Datadog × Snoonu

Datadog x Snoonu, blind spots slowed a super app's AI at scale

The Fog Lifts Problem-proofFit-proofWritten narrativeChallenge-Solution-ResultsEcommerce / Retail

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Full screenshot of Datadog x Snoonu, blind spots slowed a super app's AI at scale

Why it works

The story is structured around not knowing rather than not performing: engineers could not see what AI agents were doing in production, could not diagnose latency spikes, and waited up to a day for warehouse query results. That framing makes this a clean Fog Lifts rather than a Bottleneck, even though the 70% faster debugging and 68% latency reduction results look like step-changes. The Fit-proof secondary job is implicit throughout: Snoonu is a regional super app running custom AI agents and ML models, and the case makes observability feel native to that specific shape of company.

Steal this

Naming the exact failure mode before the product appears: engineers manually replicating user sessions, latency spikes they could not attribute to model or orchestration, and day-old warehouse queries. This specificity makes the pain credible without needing to quantify its cost.

View the original on datadoghq.com ↗

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Vendor Datadog
Customer Snoonu
Industry Ecommerce / Retail
Format Written narrative
Structure Challenge-Solution-Results
Medium Web page