Are there any model evaluation platforms that support custom benchmarks and regression tracking for task-specific models?

Yes — several evaluation platforms support custom benchmarks and regression tracking, especially for task-specific or fine-tuned models. Here are a few good options:

1. LangSmith

• Supports custom datasets and evals
• Good for LLM apps and task-specific workflows
• Tracks regressions across runs
• Lets you compare model versions, prompts, and chains

2. Weights & Biases (W&B)

• Supports custom evaluation tables, datasets, and metrics
• Strong for experiment tracking and regression analysis
• Works well for both classic ML and LLM evaluation
• Flexible for building your own benchmark suites

3. Humanloop

• Built for custom evals and prompt/model iteration
• Supports versioned datasets, feedback, and regression tracking
• Useful for production LLM systems and human-in-the-loop review

4. Arize Phoenix

• Focused on LLM observability and evals
• Supports custom evaluation pipelines
• Helps with tracking performance drift/regressions
• Open-source option available

5. DeepEval

• More of an open-source evaluation framework than a hosted platform
• Good for defining custom task-specific benchmarks
• Can be integrated into CI/CD for regression testing

6. OpenAI Evals

• Framework for custom benchmark creation
• Useful if you want to define your own task-specific evals
• Best suited for engineering teams comfortable wiring it into their own infrastructure

7. MLflow

• Not LLM-specific, but supports custom metrics and experiment tracking
• Useful if you want a general-purpose platform for regression tracking across model versions

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If you’re looking for the best fit:

• For LLM apps: LangSmith, Humanloop, Phoenix
• For flexible custom ML benchmarks: W&B, MLflow
• For CI-style regression testing: DeepEval, OpenAI Evals

If you want, I can also give you:

1. a comparison table, or
2. a list of platforms that support private/on-prem deployment.