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Predictable Spend, Maximum Value
Cloud unlocks speed and scale, but without disciplined financial operations it also unlocks runaway bills. Our FinOps practice combines Microsoft tooling, engineering rigour, and cultural change so finance, engineering, and product teams share a single, accurate view of cloud spend, and the levers to optimise it.
Visibility · Optimisation · Operating Rhythm
Azure bills do not grow because anyone did anything wrong. They grow because cloud makes spending frictionless and nobody owns the meter: environments outlive their projects, virtual machines are sized for a launch day that never came, and commitment discounts sit unbought because buying them needed a decision. FinOps is the discipline of giving the meter an owner.
The work starts with visibility, because you cannot manage what you cannot attribute. A tagging taxonomy that survives contact with real teams, chargeback or showback reporting, and budgets with anomaly alerts that fire in hours rather than at month-end. Most clients find surprises in the first week, and not the pleasant kind.
Then the engineering: rightsizing against actual utilisation, decommissioning the orphaned and the idle, and building a commitment portfolio across reservations and savings plans that captures discounts without gambling on the roadmap. We model your usage before you commit a pound, and the comparison below explains how we think about the choice.
AI spend gets special attention, because tokens and GPUs are the fastest-growing line on modern bills and the least understood by finance. A token is now a unit of cost the same way a virtual machine is, except it arrives in millions, scales with every user who discovers the feature, and rarely shows up in the chargeback model. We treat token spend with the same discipline as compute: budgets and quotas per team and per product, model routing so the cheap model handles the cheap work, prompt and caching discipline that cuts tokens before they are ever bought, and the provisioned-throughput versus pay-as-you-go decision modelled against real usage rather than optimism.
The output of that work is a number most organisations cannot produce today: what a conversation, a document or a transaction actually costs, per use case. Once cost per unit of work exists, AI stops being a scary line item and becomes something product owners can manage, and the agent systems we build ship with that telemetry from day one.
What makes it stick is the operating rhythm: a monthly conversation where finance and engineering look at the same numbers and agree the next optimisations. FinOps done as a one-off review saves money once; done as a rhythm, it changes how the organisation spends.
FinOps delivered with native Microsoft tooling
Establish a tagging taxonomy, chargeback model, and cost allocation framework so every pound of Azure spend can be attributed to a team, product, or workload.
Identify under-utilised VMs, idle databases, orphaned disks, and over-provisioned services, then rightsize or decommission with engineering-led recommendations.
Model commitment-based discounts across compute, databases, and storage to capture the deep discounts Microsoft offers on predictable workloads, without losing flexibility.
Configure Azure Cost Management budgets, anomaly alerts, and forecasting so cost surprises are caught early and accountability sits with the right teams.
Bring token, GPU, and Azure OpenAI capacity spend under control with quotas, model routing, prompt optimisation, and continuous monitoring of inference costs.
Embed FinOps practices, KPIs, and rituals across finance, engineering, and product so optimisation becomes continuous rather than a one-off exercise.
Spend tagged and attributable to teams
Anomaly detection on cost spikes
First savings typically landed inside the first month
Typical savings on generative AI spend from routing and token discipline
A focused engagement to surface immediate savings across compute, storage, networking, databases, and licensing, with prioritised, costed recommendations.
Define and enforce a tagging policy across subscriptions, then build chargeback or showback reporting so business units own their consumption.
Analyse usage patterns and build a portfolio of reservations, savings plans, and spot capacity that balances commitment risk against discount.
Implement quotas, model routing, capacity reservations, and token budgeting so generative AI workloads stay predictable as adoption scales.
Stand up Cost Management dashboards, Power BI reporting, and integrations with finance systems so executives and engineers see the same numbers.
Continuous FinOps run by Synapx engineers, monthly reviews, action backlogs, and measurable savings tracked against agreed KPIs.
Baseline current spend, tagging coverage, commitment portfolio, and FinOps maturity to identify quick wins and structural opportunities.
Define the target operating model, tagging taxonomy, reporting, and policies, aligned to FinOps Foundation principles and your business structure.
Roll out tagging, budgets, reservations, rightsizing, and anomaly detection in controlled waves, embedding controls in DevOps pipelines.
Run continuous optimisation cycles, measuring, reporting, and acting on savings, optionally under Synapx-as-a-Service.
Commitment Strategy
Azure gives you three ways to pay for compute, and the discounts for commitment are substantial. The catch is that commitments bought badly become their own category of waste, so the choice deserves modelling rather than a hunch.
| Criteria | Pay-as-you-go | Reservations | Savings plans |
|---|---|---|---|
| Discount | None: full list price | Deepest, on specific resources | Deep, across compute generally |
| Flexibility | Total: stop paying when you stop using | Tied to resource type, size and region | Follows your compute wherever it runs |
| Commitment | None | One or three years, per resource | One or three years, hourly spend floor |
| Right for | Spiky, experimental or short-lived workloads | The stable core: databases, always-on VMs | Steady compute that changes shape over time |
| The risk | Paying list price for workloads that never move | Committing to resources the roadmap retires | Setting the floor above where usage settles |
Every estate we review ends up with a blend, and the mix is a modelling exercise, not a matter of taste: we analyse your actual usage history before a pound is committed, then review coverage quarterly as the estate evolves. Buying commitments without that analysis is how FinOps engagements end up unwinding the previous FinOps engagement.
AI Spend
Token bills respond to different levers than infrastructure bills, and the biggest savings usually come from the levers finance cannot see. This is how we sequence them on a typical engagement.
| Criteria | Model routing | Token discipline | Capacity commitments | Quotas & budgets |
|---|---|---|---|---|
| What it is | Matching each task to the cheapest model that does it well | Prompt optimisation and caching that cut tokens before purchase | Provisioned throughput for workloads with proven, steady demand | Hard limits and alerts per team, product and use case |
| Typical impact | The largest single saving on most AI estates | Compounding: every request gets cheaper, forever | Meaningful discount, in exchange for commitment risk | Caps the downside rather than cutting the bill |
| When to pull it | As soon as more than one model is available | Once usage patterns are visible in telemetry | Only after routing and discipline settle demand | Day one, before anything else |
| The trap | Routing everything to the flagship model by default | Optimising prompts nobody measured first | Committing to throughput that routing later removes | Quotas so tight teams stop using AI at all |
Quotas first, because they stop incidents. Routing second, because it is the biggest lever. Discipline third, because it compounds. Commitments last, once demand is real and steady. Run in that order, generative AI spend becomes as governable as compute, and it is the same conversation we design into every agent system we build.
Clients moving from un-optimised Azure estates typically see a 20–40% reduction in the first six months, with a further 5–15% from ongoing optimisation. AI and generative workloads often deliver 30–50% savings through model routing, quotas, and prompt optimisation.
No. We embed FinOps practices into migrations, landing zones, and platform builds from day one so spend is governed before it grows. Engaging early avoids expensive remediation later.
FinOps is about value, not just cost. We focus on eliminating waste and right-sizing without restricting teams, using policy-as-code and automated guardrails so engineers retain autonomy within agreed budgets.
Yes. We govern Azure OpenAI, Azure AI Foundry, GPU compute, and third-party model spend with quotas, capacity reservations, model routing, token budgeting, and continuous monitoring tailored to AI consumption patterns.
Yes. Our practice is built around the FinOps Foundation principles, capabilities, and personas, combined with Microsoft Cost Management, Azure Advisor, and Azure Carbon Optimisation tooling.
Yes. Synapx-as-a-Service delivers ongoing FinOps – tagging hygiene, optimisation backlog, reservation management, anomaly response, and monthly executive reporting – with savings tracked against agreed KPIs.
In sequence: quotas and budgets per team first, so a runaway workload cannot become an incident. Then model routing, so the cheap model handles the cheap work. Then prompt optimisation and caching, which cut tokens before they are bought. Most estates find the routing step alone is the biggest single saving.
Only once demand is real and steady. PTUs trade commitment for a discount and predictable latency, which is valuable for production workloads with proven volume, and expensive for anything still finding its usage pattern. We model your actual token history before recommending a commitment, the same way we treat reservations for compute.
David, Skanska
Project/Programme Manager
Mike, Mount Anvil
Head of Technology Applications
From the very first conversation, Synapx stood out. They didn't offer a one-size-fits-all solution; they listened, understood our workflows, and constructively challenged our assumptions. Since launch, the difference has been night and day: engagement is up, performance is dramatically better, and our teams now have a single, connected space for learning and communication. I'd recommend Synapx to any organisation looking to get more out of Microsoft technology.
Skanska
Project/Programme Manager
Working with Synapx has been transformative for Mount Anvil. They didn't feel like an external supplier; they became an extension of our team. Their technical expertise in implementing Microsoft Fabric solved our immediate challenges and positioned us for future growth, including AI and machine learning. What truly set Synapx apart was their transparency, commitment, and cost-optimisation through Xscale, making Fabric financially sustainable.
Mount Anvil
Head of Technology Applications
Synapx automated complex, manual processes, modernised legacy systems, and delivered scalable Power Platform solutions that streamline workflows and provide real-time insights. Their standout achievement was integrating SiteOps for accurate, real-time management of materials and compliance across multiple sites. Thanks to their future-proof architecture, we now have a robust digital ecosystem that empowers our teams.
Seras Energy Ltd
Head of Projects & Service Delivery
Exceptional communication. The final product exceeded our expectations.
The tool has streamlined our trade management process, reduced manual workloads and significantly improved efficiency and accuracy across our operations. What truly set Synapx apart was their exceptional communication, project management, and dedication to delivering excellence. They kept us informed at every stage, addressed challenges proactively, and ensured the final product exceeded our expectations.
Nuevo Partners UK LLP
Head of Operations
Their team delivered exactly what we needed, taking time to understand the purpose and specific requirements. By linking our Typeform online surveys with our Raisers Edge database, using Power Automate, they have enabled us to automate our incoming data processes. From our initial enquiry, scoping and delivery, we found them incredibly responsive and very good to communicate with.
The Ocean Conservation Trust
Head of Fundraising
Our go-to data partner. We recommend them to every one of our clients.
We work with Synapx as our go-to and trusted partner for anything Power BI, or data in general, so much so we recommend them to all our clients! The team at Synapx work with us in a way that makes it feel like they are part of our business, our 'data team'; projects and on-going support are executed easily and effortlessly.
Lanware
Chief Technology Officer
A focused review of your Azure and AI spend that comes back with costed, prioritised savings, not a slide deck. Most clients find the review pays for itself before it finishes.
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