Move your AI off rented APIs and onto private models that run in your own cloud. Faster, cheaper, and yours. Start with a free 30-minute teardown of your LLM bill.
With a senior AI engineer, not a sales rep. Step 1 of 2.
Next, you pick a time. Happy to sign your NDA before the call.
A fine-tuned Qwen3.5-9B beat Claude Opus 4.5 at catching fraud, with 12 points better recall and 55% lower latency. Training cost: under $100.
Runs open-source models that perform close to frontier models at less than 10% of the cost.
Its support agent leans on Alibaba’s open Qwen models, which CEO Brian Chesky called very good, fast and cheap.
Built its Composer 2 model on the open-weight Kimi K2.5, then trained it further on its own data.
An open-weight Kimi model edged out Claude Sonnet 4.6 on its internal eval at about a third of the cost.
Public examples reported by the companies and press named above. They are not Crowdlinker clients.






Walk us through your AI features, the models behind them and roughly what you spend. No data export needed.
We size each workload and check which open-weight models can match it, hosted or in your own cloud.
A short written plan: what to move, to which model, expected savings, and what stays on frontier models. Yours to keep.
Not at everything. Frontier models still lead on hard, open-ended reasoning. But for narrow, high-volume tasks like classification, extraction, routing and support, a fine-tuned open model often matches or beats them. The plan tells you which is which.
No. Open models can run on hosted providers, in your own cloud account or on-prem. We recommend the simplest option that meets your cost, latency and data needs.
It’s how we both find out if moving is worth it. If the savings aren’t meaningful, we’ll say so, and you keep the plan.