platform · Microsoft

LLM Cost Optimisation with Azure OpenAI

LLM Cost Optimisation built on Azure OpenAI, chosen where it genuinely fits, and swapped where it does not.

Category
platform
Vendor
Microsoft
Alternatives we also use
9

Why Azure OpenAI for this

Semantic caching pays for itself immediately in any system with repeated questions, support assistants and internal search especially.

Azure OpenAI is strongest at enterprise compliance posture and integration with existing Microsoft estates. For llm cost optimisation that matters because the failure modes of this kind of system tend to cluster exactly there.

The honest trade-off: quota management and regional capacity can constrain scaling at short notice. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

You own the code, the models where they are open-weight, and the documentation to run it without us.

The honest assessment

What it is
OpenAI models under Azure's compliance envelope and enterprise agreements.
Strongest at
enterprise compliance posture and integration with existing Microsoft estates
Trade-off
quota management and regional capacity can constrain scaling at short notice
Category
platform

We are not a reseller for Microsoft and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.

What is included

  • Spend audit broken down by feature and by call
  • Model routing so each task uses the cheapest adequate model
  • Semantic caching for repeated and near-identical queries
  • Prompt compression that preserves meaning
  • Budget ceilings and anomaly alerts
  • Quality benchmarked before and after, so savings are not silent regressions

Questions

How much can we realistically save?

Most unoptimised systems have 40 to 70% of avoidable spend, concentrated in a few features. The audit tells you the specific number for your workload before you commit to any work.

Will quality drop?

We benchmark before and after on your real tasks. Any change that measurably degrades output does not ship. That is the whole discipline.

How long does the audit take?

About a week for most systems, and it usually pays for itself in the first month after the changes land.

Alternatives for llm cost optimisation

Same capability, different stack. Each page states its own trade-off.

Building with Azure OpenAI?

Bring us the workload and we will tell you whether this is the right stack for it.

Or email bd@dtrasglobal.com · call +91 74118 77878