MLflow vs Datadog LLM Observability
Both are observability & tracing tools. Here is how they actually differ on price, billing model and deployment.
MLflow
The open-source ML platform that grew a serious GenAI half. MLflow 3 adds OpenTelemetry-compatible tracing, LLM judges and review apps - free and self-hostable, with Databricks selling the managed version. The best zero-cost option if you can run infrastructure.
Datadog LLM Observability
LLM tracing bolted onto the Datadog APM platform. Bills per LLM span rather than per trace, which is the single most misunderstood thing about it - agentic workloads can burn the 40,000-span free tier in a day.
| MLflow | Datadog LLM Observability | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 3/5 |
| Starting price | $0 (open source) | $160/mo |
| Billing meter | No usage metering | span |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | No or paid tier only |
| Best for | Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. | Teams already standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. |
Our verdict on MLflow
MLflow is the strongest zero-cost option in the category, with the caveat that free software is not free to operate. MLflow 3 turned what was an experiment-tracking tool into a real GenAI platform - OpenTelemetry-compatible tracing from a single line of code, built-in and custom LLM judges, review apps that collect expert feedback and align automated judges against it, and evaluation datasets built directly from production traces. It is Apache 2.0 and the open-source build is complete rather than a gated teaser, which is more than can be said for several commercial competitors advertising self-hosting. Two honest caveats. The UI is functional rather than pleasant, and it shows its lineage as a tool built for ML engineers rather than application developers. And the genuinely best-governed experience - Unity Catalog trace storage in OTel Delta tables, no storage cap, SQL queryable - is available on Databricks, which is where the commercial gravity sits. If you already run MLflow or Databricks, this is close to automatic.
Full MLflow review →Our verdict on Datadog LLM Observability
Datadog LLM Observability is the obvious pick if Datadog is already your monitoring platform, and a poor one otherwise. The correlation story is real - being able to trace an LLM latency spike down through the service, host and database in one product is something no AI-native competitor matches. The eval tooling is competent but not the reason you would buy it. The thing that decides this product is billing. Datadog charges per LLM span, not per trace, and that distinction is where teams get hurt. A simple completion is 3 to 5 spans, but an agentic workflow with tool calls, retrieval and reasoning chains is commonly 20 to 50. Estimate your bill from requests and you will be wrong by more than an order of magnitude. Model span volume first, then decide.
Full Datadog LLM Observability review →Frequently Asked Questions
What is the main difference between MLflow and Datadog LLM Observability?
MLflow: Teams that already run MLflow for classical ML, Databricks customers, and anyone who wants a genuinely free and complete self-hosted platform and has the operational capacity to run it. Datadog LLM Observability: Teams already standardised on Datadog for infrastructure and APM, running LLM features inside a larger system, who value one pane of glass over best-in-class eval tooling and can model their span volume accurately. Both sit in Observability & Tracing, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, MLflow or Datadog LLM Observability?
It depends entirely on your workload shape, because they meter differently - MLflow bills on no usage metering and Datadog LLM Observability bills on span. Published starting prices are $0 (open source) and $160/mo respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host MLflow or Datadog LLM Observability?
MLflow: Yes, free. Datadog LLM Observability: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.