Keeping AI Spend Predictable
Organisations often face unexpected AI costs due to unplanned usage across departments. The eLLM system helps manage these expenses by routing queries to local models for everyday tasks, while reserving cloud models for more complex needs.
The problem: a bill that grows on its own
AI adoption rarely arrives as a single planned rollout. It creeps in. A department starts using a cloud AI tool for drafting, another picks it up for research, someone else starts running documents through it for summaries. Each use looks small and reasonable on its own. Added together across an organisation, the cost can climb well past what anyone budgeted for, and it often isn't clear until the invoice arrives.
The difficulty isn't that cloud AI models are expensive for what they do. It's that usage is hard to see and harder to forecast when every user, department, and query is a separate decision made without reference to a budget.
Why cutting off cloud access isn't the answer
Cloud based AI models are genuinely useful for the harder end of the task spectrum, the queries that need more reasoning or broader general knowledge than a smaller local model can offer. Removing access to them entirely to control cost also removes the capability that made people want to use AI in the first place. The better approach is knowing where the spend is going and having a way to direct it.
How eLLM addresses this
Local models handle the everyday questions.
A large share of day to day queries, drafting an email, summarising a short document, answering a routine question, don't need a large cloud model at all. Running these on local models means they cost nothing extra per query.
Cloud models are available with budget management built in.
When a task does need a larger cloud model, eLLM supports this as an option with privacy controls and budget management, so cloud usage is a deliberate, visible choice rather than something happening quietly in the background.
Automatic routing sends queries to the right place.
Rather than relying on individual users to judge which model a question needs, eLLM can route straightforward queries to local models and reserve cloud models for the tasks that genuinely warrant them.
The outcome
Staff still get access to more capable cloud models when a task calls for it. The organisation gets a system where that access is managed and budgeted, rather than an open tap that only gets noticed once the bill lands. IT can see where cost is coming from and adjust settings accordingly, instead of finding out after the fact.
Learn more
The eLLM Knowledge Base covers how local and cloud models work together, how automatic routing decides where a query goes, and how budget controls are configured for cloud usage.
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