The same control layer works for agents, workflow tools and private in-house AI.
A single agent run can retry, spawn subagents and loop, and no per-request limit sees the total.
Every call in the run, subagents included, draws from one shared budget that shifts to cheaper models at 70%, warns at 90% and stops only at 100%.
Workflows in n8n or similar tools, wired into a CRM like Salesforce, send every step to the same expensive model, and nobody can say whether the spend pays off.
Drafting and enrichment run on cheap models, the messages that matter get a better one, and spend is tracked per run so you can compare it against outcomes.
A reply goes through draft, review, send and revision, and each pass is billed as if it were unrelated work.
The whole loop carries one run ID, so every draft and revision stays inside a single budget set for that conversation.
Generation pipelines regenerate whole assets on every failed check, and costs grow with each retry.
A vision model checks each output, only the failed parts are regenerated, and the loop runs under a run budget until a human signs off.
When customers write the requests, you can't predict which calls are easy and which are hard, so margins depend on their usage.
Each call goes to the cheapest model that meets its quality floor, with per-tenant ceilings behind the same OpenAI-compatible endpoint.
Coding agents use frontier models for lint and security-scan fixes that a small model could handle.
Routine fixes run on cheap, fast models, and only the hard cases escalate to a stronger one.
Some data can't be sent to a third-party model API, whether for contract, regulatory or policy reasons.
With Managed SLM Deployment, we fine-tune a small open-license model on your data and run it on your server, so the data never leaves.
Factory floors and field sites have unreliable connections and can't wait on a round trip to the cloud.
A small model runs locally on the device, with low latency, and keeps working offline.
No use cases in this category yet.
We fine-tune a small open-license language model on your own data and run it on your server, private cloud or edge hardware. Flux sits in front of it for routing, fallback and monitoring.
A small model is best for narrow, repeatable tasks: classifying tickets, extracting fields, answering from a fixed body of documents. For requests outside that scope, Flux can route to a large model, but only if you allow it.
| Factor | Cloud model API | Your own SLM |
|---|---|---|
| Data control | Requests go to the provider, under its data terms. | Data stays on hardware you control. |
| Per-token cost | Paid per token, every call. | No per-token fee; you pay for hardware and upkeep. [metric TBD] |
| Latency | Depends on network and provider load. | Local inference; no network round trip. [metric TBD] |
| Customization | Prompting, plus fine-tuning where the provider offers it. | Fine-tuned on your data for your specific tasks. |
| Setup effort | Low. Get an API key and start. | Higher. Needs data preparation, evaluation and deployment. |
| Best for | Broad, open-ended tasks. | Narrow, repeatable tasks. |
We map the task, the data you have and where the model will run, and agree on what "good enough" means before any training.
We fine-tune a small open-license model and evaluate it against your own examples, so you see results on your workload.
We deploy on your hardware with Flux in front, then monitor, retrain and update it over time.
Engagements are priced as a one-time setup fee plus ongoing maintenance. The base model's own license applies, you need the rights to the training data, and performance is only guaranteed as far as the signed statement of work says. Ask us for a quote.
Tell us what your agents or workflows do and what a typical run costs today. We'll tell you plainly whether Flux helps.