LLMs have quietly become a verification layer in security tooling: a second pass that reads first-stage findings and filters false positives. For many teams the open question is not whether to use one, but where it should run.
Why on-prem keeps coming up
In finance, healthcare and the public sector, source code and vulnerability findings are among the most sensitive artifacts an organization holds. Sending them to a cloud API, even transiently, can be a compliance non-starter. Running the model inside the network removes that objection entirely.
The trade-offs
- Accuracy: the real question is whether an in-network model matches a frontier cloud model on the task. On security benchmarks this gap has been closing, and in some published comparisons the two are effectively tied.
- Latency and throughput: a single on-prem deployment can be tuned for batch auditing rather than interactive chat, which changes the hardware math.
- Operations: you own the uptime, the updates and the GPUs. That is a cost, and for some teams it is exactly the control they want.
What to measure before you decide
Pin the benchmark and the version, compare the on-prem and cloud configurations under identical conditions, and separate self-measured claims from results run through an official harness. If the in-network configuration holds its score, the sovereignty win comes for free. If it does not, you are trading accuracy for control, and that should be a deliberate choice.
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