Datadog × KT
Datadog x KT, building a GPU operations platform from scratch
1,700+ GPUs managed across a unified operations platform
Manual, fragmented GPU chaos to automated, unified lifecycle control
Members read free
Unlock the full breakdown
Enter your email to open every case study in the library: the full screenshot, why it works, and the one thing worth stealing. One email unlocks all of them.
Free. No spam. Unsubscribe anytime.
You're in — enjoy the library.
Why it works
The story earns New Capability because KT did not simply speed up existing GPU management; it built an entirely new incident-driven operations platform that did not exist before. The before-state was manual spreadsheets and context-switching across disconnected dashboards, with no systematic lifecycle at all. Clarity is a strong secondary because the headline question the case study opens with is explicitly 'who is using how many GPUs, and where?' but the framing leads with net-new capability rather than visibility alone.
Steal this
Anchoring the entire operations workflow to a native incident management object (the Datadog Incident) so that every GPU carries a traceable IR number as a tag from request through reclamation. This gives readers a concrete, copyable architectural pattern rather than a vague 'we used the platform' narrative.
Click to enlarge ↗ This is editorial commentary and curation. The case study, screenshot, and all metrics are Datadog's published work; we link to the source and lead with our analysis.