LangWatch vs Databricks Agent Evaluation
Both are agent evaluation tools. Here is how they actually differ on price, billing model and deployment.
LangWatch
Apache-2.0 agent evaluation built around simulation - an Agent Under Test, a User Simulator and a Judge, run through pytest in CI. That architecture is the right shape for agents and almost nothing else here has it.
Databricks Agent Evaluation
Mosaic AI's agent evaluation, where tools are registered in Unity Catalog so the permissions protecting your data also scope what an agent may do. Judge Builder lets you tune the judges to your domain.
| LangWatch | Databricks Agent Evaluation | |
|---|---|---|
| Category | Agent Evaluation | Agent Evaluation |
| Our rating | 5/5 | 4/5 |
| Starting price | Event-based, rates not published | Databricks consumption |
| Billing meter | event | No usage metering |
| Free plan | Yes | No |
| Free self-hosting | Yes, free | No or paid tier only |
| Best for | Teams building multi-turn or multi-agent systems who need evaluation that models conversations rather than scoring single outputs, and who want it running in CI. | Existing Databricks customers building agents over their own governed data, where inheriting Unity Catalog permissions and MLflow lineage is worth more than best-in-class conversation simulation. |
Our verdict on LangWatch
LangWatch is the strongest agent evaluation tool we have found, and the reason is architectural rather than a feature list. Its scenarios pair three components - an Agent Under Test, a User Simulator Agent that plays the human, and a Judge Agent that scores the result - and run through pytest-compatible runners locally or in CI. That is the correct shape for the problem. You cannot meaningfully evaluate a multi-turn agent by scoring individual outputs, because the failures that matter are trajectory failures, and almost every other tool in this category still treats the single response as the unit. Simulating a conversation and judging the whole thing is what agent evaluation actually requires. It is Apache 2.0 at the core with genuine self-hosting, OpenTelemetry-native so instrumentation stays portable, and billed on ingested events with no per-feature charges. The gaps are honest ones. Per-event rates are not published anywhere we could verify, the ee/ modules are commercially licensed, and running simulations costs real inference since each scenario turn drives several model calls.
Full LangWatch review →Our verdict on Databricks Agent Evaluation
Databricks Agent Evaluation has the best answer to a question most of this category ignores - what is an agent actually allowed to do. Tools are registered in Unity Catalog, so the same least-privilege permissions protecting your data also scope what an agent may access. That means agent authorisation is not a new system to design and audit, it is the one you already have, which is the most coherent approach to the problem we have encountered. The evaluation side is strong too. Built-in AI judges score correctness, relevance and safety, runs are tracked in MLflow so versions compare and regressions can gate deployment, and Agent-as-a-Judge, Tunable Judges and Judge Builder attack the genuine weakness of LLM-as-judge, which is that a generic judge does not understand your domain. The obvious constraint is that none of this exists outside Databricks. There is no standalone product, no separate pricing, and evaluation cost sits inside consumption where it is hard to isolate. If you are a Databricks shop building agents over your own data, this is close to automatic. If you are not, it is not a realistic option.
Full Databricks Agent Evaluation review →Frequently Asked Questions
What is the main difference between LangWatch and Databricks Agent Evaluation?
LangWatch: Teams building multi-turn or multi-agent systems who need evaluation that models conversations rather than scoring single outputs, and who want it running in CI. Databricks Agent Evaluation: Existing Databricks customers building agents over their own governed data, where inheriting Unity Catalog permissions and MLflow lineage is worth more than best-in-class conversation simulation. Both sit in Agent Evaluation, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, LangWatch or Databricks Agent Evaluation?
It depends entirely on your workload shape, because they meter differently - LangWatch bills on event and Databricks Agent Evaluation bills on no usage metering. Published starting prices are Event-based, rates not published and Databricks consumption 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 LangWatch or Databricks Agent Evaluation?
LangWatch: Yes, free. Databricks Agent Evaluation: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.