SRE, observability and cloud and AI token FinOps
Reliable systems and cloud and AI bills that every team can explain
Incidents and bills nobody can explain
Systems often fail in ways the dashboards do not show until customers notice. Alerts arrive in floods, on-call engineers chase symptoms, and the same incident returns because nobody fixed its cause.
Costs drift the same way. Cloud bills grow faster than usage, and AI makes it worse: uncapped multi-step agent workflows often run far over budget, because every step calls a large model whether it needs one or not.
What changes for you
Reliability you can measure
Each service has agreed reliability targets, and you can see at any time whether it meets them.
Fewer repeat incidents
Incidents are found early, handled around the clock, and closed with a fix for their cause.
Lower, explained spend
Every cloud and AI cost has an owner, and AI inference spend falls through caching and model routing.
What we deliver
Observability
Logs, metrics and traces collected through OpenTelemetry, with dashboards that answer real questions and alerts tied to what users experience.
Site reliability engineering
Site reliability engineering (SRE) runs operations as an engineering discipline: reliability targets for each service, error budgets that tell teams when to slow releases, and automation in place of repeated manual work.
AIOps monitoring and predictive alerting
Machine learning on your telemetry that spots unusual patterns and capacity trends before they become incidents, and groups related alerts so engineers see one problem, not a flood.
Round-the-clock SRE
Engineers on call around the clock under the response and resolution times of our service levels, with a written review of every significant incident.
Cloud and AI spend telemetry
Spending tagged and reported by team, service and AI feature, so every cost has an owner. FinOps is the practice of managing cloud spend with the same data and discipline as any other engineering work.
AI token FinOps gateway
Semantic caching answers a question that has already been answered without calling a model again. Model routing sends routine tasks, such as classification and extraction, to small language models and keeps frontier models for complex reasoning.
GreenOps and carbon-aware compute
Workloads sized and scheduled with their energy use and carbon intensity in view, with the reporting your ESG commitments call for.
How the work runs
Measure the baseline
We collect what your systems and bills show today: incidents, alerts, reliability, cloud spend and AI token use by feature.
Agree the targets
We agree reliability targets for each service and cost targets for each team with the people who own them.
Instrument and route
We add the telemetry, tune the alerts and put the caching and routing gateway in front of your AI workloads.
Operate
We run on-call with your team or for it, and close every significant incident with a review and a fix for its cause.
Review regularly
We report reliability and spend against the targets and act on what the reports show.
Proof in production
Our token FinOps gateway is measured against a baseline that sends every prompt to a frontier model, and the savings of each source are reported separately.
- lower LLM inference spend than an unrouted frontier model baseline, through semantic caching and tiered small-model routing
- 35–60%
Tools and standards
- AI and machine learning
- Mistral
- Qwen
- DeepSeek
- Databases
- Redis
- DevOps and observability
- OpenTelemetry
- Prometheus
- Grafana
- Kubernetes
How to start
What happens next
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
Delivery in five stages
Mobilisation, sprints with live demonstrations, integration and hardening, acceptance and go-live, then managed services. After launch comes a warranty period, then optional support and maintenance.
Fixed-outcome projects and round-the-clock managed services
Fixed-scope, milestone-driven delivery with named delivery leads, and round-the-clock application managed services under SLA governance.
AI-native product teams
Managed, cross-functional pods of senior engineers, equipped with agentic coding and QA harnesses.
The quickest way in is the Cloud portability and AI token FinOps audit package, one of our rapid engagement packages.
Questions and answers
What does site reliability engineering add to ordinary monitoring?
Monitoring tells you something is wrong. SRE decides how reliable each service must be, measures it against that target, and spends engineering time on the causes of failure instead of on repeated firefighting. The result is fewer incidents, shorter ones and a clear rule for when reliability work comes before new features.
What is AI token FinOps?
Language models are paid for by the token, a small piece of text in a prompt or an answer. AI token FinOps measures that spend by feature and team, then lowers it: repeated questions are answered from a cache, routine tasks go to small, cheaper models, and frontier models are kept for the tasks that need them.
How do you measure the savings?
Against a baseline we record before any change: the same workload with every prompt sent to a frontier model. The savings come from two sources, duplicate prompts answered by semantic caching and routine tasks routed to small language models, and we report each one separately.
Will caching and routing lower the quality of the answers?
Not when they are set up per use case. A task moves to a smaller model only after it passes the same evaluations as the frontier model, and cached answers are used only where a stored answer stays correct. Anything uncertain still goes to the larger model.
Can you take over our on-call?
Yes, fully or alongside your team. Under a managed services agreement we watch the alerts around the clock and respond within the times set for each severity, from a critical outage to a minor request. Every significant incident gets a written review of its cause and of the change that stops it happening again.
What is GreenOps?
GreenOps applies the FinOps approach to energy and carbon. Workloads are sized to what they use, run where and when electricity is cleaner if they can wait, and reported in the terms your ESG reporting needs.
Ready to build? Ask for a proposal.
Send your requirements or RFP. Our architects validate them and return a binding, fixed-scope proposal.
What happens next
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Binding proposal
After architecture validation at no cost, you receive a binding, fixed-scope proposal within 3–5 business days: milestones, architecture, team structure and SLAs. We sign a non-disclosure agreement (NDA) with you on request, before you share your requirements.
Mobilisation
Once you sign, our engineering pods begin Sprint 0 within 5–10 business days of contract signature.
Delivery in five stages
Mobilisation, sprints with live demonstrations, integration and hardening, acceptance and go-live, then managed services. After launch comes a warranty period, then optional support and maintenance.