Comparisons Local vs cloud models
Compare · Approaches
Local models vs cloud frontier models for the SOC
Language models are now part of security operations: summarising alerts, writing investigation briefs, drafting queries. The choice between a model you host and a frontier model in the cloud is not only about quality. It decides where your security data goes, what the bill looks like at full alert volume, and what happens when the connection drops.
What SOC work asks of a model
A SOC produces a steady, large stream of text: alerts, logs, process command lines, case notes. Much of the model work on it is repetitive and narrow, such as classifying, extracting, summarising, writing the same kind of brief again. A smaller part is open-ended: reasoning about a novel attack, reading an unfamiliar log format, explaining a complex chain.
Security data is also sensitive. It names internal hosts and users, shows where defences are weak and sometimes contains personal data or secrets. Where that data is processed is a governance decision, not just an engineering one.
Side by side
| Dimension | Local models (on-premise) | Cloud frontier models |
|---|---|---|
| Where data goes | Stays in your environment | Sent to the provider, under its terms and region |
| Cost at high volume | Mostly fixed hardware cost; does not grow with every token | Grows with every token processed |
| Breadth of capability | Narrower; strong when specialised for a task | Broad general reasoning and knowledge |
| Specialisation | Can be adapted to your environment and tasks | Limited to prompting and the provider’s options |
| Latency and availability | Predictable; works offline and in air-gapped sites | Depends on the network and the provider’s service |
| Upkeep | You run hardware, updates and evaluation | The provider runs the model; you manage usage and policy |
| Regulatory fit | Simpler for sovereignty and data residency rules | Needs review of transfers, contracts and retention |
When each is the right choice
Cloud models are fine when
Data is not sensitive or can be masked first, volume is low, rules allow it, and you need broad reasoning on rare, hard questions.
Local models fit best when
Data must stay in the country or the building, volume is high and steady, networks are restricted, or cost must stay predictable.
Many teams combine them
Local models carry the everyday volume; a frontier model is called only by policy, on masked data, for the few cases that need it.
How ApexAI approaches it
ApexAI is local-first. It runs on your infrastructure, and its default models run there too, so security data stays with you and cost does not grow linearly with tokens. The models keep learning from your own environment’s settled outcomes. A cloud frontier model is optional: it is off by default, and when you enable it, calls go through a server-side gateway under your policy, with sensitive values masked and budgets applied.
Questions
Are local models good enough for security operations?
For the narrow, repetitive tasks that make up most SOC model work, a smaller model specialised for the task can perform well. Open-ended reasoning on rare cases is where large frontier models still have an edge.
Is it safe to send alerts to a cloud model?
It depends on what the alerts contain, the provider’s terms and the rules you are under. Masking sensitive values, limiting what is sent and recording every call reduce the risk; keeping the data local avoids it.
Why does cost matter so much?
A SOC sees a large, continuous stream of events. If every alert passes through a metered model, the bill grows with the alert volume. Local models turn most of that into a fixed cost.
What is model distillation, and why does it matter here?
Distillation trains a smaller model to reproduce a larger model’s answers on a specific task. It is one way to get strong task performance from a model small enough to run locally.
See ApexAI on your own alerts.
A walkthrough with your team, in observe mode first: ApexAI scores and proposes, nothing acts on its own.