DigiEdze AI · AI FinOps
Know What Every AI Decision Costs.
AI creates a new cost layer that behaves unlike anything in your existing cloud bill: it scales with usage, varies with prompt design and can multiply overnight when an agent starts retrying. We make it measurable, attributable and controllable.
- token economics
- model routing
- GPU utilisation
- inference cost
- budget controls
- chargeback/showback
Reference architecture
- 06Usage telemetry — tokens · tools · GPU
- 05Attribution & tagging
- 04Unit-cost model
- 03Routing & caching optimization
- 02Budgets, alerts & throttles
- 01Showback / chargeback reporting
The problem
What Changes When This Goes Wrong?
The AI invoice is a single line item that nobody can decompose by team or use case.
An agent loop or a retry storm turns a modest bill into an incident.
There is no unit-cost figure, so nobody can tell whether a use case is economically viable.
What we build
AI FinOps Capability Grid
Unit economics model
Cost per token, per model call, per agent run, per workflow and ultimately per business outcome — the only number that supports an investment decision.
Attribution
Every unit of spend tagged to a team, feature, tenant and owner at the gateway, so showback and chargeback are possible.
Model routing for cost
Cheaper models for the tasks that do not need frontier reasoning, with quality held by evaluation rather than assumption.
Prompt and context efficiency
Context trimming, caching and structured output — often the largest single reduction available.
GPU and inference efficiency
Batching, quantisation, autoscaling and utilisation improvement across owned and rented capacity.
Budgets and guardrails
Hard ceilings, anomaly detection and automatic throttling before overrun rather than after the invoice.
How it works
While translating AI capabilities into business benefits, we bring the right frameworks and intelligent platforms to establish and streamline business value.
Technology
- dZopti
- dZlens
- Inference gateways
- Semantic caching
- GPU schedulers
- FinOps reporting
Use Cases
Where This Lands in Practice
BFSI
AI spend attributed to the business line that generated it, reviewed monthly against value delivered.
Telecom
Routing and caching applied to very high-volume classification workloads.
Technology
Per-feature unit costs used to decide what to scale and what to retire.
Outcomes
What We Measure
Optimize your AI investments, achieve aligned business outcomes and avoid unnecessary expense. We implement best practices through continuous monitoring and observability that saves on AI spend.
- Cost per task and per outcome
- Percentage of spend attributed to an owner
- Blended cost per thousand requests
- Budget adherence
Questions
AI FinOps, Answered Directly
Why does AI need FinOps separate from cloud FinOps?
Because the cost drivers are different. Cloud cost follows provisioned capacity; AI cost follows usage, prompt length, model choice, retry behaviour and agent autonomy. A prompt change can move spend more than an instance-type change ever will, and none of that is visible in conventional cloud tooling.
Where does the largest saving usually come from?
Routing and context discipline, together. Sending easy tasks to a small model and trimming context that was never used routinely removes a large share of spend before any infrastructure change is considered.
What is 'cost per outcome'?
Total AI cost for a completed business unit of work — a resolved ticket, a processed claim, a closed reconciliation — including retries and failed attempts. It is the only figure that can be compared against the cost of doing the work another way.
Related services
Infrastructure
GPU & AI Infrastructure
GPU clusters, scheduling, serving and utilisation economics.
ExplorePlatform & Operations
LLMOps
Lifecycle, deployment and monitoring for prompts and models.
ExploreIntelligence
Custom LLM & SLM Engineering
A routed model portfolio matched to task, risk and cost.
ExploreChoose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Calculate Your AI Cost
Let us evaluate how well your AI is implemented and the costs it is adding through intelligent platforms and an approach that makes FinOps a cultural imperative.
30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT