We implement SLO/SLI frameworks, build incident response playbooks, and run chaos engineering experiments — so your team can guarantee 99.9%+ uptime backed by data, not hope.
Engineered for growing organisations.
Uptime is not a checkbox — it is a discipline that requires organizational commitment, engineering rigor, and continuous investment. Most organizations confuse monitoring with observability and dashboards with reliability. The result is reactive firefighting: an incident occurs, engineers scramble to diagnose it using scattered logs and metrics that were never correlated, a fix is applied under pressure, and the same class of failure repeats three months later because nobody had time to address the systemic cause. This cycle burns out on-call engineers, erodes customer trust, and costs orders of magnitude more than proactive reliability engineering.
Site Reliability Engineering transforms this reactive posture into a disciplined practice built on measurement, automation, and organizational alignment. The core mechanism is the error budget: a quantified tolerance for unreliability that creates explicit trade-offs between shipping velocity and system stability. When the error budget is healthy, teams ship aggressively. When it depletes, engineering effort shifts to reliability improvements. This simple framework replaces political arguments about "how much testing is enough" with data-driven decisions that align product managers, developers, and operations teams around the same objective.
CloudForge implements Google SRE principles adapted to mid-market and enterprise organizations that do not have the luxury of a dedicated 50-person SRE team. We define SLOs that map to real user journeys (not vanity metrics), build observability stacks that correlate metrics, traces, and logs across service boundaries, establish incident response frameworks with structured escalation and blameless post-incident reviews, and introduce chaos engineering to validate resilience assumptions before production validates them for you. The goal is not perfection — it is a measurable, improvable reliability practice that your team owns and evolves independently.
Common scenarios where this service delivers the highest impact.
Organization has no formal SRE practice — monitoring is ad-hoc, incidents are handled by whoever happens to be online, and there are no defined SLOs or error budgets.
Fully operational SRE program with SLO/SLI framework for critical services, on-call rotation, incident response playbook, and blameless post-incident review process — operational within 14 weeks.
Organization experiencing 12+ weekly incidents with 2-hour average resolution time, no structured response process, and a blame culture that discourages honest post-mortems.
Structured incident response with severity classification, automated escalation, war-room coordination protocols, and blameless post-incident reviews that produce actionable follow-up items — reducing MTTR to under 15 minutes.
Engineering leadership wants to define reliability targets but current metrics are infrastructure-focused (CPU, memory) rather than user-journey-focused (latency, error rate, availability).
SLO/SLI framework mapping critical user journeys to measurable indicators, error budget policies with automated burn-rate alerting, and executive dashboards showing reliability posture in business terms.
Organization claims high availability but has never tested failure scenarios — confidence is based on architecture diagrams rather than empirical evidence.
Chaos experiment catalog with controlled failure injection (network partitions, service degradation, dependency failures), game day protocols, and a resilience scorecard that quantifies actual versus assumed fault tolerance.
On-call rotation causing burnout — engineers paged 15+ times per week, 40% of alerts are false positives, and no clear escalation path exists for complex incidents.
Restructured on-call with alert deduplication, severity-based routing, automated runbooks for common scenarios, balanced rotation schedules, and a target of fewer than 2 actionable pages per on-call shift.
A proven methodology built for growing organisations.
Identify critical user journeys and define meaningful service level objectives
Deploy metrics, logs, and traces with correlated alerting and dashboards
Build escalation paths, runbooks, and blameless post-incident review processes
Introduce controlled failure injection to validate resilience assumptions
A healthcare SaaS platform serving 500+ clinics experienced 12+ weekly incidents with 2-hour average resolution time. No structured incident response existed — the CTO was personally paged for every severity. On-call engineers were burning out, and customer churn was directly correlated to reliability incidents.
CloudForge implemented a comprehensive SRE program: SLO/SLI framework for 5 critical user journeys, structured incident response with severity-based escalation, observability stack with correlated metrics/traces/logs, blameless post-incident reviews, and a chaos engineering program that validated failover assumptions.
Before CloudForge, I was personally handling every major incident at 3 AM. Six months later, our team runs a disciplined SRE practice — incidents are rare, response is structured, and I have not been paged in four months. The error budget framework finally gave us a language to discuss reliability trade-offs with our product team.
— CTO, European Healthcare SaaS Platform
Metrics collection with PromQL-based alerting and Thanos for multi-cluster long-term storage, providing unlimited retention and global query view across federated Prometheus instances.
Visualization and alerting platform for SLO dashboards, error budget burn-rate tracking, incident timelines, and team-level reliability scorecards with unified alerting across data sources.
Vendor-neutral distributed tracing and metrics instrumentation providing end-to-end request correlation across microservices — critical for diagnosing latency and error propagation in distributed systems.
Incident management platforms with severity-based routing, escalation policies, on-call scheduling, and integration with observability tools for automated incident creation and context enrichment.
Controlled failure injection frameworks for validating resilience assumptions — Chaos Monkey for random instance termination, Litmus for Kubernetes-native chaos experiments with CRD-based experiment definitions.
Distributed trace analysis for debugging request flow across microservices — identifying latency bottlenecks, error sources, and dependency failures through visual trace exploration and comparison.
SLO/SLI framework defined for critical services — user journey mapping complete, reliability indicators selected, and burn-rate alert thresholds calculated.
Observability stack deployed and alert routing configured — Prometheus/Grafana operational, distributed tracing active, and symptom-based alerting replacing noisy infrastructure alerts.
Incident response framework live and first chaos experiments completed — escalation matrix active, blameless review process established, and initial resilience findings documented.
Error budgets operational with organizational adoption — executive dashboards live, on-call rotation optimized to <2 pages/shift, MTTR consistently under 15 minutes.
Our SRE practice is led by Google SRE-certified engineers who have implemented reliability programs for organizations handling 2M+ daily transactions across financial services, healthcare, and e-commerce. We do not teach theory — we build operational SRE programs with SLO dashboards, incident response playbooks, and chaos experiments that are running in production when we hand over.
We adapt Google SRE principles to organizations that do not have 50-person reliability teams. A 10-engineer startup and a 500-engineer enterprise need fundamentally different SRE implementations. We calibrate complexity to organizational maturity — starting with core SLOs and incident response, then layering error budgets, chaos engineering, and capacity planning as your team grows into them.
Reliability is a cultural transformation, not a tooling project. We embed alongside your engineering teams during incident response, facilitate blameless post-incident reviews, and coach engineering managers on error budget policies. The tools are important — but the organizational behavior change is what sustains reliability improvements after our engagement ends.
Our chaos engineering practice has designed and executed 100+ controlled failure experiments across production and staging environments — from network partition injection to cascading dependency failures. Every experiment follows a scientific method: hypothesis, controlled blast radius, steady-state verification, and documented findings that feed back into architecture improvements and runbook updates.
Let's start with a technical conversation about your specific needs.