Open models. Proven on your traffic.
Test a small slice, serve what works, and adapt your model with your own data.
AI investment is growing. Production still takes work.
$37B
Estimated enterprise generative AI spend in 2025. Based on a survey of about 500 U.S. decision-makers and market modeling.
76%
Enterprise AI use cases purchased rather than built internally. Based on the 2025 enterprise AI survey.
$20 vs $1
Illustrative cost per million output tokens: $20 for a frontier API versus $1 for an open model.
Industry spend and adoption source: Menlo Ventures, 2025. Token costs are illustrative, not Kordus results.
What Kordus does
A safer path onto open models.
Canary it.
Send a small share of traffic to an open model before anything else changes.
Serve it.
Run inference on the open model where the canary looks good.
Train it.
Post-train on your own data when you want the model closer to the job.
The product
From a small test to a model you can keep improving.
Canary. Inference. Post-training.
Canary rollouts
A small slice of traffic, with a way back.
Inference
Open-model serving for the traffic that is ready.
Post-training
Your data, used to push the open model further.
Quality checks
See whether the open model holds before you expand.
Keep what works
Frontier calls can stay on the traffic you are not ready to move.
Your models
Open weights you can keep working on.
Frontier API / Kordus
Frontier API
Full price on every token
Switch everything at once
The provider serves every call
The model stays as shipped
One vendor
Quality is assumed
A lower price where an open model holds
Canary a small slice first
You serve the traffic you move
Post-train it on your data
Room to try open models
Quality is checked on your traffic
Pricing
Early access.
Tell us what you want to try. We are learning which parts matter.
Early-access pricing
Let’s scope your rollout
Pricing is tailored to your model, traffic, and training needs. Start with canary rollout, inference, or post-training.
Canary rollout.
Inference.
Post-training.
FAQ
Questions
Start with a small slice of traffic.
Canary an open model. Serve it if it holds. Post-train it if you want it closer.
