The loop where intelligence compounds
Scaling Compute on Your Own Context
Tahuna turns real usage —traces, feedback, failures, outcomes— into evals, training runs, and better models. Deployed back into your product, on repeat.
The Platform
Primitives for
Continual Learning
One loop that compounds. Train on managed GPUs, serve any checkpoint in production, observe what production tells you, and point Hillclimb at a metric to run autonomous research that climbs it.
Tahuna Compute
Train
Post-train models and run sandboxed experiments on managed GPUs. Code, data, checkpoints, and metrics stay attached to the run that produced them.
Tahuna Serve
Serve
Promote any checkpoint into a production inference endpoint in one step. No gap between the training run and the release.
Tahuna Observability
Observe
Capture traces, feedback, evals, and outcomes from production — the evidence that decides what improves next.
Tahuna Hillclimb
Hillclimb
Autonomous research on your models. Give an agent an objective and compute: it runs experiments, benchmarks them, keeps what moves the metric, and climbs.
Bring the stack you already use
Unsloth
TRL
VerifiersYour training loop,
executed cleanly
Point Tahuna at your workspace, choose the recipe, and launch on managed GPUs.
Code, data, metrics, checkpoints, and artifacts stay attached to the run that produced them.
curl -fsSL https://tahuna.app/install.sh | bash
Build the AI that no one can replicate
From static models
to compounding intelligence
Capture the real signal
The best training signal is hidden in how people use your product: retries, edits, accepts, rejections, evals, and completed tasks. Tahuna captures this signal and turns it into a powerful engine for improvement.
Shape model behavior
Model behavior is something you shape. Tahuna starts with the primitives to run, compare, auto-research, and promote improvements under your control.
Compounding intelligence
Move beyond one-off model updates. Every run, eval, prompt, and snapshot becomes part of an improvement loop that compounds over time.
Ownership model
Everything around
the training loop
You own the learning loop: traces, evals, reward logic, rollout code, and training recipe. Tahuna handles the run lifecycle around it.
You own
Your loop
- Traces
- Feedback
- Evals
- Rewards
- Training code
- Model choices
Tahuna handles
Tahuna path
- Sync
- GPUs
- Execution
- Metrics
- Checkpoints
- Artifacts
How we partner
Frontier ML,
Embedded in your stack
We pair frontier ML — RL post-training, long-horizon agents — with real enterprise deployment, embedded in your team.
Forward-deployed
We embed with your team
Our engineers work alongside yours — from first audit to a model improving in production.
Step 1
AI Opportunity Audit
We run a structured audit of how work moves through your product and organization — across teams, systems, and decision points. The goal is to pinpoint where AI can reduce operational overhead, accelerates execution, or replaces manual coordination entirely.
Step 2
Architecture & Design
We design and build the model-improvement loop that will power the workflow — integrating your company's context, data, and operational logic behind the process.
Step 3
Production Deployment
Tahuna deploys the loop into your enterprise stack, connecting it to live systems and workflows so the model starts improving on real production work. Everything is built on top of your existing software — no migrations required.
Self-serve
Use it however you want
Your team designs and runs experiments. We manage the training, serving, and infrastructure load.
- Instant access
- CLI & full documentation
- Bring your own training loop
- Usage-based pricing
What is Tahuna?
Infrastructure for continual learning. We provide the primitives for you to own the loop — traces, evals, rewards, and training code; Tahuna runs the cloud GPU lifecycle around it and feeds every run back into the next.
Do I have to change my training code?
No. Tahuna runs your configured Python entrypoint, so your code owns the training loop. Tahuna is the control-plane that wraps it with the cloud lifecycle, observability, and autonomous research. You can use our CLI to scaffold a new project or add a few lines to an existing one.
Which frameworks are supported?
Tahuna is intentionally agnostic. PyTorch, Hugging Face, Unsloth, TRL, and Verifiers — and anything that runs from a standard Python entrypoint.
Where do my metrics go?
Logs and metrics stream live. Metrics are Weights & Biases-compatible, so you can keep using familiar dashboards.
Does Tahuna learn from production traffic?
Yes — under your control. Tahuna captures production traces, feedback, evals, and outcomes, turns them into training data and rewards, and feeds the next run. Hillclimb can run that research autonomously against an objective you set, but nothing promotes to production without your sign-off. It's a loop you own, not a black box.
How does pricing work?
You pay for active GPU time and attached volume from prepaid credits. See the pricing page for the current GPU catalog.
Which GPUs are available?
From L4 and RTX 3090/4090 up to A100, H100/H200, RTX PRO 6000, and B200 — billed per seconds.
Get started
Don't rent intelligence.
Compound it.
Bring your code, framework, and training loop. Tahuna handles the reproducible path from production signal to GPU run to deployed model — and does it again every week.