HUD
A platform for creating, running, and reviewing reinforcement-learning tasks and benchmarks.
- Tool type
- RL task and benchmark platform
- Made for
- Researchers and task-building teams
- Runs on
- Web app and Python
- The web app supports no-code task creation; the SDK defines environments in Python. The quickstart runs environments locally.
What it does
HUD is an RL task and benchmark platform for researchers, task authors, and teams evaluating or training models. Tasks combine an environment where an agent acts with a grader that evaluates the result. You can create tasks in the web app from files and a rubric, import Harbor tasks, or define environments in Python with the HUD SDK. Tasksets group tasks for evaluation and review.
Run tasks against one or more models, with configurable repetitions. HUD records each attempt as a trace containing the agent’s trajectory and reward; traces can be replayed and reviewed. QA agents examine tasks and traces for issues such as reward hacking, grader mistakes, and gaps between prompts and criteria. Teams can revise tasks, move them through review, publish benchmarks, or train models on tasksets.
The web workflow requires a HUD account and credits for runs and QA; a new team starts without credits. SDK users need Python and a HUD API key, and can run an environment locally with the CLI before viewing traces on hud.ai. Usage draws on credits for environment runtime and model calls; model calls through HUD’s gateway include a platform fee, while using your own provider key also incurs charges from that provider. Evaluation results depend on the task, grader, and model; QA reviews are billed separately.
What it can do
- Create tasks from files and rubrics in the web app
- Define environments and tasks in Python with the SDK
- Import Harbor tasks and runs
- Run tasksets against models with configurable repetitions
- Record and replay traces with trajectories and rewards
- Review tasks and traces with QA agents
Works with
- Harbor task imports
- Model providers through HUD's gateway or user-supplied provider keys
- REST API
Before you choose
- A new team starts with no credits; environment runtime and model use consume credits.
- Model calls through HUD's gateway are charged by token use plus a 5% platform fee; using a provider key also means the provider bills for tokens.
- QA reviews incur model-call and environment-runtime costs, so reviewing traces adds usage.
Listed under
Get started
Sign in at hud.ai; runs require credits. SDK use requires an API key.
- Create a HUD account and, for web runs, add credits or subscribe to Pro.
- For SDK use, install the HUD CLI or Python package and obtain an API key.
- Create or import a task, run it against a model, then inspect its trace and optional QA review.
Plans and prices
Prices in USDPrepaid USD credits cover usage; Pro costs $50/month and adds $50 credits. Compute charges depend on size/resources: EC2 has a five-minute minimum then per-minute billing; Modal uses per-second CPU/memory/GPU billing. Gateway model tokens add a 5% platform fee. With your own provider key, HUD charges 5% of the equivalent call cost and the provider separately bills tokens. QA also consumes model and runtime usage.
- No subscription
- Usage is paid from credits added to the team's balance
- Each paid month adds $50 of credits
- Higher limits than Pay as you go
- Custom pricing
- No credit check before work starts
- Runs up to 24 hours
- Unlimited storage
- No per-minute limit on model calls
- Sign in with HUD for a customer's own product users
Source: docs.hud.ai/platform/billing.md, checked 1 Nov 2026. Prices can change; check before you buy.
Sources for this overview
Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.
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Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.