The open weights are free, but running them in-house is not: you need an environment, GPUs for many of the models, and a pipeline to run several of them together. Then comes the real cost — a dedicated team to calibrate, surface and maintain predictions people can trust. Orbion ships that whole layer prebuilt.
You have strong scientists, maybe an ML engineer, and you're weighing whether to stand up protein prediction internally instead of buying it.
The Difference
What Actually Separates Them
Running a protein model in-house is more than a download. Most useful models need a configured environment and GPUs, and a real workflow runs several models together — so first you're building and hosting a multi-model pipeline before a single prediction is usable.
Then comes the part that costs the most. Someone has to maintain and update the pipeline as the field moves, build an interface your scientists will actually use, and — the hard, quiet part — calibrate confidence thresholds so people don't act on an unreliable prediction. Then design the workflow so a wrong number doesn't quietly become a wrong decision.
None of that is a one-off. It is a dedicated team to build it, maintain it, and keep improving it — a standing commitment that competes with the science you hired for. Buying the judgment layer keeps your scientists on the science.
At A Glance
The Model Is The Small Part
Side By Side
How They Compare
| Build In-House | Orbion | |
|---|---|---|
| Infrastructure & GPUs | You Provision And Run It | Hosted |
| Multi-Model Pipeline | You Build And Host It | Built In |
| Time To First Result | Months Of Platform Work | Day One |
| Ongoing Maintenance | Yours, Forever | Handled |
| Confidence Calibration | You Calibrate | Built In |
| Interface For Scientists | You Design And Support It | Included |
| New Model Updates | Your Team Tracks The Field | Rolled In |
| Cost Model | Standing Headcount | Subscription |
The Hidden Work
What You End Up Owning
A Dedicated Team
Building it, maintaining it, then improving it as the field moves is standing headcount — not a one-off project.
Infrastructure And GPUs
An environment, the GPUs many models need, and a pipeline that runs several models together and keeps running.
Calibrated Confidence
Thresholds that tell a scientist when to trust a prediction. Getting the cutoffs right is a research project in itself.
Every Wrong Call
With no low-confidence flagging, a shaky prediction looks exactly like a solid one — until it costs you a bench round.
When In-House Is The Right Call
If you have a standing ML-platform team, want full control of the stack, and treat protein prediction as core IP you must own end to end, then in-house can be the right answer — and we'll tell you so. Orbion is for teams that would rather point that talent at their science.
Where Orbion Fits
The Judgment Layer, Prebuilt
- Orbion is the judgment layer, prebuilt: model routing, calibrated confidence, low-confidence flagging, and a decision-safe interface — the months of work that come after the model runs.
- Your sequences and results stay private and entirely yours. Inference only, EU-hosted, no training on your data, full IP ownership.
The model is the cheap part. The product is the judgment around it — model routing, calibrated confidence, low-confidence flags, and a workflow that stops a bad prediction from becoming a bad decision.
The Evidence
Backed By Bench Results, Not Just Claims
Prospective and blind where noted. The signal is in the ranking — absolute numbers still need your wet lab, which is why every engagement starts with a blind benchmark on your own targets.
Go Deeper
Related On Orbion
FAQ
Common Questions
The model is the cheap part. Useful prediction isn't one model either — it's several open models combined with our own — and the real cost is the infrastructure, the pipeline, the confidence calibration and the decision guardrails around them, a standing commitment rather than a one-off. Worth knowing too: many public models and hosted services train on what you send them, whereas Orbion runs inference only and never trains on your data.