We make the cloud cost of AI workloads visible; we optimize GPU/TPU allocation and tie AI spend to business value with the FinOps 2.0 framework.
The critical topics this service addresses and the outcome we deliver in each.
With an AI workload cost inventory and consumption analysis, we make spend manageable through a baseline measurement, target and review cadence.
Through GPU/TPU utilization and idle analysis we prevent resource waste and separate training and inference cost.
With an AI ROI measurement framework and KPI definitions we tie investments to business value and provide department-level cost transparency.
With a FinOps dashboard, anomaly detection and a monthly review loop we catch budget overruns early and sustain optimization.
How we phase the service across delivery, governance, and connected service pillars.
We begin with an AI workload cost inventory and consumption analysis, producing GPU/TPU resource utilization, training/inference cost separation and an AI ROI measurement framework.
We apply optimization: GPU/TPU rightsizing, mixed precision/distillation/quantization, inference endpoint autoscaling/serverless assessment and a FinOps tagging/chargeback model.
We deliver an AI FinOps report, an optimization action plan, an ROI dashboard (Power BI/Grafana) and a monthly review template, and provide 3 months of monitoring and tuning support after the first loop.
Illustrative surfaces where this service is commonly activated.
Building the habit of correct tagging and cost monitoring at small scale to prevent uncontrolled growth as it scales.
On-premise TCO for sustained high utilization, cloud GPU flexibility for variable workloads; an economic analysis of the hybrid model for most scenarios.
Balancing training and batch-inference workloads to spot and interruption-sensitive production workloads to on-demand/reserved capacity.
This service's depth on sector-specific technical and compliance topics.
Reducing training time and inference cost with mixed precision, gradient checkpointing, distillation and quantization; proving the impact with PoC/baseline measurement.
Department-level AI cost transparency through tagging, showback/chargeback and anomaly detection; cloud cost API integration.
A Power BI / Grafana dashboard with Prometheus/Datadog integration; a monthly FinOps review and quarterly strategy update.
AI and machine learning workloads introduce a new category of cloud cost complexity that traditional FinOps tooling was not designed to manage. GPU compute, large model inference endpoints, training job orchestration, and token-based API consumption create unpredictable and rapidly escalating spend. Without AI-specific cost governance, organisations risk over-provisioning expensive GPU instances, paying for idle inference endpoints, and lacking the visibility needed to justify AI investment to the board. Our AI Cost Optimisation practice delivers FinOps 2.0 capabilities purpose-built for AI workloads.
Make cost and resource optimization measurable against the agreed baseline and review cadence
The AI FinOps engagement covers current-state AI spend discovery, cost optimisation architecture, implementation of governance tooling, and ongoing managed optimisation reporting. We address the full AI cost stack from infrastructure compute and storage, through MLOps platform costs, to third-party model API consumption. The scope includes both training and inference phases of the AI lifecycle.
Turn the outcome into a measurable target with baseline, owner, and review cadence
Make cost and resource optimization measurable against the agreed baseline and review cadence
Enable AI programme scale without proportional cost growth through efficient model serving architectures
AI FinOps deliverables combine financial reporting artefacts with technical implementation guides and ongoing optimisation recommendations. Board-level financial summaries are complemented by engineering-level configuration guides that enable your platform teams to sustain optimisations independently. All dashboards are built on open standards to avoid proprietary tool lock-in.
Turn the outcome into a measurable target with baseline, owner, and review cadence
Enable engineering teams to self-serve cost optimisation decisions with real-time dashboards
Document AI spend governance for ESG and sustainability reporting requirements
We implement a tagging taxonomy at provisioning time that maps GPU instances, storage volumes, and API gateway keys to cost centres, product codes, and team identifiers. For token-based API consumption, we deploy an AI gateway layer that enforces tagging on all outbound API calls and produces per-team usage reports integrated with your existing FinOps dashboard.
Yes. We produce a 3-year TCO model comparing cloud GPU spot and on-demand pricing against on-premises GPU server capital and operational costs, factoring in utilisation rates, power and cooling, and personnel costs. The model includes break-even analysis and scenario modelling for different workload growth trajectories, giving your leadership team the data needed to make an informed investment decision.
Spot instance interruption is the primary risk, but it is fully manageable with proper checkpointing. We implement automatic checkpoint saving to object storage at configurable intervals (typically every 30 minutes) so that interrupted training jobs resume from the latest checkpoint rather than restarting from scratch. With this in place, spot instances can surface cost savings against the agreed workload baseline with negligible additional training time.
For real-time inference we focus on model efficiency rather than instance type arbitrage. Techniques include model quantisation, batching strategies, scale-to-zero configurations, and model routing, with compute impact measured against the agreed workload baseline and acceptance targets.
We establish a cost baseline in the first two weeks of the engagement using 90 days of historical billing data. All subsequent optimisations are measured against this baseline with full methodology transparency. Savings are categorised as realised (implemented and running), committed (implementation in progress), and identified (recommended but not yet implemented) to give an accurate picture of value delivered.
Yes. We design AI FinOps as an extension of your existing FinOps practice, using the same cost centre taxonomy, chargeback model, and reporting cadence. AI-specific metrics such as cost per training run, cost per inference request, and GPU utilisation rate are added as new dimensions to your existing FinOps reporting, rather than creating a separate parallel process.
This short form routes your request to the right support team. We clarify context first, then define the safe sharing method.
Privacy-aware first contact; safe sharing flow when needed; no sales pressure.
