Databricks Agent Evaluation vs Vertex AI Gen AI Evaluation Service
Both are agent evaluation tools. Here is how they actually differ on price, billing model and deployment.
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.
Vertex AI Gen AI Evaluation Service
Google's evaluation service, whose defining feature is adaptive rubrics - a unique set of pass/fail criteria generated per prompt, working like unit tests. Now branded under the Gemini Enterprise Agent Platform.
| Databricks Agent Evaluation | Vertex AI Gen AI Evaluation Service | |
|---|---|---|
| Category | Agent Evaluation | Agent Evaluation |
| Our rating | 4/5 | 4/5 |
| Starting price | Databricks consumption | Per token plus GCP compute |
| Billing meter | No usage metering | No usage metering |
| Free plan | No | No |
| Free self-hosting | No or paid tier only | No or paid tier only |
| Best for | 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. | Teams on Google Cloud running model migrations, prompt changes or fine-tuning comparisons, who want per-prompt evaluation criteria rather than a fixed metric set. |
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 →Our verdict on Vertex AI Gen AI Evaluation Service
Vertex AI's evaluation service has the most interesting single idea among the cloud platforms - adaptive rubrics. Rather than applying the same metric set to every prompt, it generates a unique set of pass/fail rubrics for each one, and the framing Google uses is exactly right - rubrics act like unit tests. Comparing two models becomes evaluating their responses against the same generated tests and comparing pass rates, which is far easier to reason about than comparing averaged similarity scores, and far more informative about what specifically broke. It also offers static rubrics, deterministic computation-based metrics like ROUGE and BLEU where ground truth exists, and custom Python functions, so you are not forced into model-based judging when a cheap check would do. Two things to note. It has been rebranded under the Gemini Enterprise Agent Platform, so a lot of documentation and comparison content references a name that has changed. And adaptive rubrics are themselves generated by a model, which means rubric quality varies and inherits the usual judge bias, on top of costing more inference than a fixed metric set.
Full Vertex AI Gen AI Evaluation Service review →Frequently Asked Questions
What is the main difference between Databricks Agent Evaluation and Vertex AI Gen AI Evaluation Service?
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. Vertex AI Gen AI Evaluation Service: Teams on Google Cloud running model migrations, prompt changes or fine-tuning comparisons, who want per-prompt evaluation criteria rather than a fixed metric set. Both sit in Agent Evaluation, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Databricks Agent Evaluation or Vertex AI Gen AI Evaluation Service?
It depends entirely on your workload shape, because they meter differently - Databricks Agent Evaluation bills on no usage metering and Vertex AI Gen AI Evaluation Service bills on no usage metering. Published starting prices are Databricks consumption and Per token plus GCP compute 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 Databricks Agent Evaluation or Vertex AI Gen AI Evaluation Service?
Databricks Agent Evaluation: No or paid tier only. Vertex AI Gen AI Evaluation Service: No or paid tier only. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.