One runtime, not four dashboards

Helios Labs was founded by infrastructure engineers who kept watching the same movie: a team ships an LLM feature, then stitches together four tools to keep it alive — one for training runs, one for evals, one for logging, one for monitoring. Every handoff between them loses context, and nobody can answer the only question that matters when something breaks: what changed? We built a single runtime where those stages share one source of truth.

What the platform does

Helios is built for ML platform teams and application engineers shipping AI features to production:

• Training and fine-tuning runs with reproducible environments — every run is tied to its data snapshot, config, and hardware.
• Evaluation harnesses that live next to the training loop, so a model is scored against your real task suite before it can reach staging.
• Observability for deployed models: latency, drift, and quality signals on the same timeline as the eval history.
• A single-line SDK integration that replaces the glue scripts most teams maintain by hand.

How we work

We ship weekly and publish changelogs that read like an engineer wrote them, because one did. Our support is handled by the same engineers who build the runtime — when you report a regression in eval scoring, the person who answers is the person who can fix it. We price per seat rather than per token, so teams are never punished for instrumenting thoroughly.

Reliable AI features are not a model problem; they are an infrastructure problem. Helios Labs exists to give ML teams the runtime they should have had from day one — one place to train, evaluate, and observe, so shipping stops being an act of faith.