. If Traversal can become the place teams go when incidents cross cloud, code, and observability boundaries, it can earn a durable slot beside Datadog. If not, AI investigation will likely collapse into the incumbent observability bundle.1 2 4