Why run models yourself?
You can run open-weight coding models on your own servers. For the right workload, that means a smaller bill and more control over company code.
Spend less on coding AI.
For steady use, running a model yourself can cost less than paying for every API call. We do the maths on your usage.
Quality tested on your work.
Open-weight models can match proprietary models on some coding benchmarks. We test coding quality and response time on your own tasks.
Qwen3-Coder-Next technical reportMore control of company code.
Choose where prompts, code, and logs are handled, who can access them, and when your model changes.
What we work on together.
Choose and set up the model.
Work out the model and hardware, install it, and configure the server.
Connect it to your team.
Get your apps talking to the model. Test that the setup works and leave clear operating notes.
Secure the whole setup.
Control access, protect prompts and logs, and check what connected tools can do.
Start with a conversation
Tell us what you need.
Tell us what you want to run and what hardware you have. We’ll quote for the setup and security work.
Pricing depends on your setup and the work involved.
We agree access, data handling, and support for your setup. Connected tools can still send data outside your servers. Quoted separately from the Shielda platform preview.
A few fair questions.
What does on-prem mean?
On-prem means the model runs on your company’s own hardware. Self-hosting can also use dedicated cloud servers. We agree where prompts, code, logs, and tool traffic go before setup.
Are open-weight coding models as good as proprietary models?
They can be comparable on specific coding benchmarks, but results depend on the task, model, and setup. We test coding quality and response time on your real work before proposing a switch.
Qwen3-Coder-Next technical reportCan we save up to 90% on coding AI?
Savings at that scale have been reported on model bills. Revolut described a self-hosting invoice about one-tenth of its previous provider spending. That does not establish a 90% saving on total development costs. We include hardware, power, operations, licensing, and setup when assessing your workload.
Revolut’s AI Engineer talkWhen does running our own model make financial sense?
We compare your recurring API bill with the full cost of a local setup, then test quality and speed at your expected usage. We show the assumptions and break-even point before you commit. Low or irregular usage can favour a hosted API. Contact support@shielda.ai for an assessment.
Sources and cost assumptions
- Qwen3-Coder-Next technical report
The authors report task- and agent-dependent benchmark results, with remaining gaps on complex work.
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- Revolut’s AI Engineer talk
A speaker-reported comparison of model invoices. Matched usage periods and a full ownership-cost breakdown were not published. This is an industry example, not a Shielda customer result.
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