Azure AI Foundry Evaluation vs Databricks Agent Evaluation
Both are agent evaluation tools. Here is how they actually differ on price, billing model and deployment.
Azure AI Foundry Evaluation
Microsoft's evaluation, red-teaming and observability layer, positioned as a production lifecycle tool rather than a model API. Charges no separate runtime fee - but Foundry Memory was still in preview as of Q1 2026.
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.
| Azure AI Foundry Evaluation | Databricks Agent Evaluation | |
|---|---|---|
| Category | Agent Evaluation | Agent Evaluation |
| Our rating | 4/5 | 4/5 |
| Starting price | Model inference cost only | Databricks consumption |
| 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 | Enterprises already standardised on Azure that need governance, audit trails and red-teaming around AI usage as much as they need the models themselves. | 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 Azure AI Foundry Evaluation
Azure AI Foundry is the most convincingly enterprise-shaped of the three cloud evaluation offerings, and Microsoft's own framing is the clearest signal - it presents Foundry less as a model API than as a full production lifecycle tool covering evaluation, red-teaming and observability alongside inference. That matches its buyer, which is a large organisation needing governance and audit trails around AI usage as much as it needs the models. Built-in red-teaming is genuinely unusual for a cloud platform's evaluation layer, and the pricing has one quietly good property - there is no separate runtime fee for the agent service, so you pay for model inference and tool calls rather than a per-agent-hour charge, and OpenAI models cost the same as going to OpenAI directly while gaining Azure's SLA. Two cautions. Foundry Memory was still Public Preview as of Q1 2026, so SLAs and pricing were unsettled for anyone evaluating then. And total cost can span inference, orchestration, retrieval, evaluations, observability, storage and connected Azure services, which makes a single number close to meaningless.
Full Azure AI Foundry Evaluation 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 Azure AI Foundry Evaluation and Databricks Agent Evaluation?
Azure AI Foundry Evaluation: Enterprises already standardised on Azure that need governance, audit trails and red-teaming around AI usage as much as they need the models themselves. 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, Azure AI Foundry Evaluation or Databricks Agent Evaluation?
It depends entirely on your workload shape, because they meter differently - Azure AI Foundry Evaluation bills on no usage metering and Databricks Agent Evaluation bills on no usage metering. Published starting prices are Model inference cost only 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 Azure AI Foundry Evaluation or Databricks Agent Evaluation?
Azure AI Foundry Evaluation: No or paid tier only. 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.