SaaS & Technology

LLM Cost Optimisation for SaaS & Technology

LLM Cost Optimisation for saas & technology, built around the constraint that defines the sector: per-tenant economics and enterprise security review decide whether a feature can ship.

Regulations in scope
4
Systems we integrate
4
Typical first release
6 weeks

What changes when it is saas & technology

Orqent Labs audits AI spend and typically removes 40 to 70% of it with no measurable quality loss, and we show the benchmark both ways.

In saas & technology, per-tenant economics and enterprise security review decide whether a feature can ship. That single fact reshapes how llm cost optimisation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is onboarding automation, usually integrated against your own product. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.

The sector constraints we design around

Defining constraint
per-tenant economics and enterprise security review decide whether a feature can ship
Regulations in scope
SOC 2 · ISO 27001 · GDPR and DPDP · customer data processing agreements
Systems of record
your own product · billing and metering · customer data platform · support tooling
Where we usually start
in-product AI features

LLM Cost Optimisation workloads in saas & technology

  • in-product AI features
  • usage-based metering for AI
  • support deflection
  • onboarding automation
  • churn prediction

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 from this sector

How do we price AI features?

Usually usage-based or tiered, and either way you need per-tenant cost visibility first. Flat pricing on variable inference cost is how margin disappears.

Will enterprise customers accept it?

If you can answer the security questionnaire, data handling, subprocessors, training opt-out, residency. We build so those answers are straightforward.

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.

LLM Cost Optimisation for saas & technology, worth a conversation?

Tell us the workload and the regulation it sits under. We will tell you what is realistic.

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