Replace Datadog
Why builders leave Datadog
- Costs scale with data volume — bills can 10x overnight with a traffic spike
- Dashboard fatigue — hundreds of dashboards nobody looks at
- Alert rules require constant tuning as systems change
- Vendor lock-in through proprietary query language and integrations
Agent-native alternatives
What you gain
Built specifically for LLM and agent workloads — traces, costs, latency, quality scores
Usage-based or flat pricing without the per-host/per-GB surprises
Token usage, model performance, prompt quality — metrics Datadog doesn't natively track
Langfuse is open source — self-host for zero marginal cost
Migration path
Identify agent-specific workloads
List all LLM calls, agent runs, and AI pipelines currently monitored in Datadog.
Deploy Langfuse/Helicone alongside
Instrument your LLM calls with Langfuse traces. Keep Datadog running for infrastructure.
Build agent dashboards
Create dashboards in Langfuse for the metrics that matter: cost per call, latency p95, quality scores.
Verdict
Partial replacement is the right move. Use Langfuse or Helicone for your AI/agent layer. Keep Datadog for traditional infrastructure unless your stack is 90%+ AI workloads. The cost savings on the AI monitoring alone usually justify the migration.