MLflow vs SigNoz
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.
SigNoz
An open-source Datadog alternative that handles LLM telemetry as part of full-stack observability rather than as a separate product. Free to license, but you are running ClickHouse - the cost is infrastructure and ops, not fees.
| MLflow | SigNoz | |
|---|---|---|
| Category | Observability & Tracing | Observability & Tracing |
| Our rating | 4/5 | 4/5 |
| Starting price | $0 (open source) | $19/mo (startup) or $49/mo |
| Billing meter | No usage metering | gb-ingested |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| 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 that want one observability backend for their whole stack rather than a separate LLM tool, are cost-sensitive relative to Datadog, and either have ClickHouse operational capacity or will pay for cloud. |
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 SigNoz
SigNoz is the right answer to a question many teams are actually asking - not "which LLM observability tool" but "how do I see my LLM calls alongside everything else without paying Datadog prices." It handles LLM telemetry as one workload within full-stack observability, giving token-level tracing, per-model cost attribution and prompt latency breakdown, all correlated with the databases and microservices underneath. It is genuinely open source and self-hostable with no feature gating. Two honest caveats. Self-hosted free covers the license and nothing else - you are operating ClickHouse, which is resource-hungry, and you inherit the on-call burden of your own monitoring stack going down. And there is no evaluation capability whatsoever, so if you need LLM-as-judge scoring, datasets or regression testing, SigNoz is half your stack and you will pair it with something like DeepEval or promptfoo.
Full SigNoz review →Frequently Asked Questions
What is the main difference between MLflow and SigNoz?
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. SigNoz: Teams that want one observability backend for their whole stack rather than a separate LLM tool, are cost-sensitive relative to Datadog, and either have ClickHouse operational capacity or will pay for cloud. 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 SigNoz?
It depends entirely on your workload shape, because they meter differently - MLflow bills on no usage metering and SigNoz bills on gb-ingested. Published starting prices are $0 (open source) and $19/mo (startup) or $49/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 SigNoz?
MLflow: Yes, free. SigNoz: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.