COMPARE / DYNATRACE
Dynatrace maps every host.
Your DBUs aren’t on the map.
Respect first: Dynatrace is the reference for what world-class general observability looks like. Grail unifies logs, metrics, and traces in one topology-aware lakehouse; Smartscape auto-discovers billions of dependencies; their causal AI does real root-cause, not correlation theater. And they publish a full public rate card — a bar most of this industry refuses to clear.
The seam: all of it is built on hosts, pods, and traces. Dynatrace does not model a Databricks job, a DBU, a Snowflake credit, or a Spark stage — your executors are anonymous JVMs on well-monitored hosts. Full-stack pricing per host on an autoscaling data cluster adds up fast, and at the end of it nobody can still tell you which pipeline paid for the waste.
| dimension | Dynatrace | telemetria |
|---|---|---|
| hosts, services, APM | world-class: Grail, Smartscape, causal RCA | — not an APM, by design |
| kubernetes infrastructure | deep platform monitoring; workloads anonymous | same layer, joined to Spark apps, jobs, teams |
| spark / warehouse semantics | JVM metrics via OneAgent at best | stage, task, executor · skew, spill, shuffle — mid-run |
| platform spend (DBUs, credits, slots) | — not modeled | attributed to job → table → team, daily |
| AI / LLM observability | productized module: agents, LLMs, guardrails | — roadmap (GPU workloads first) |
| topology & causal AI | estate-wide, billions of dependencies | correlation graph scoped to data workloads |
| pricing transparency | full public rate card | published bands on monitored spend |
| cost of watching a data cluster | per-host full-stack on autoscaling nodes | priced on monitored spend, not node count |
choose Dynatrace if
- You need one platform across services, infra, RUM, security, and logs — and your estate is apps, not pipelines.
- You want productized AI/LLM observability today, from an established vendor with a Gartner MQ leader position.
- You're already on Dynatrace and your data platform footprint is too small to justify a second lens.
choose telemetria if
- Databricks or Spark is a top-3 line on your cloud bill and Dynatrace can't say which pipeline drives it.
- You want stage-level reliability answers — skew, spill, spot reclaims — joined to pods and dollars, not JVM heap charts.
- You want to keep Dynatrace exactly where it is: we ingest OpenTelemetry and cover the layer it doesn't model.
Coexistence is the design: Dynatrace on your services, telemetria on your data workloads, OpenTelemetry between them. This is not a rip-and-replace argument — it's a missing-layer argument. If we misstate a capability, email hello@telemetria.ai and we'll fix it within a week.