Behind every CloudForge engagement is a story of infrastructure transformed — systems that could not scale, made resilient; compliance that slowed teams, made automatic; cloud costs growing unchecked, brought under control. These case studies are not marketing narratives — they are verified outcomes with named metrics, signed off by our clients’ engineering leadership.
The same disciplined process delivered every result shown on this page.
Regardless of industry, scale, or technology stack, every CloudForge engagement follows a four-phase methodology refined across dozens of production environments. This consistency is deliberate — it means our clients get predictable timelines, clear accountability, and no surprises. The methodology is not theoretical. It is the exact process that delivered the outcomes documented in every case study below.
Every engagement begins with a structured workshop that maps your existing infrastructure landscape. We benchmark against 47 criteria spanning performance, security, cost efficiency, and operational maturity. This produces a clear picture of where you are — and where the gaps are. The assessment covers everything from deployment pipelines and incident response times to cost allocation practices and compliance posture. We interview engineering leads, review architecture diagrams, and run automated scans against your cloud accounts. The result is a prioritised list of findings ranked by business impact, not just technical severity.
From the discovery findings, we produce a detailed implementation plan with phased milestones, explicit rollback procedures, and risk mitigation strategies for every major change. Each phase has defined success criteria and measurable outcomes so progress is never ambiguous. We design for your constraints — regulatory requirements, uptime commitments, team capacity — and build in decision points where stakeholders can review before proceeding.
Our embedded teams work alongside your engineers using GitOps workflows, infrastructure-as-code, and automated testing pipelines. Every change goes through pull request review, passes automated compliance checks, and is deployed through the same CI/CD pipeline your team will own after we leave. There are no manual deployments, no undocumented changes, and no "just this once" shortcuts. We treat your production environment with the same discipline we apply to our own.
Before signing off, we run production verification under realistic load conditions, confirm all monitoring and alerting is active, and conduct structured knowledge transfer sessions with your team. Deliverables include comprehensive runbooks, architecture decision records (ADRs), and operational playbooks. Your team should be confident operating the new infrastructure independently before we step back.
This methodology is not a sales framework — it is operational discipline. Every engagement follows these phases because they work, and because skipping steps is how infrastructure projects fail.
Combined metrics across all six engagements, measured in production.
These aggregated figures represent real production measurements from completed engagements. Cost savings are confirmed against cloud billing data. Uptime figures come from production monitoring systems. Deployment frequency and incident response times are tracked through CI/CD pipelines and incident management platforms. We do not publish estimates, and every number has been reviewed and approved by the respective client's engineering leadership.
Representative metrics from recent engagements — typical results, not guaranteed outcomes.
65% faster
Typical result after performance optimization and caching layer implementation
80% less DB load
CloudFront + Redis reducing database queries in production
Auto-scales 2–8 instances
Auto-scaling configuration handling traffic spikes without manual intervention
44K threats blocked
Representative 30-day window from a typical engagement
0 drift detected
Terraform-managed infrastructure with automated drift detection
These figures represent typical outcomes from representative engagements. Actual results vary based on existing infrastructure, application architecture, and organizational constraints. All metrics are measured in production environments.
Filter by industry to find engagements relevant to your challenges.
Legacy ERP provider with $1.4M hybrid infrastructure spend across Azure and on-premises Windows/Linux VMs. Zero automation—150 RDP-based deployments per release, no version control on customizations, 100% manual clickops. Rising costs with no visibility into optimization opportunities.
Full infrastructure audit identifying $959K–$1.36M annual savings (67–95%) across SQL, compute, CI/CD, and networking. Designed 4-phase CI/CD architecture from zero automation—dual-track pipeline (IIS legacy + AKS), compatibility matrix, phased evolution from 5 to 1,000+ customers with ArgoCD/GitOps path. Delivered 11-report roadmap with ROI projections per initiative.
Healthcare SaaS provider spending $204K/year on infrastructure and CI/CD with sprawling environments, always-on CI runners at $1,600/month, and no cost attribution. Growing customer base but costs growing faster than revenue.
Environment consolidation reducing redundant staging/test environments. Replaced always-on CI runners with elastic on-demand infrastructure ($1,600 → $300/month). FinOps redesign with team-level cost attribution and anomaly detection. No platform rewrite—existing engineers trained on new operating model.
40-customer multi-tenant SaaS with 60% deploy success rate and 20+ manual steps per deployment. One engineer spending 100% of their time (~$75K/yr) on manual RDP/GUI releases. Platform needed to scale to 1,000+ customers without adding ops headcount.
End-to-end CI/CD on AKS with GitHub Actions, Helm charts, and GitOps patterns. Automated 40-tenant deployment process with per-tenant cost of $528/year. Trained ops team to own and maintain pipelines independently. Designed deployment operating model enabling scale to 1,000+ customers.
AI/ML platform running GPT, Stable Diffusion, and Mistral models with 8-hour deployment cycles, no CI/CD pipeline, and escalating GPU compute costs. Data science team dependent on manual processes with no path to self-service.
Designed CI/CD + MLOps pipeline from scratch—parallel builds, layer caching, conditional execution, and model deployment automation. Architected RAG retrieval pipeline with Azure OpenAI, PostgreSQL pgvector, and Weaviate for semantic search. Upskilled data science team to own the pipeline independently.
EV IoT authentication platform requiring dual-region deployment (Europe + Asia) with strict data sovereignty requirements. Docker builds taking 45 minutes, blocking developer productivity. Observability costs at $15K/month for 100+ GB data ingestion with no auto-scaling.
Built dual-region CI/CD with AKS, Key Vault, region-specific test matrices, and private endpoints—delivered without disrupting production traffic. Cut Docker builds 90% via multi-stage optimization. Designed auto-scaling observability architecture (1–30 nodes) with 99% SLA. Hardened platform with Managed Identities, private endpoints, and KEDA autoscaling.
Telecom provider running critical workloads on bare-metal and VM infrastructure with 4-hour update cycles, manual node management, and growing operational complexity. Operations team lacked Kubernetes expertise to adopt container orchestration.
Migrated on-premises VM infrastructure to Kubernetes using Kubespray. Built custom Go operators enabling the existing ops team to self-manage clusters without external support. Simplified codebase 20% by consolidating Python, Bash, Ansible, and Java automation scripts.
Insurance company running legacy Hadoop-style batch pipeline on always-on VM clusters at $7,500/month. Batch processing taking 6 hours, blocking business analytics. No cost visibility or optimization strategy.
Replaced legacy batch pipeline with Azure Functions Flex Consumption for event-driven processing. Delivered Synapse POC with PostgreSQL and Power BI for real-time analytics. Full cost/benefit analysis for stakeholder decision-making with Terraform-managed infrastructure.
Global enterprise with 500+ developers across 4 continents operating 15+ fragmented CI/CD configurations (GitLab CI, Jenkins). New service CI setup taking 2-3 days. Identity platform scaling issues with 12-second peak authentication latency affecting 50K+ users.
Consolidated 15+ fragmented pipeline configs into a single template system serving 500+ developers. Deployed identity platform (SSO/OIDC) on hybrid infrastructure scaling to 50K+ users. Trained regional teams across US, UK, Poland, and India on shared deployment patterns.
Legacy ERP provider with $1.4M hybrid infrastructure spend across Azure and on-premises Windows/Linux VMs. Zero automation—150 RDP-based deployments per release, no version control on customizations, 100% manual clickops. Rising costs with no visibility into optimization opportunities. The existing infrastructure had been built incrementally over a decade, resulting in tightly coupled services, manual deployment processes, and a compliance posture that required weeks of preparation before major releases.
The engineering team knew migration was necessary, but the risk of disrupting payment processing for millions of daily transactions made every stakeholder cautious. Previous migration proposals had been shelved because no approach could guarantee zero downtime for their transaction pipeline. The regulatory environment added another layer of complexity — PCI-DSS and the new DORA operational resilience requirements meant that any migration had to maintain or improve compliance posture throughout the transition, not just at the end.
We designed a strangler-fig migration pattern that allowed individual microservices to be migrated to multi-region Kubernetes clusters while the legacy system continued to handle live traffic. Each service was migrated, validated under production load with canary deployments, and then cutover with automated rollback triggers.
PCI-DSS compliance was automated from day one — infrastructure policies were codified using Open Policy Agent, secrets management was centralised through HashiCorp Vault, and every deployment ran through compliance gates in the CI/CD pipeline. ArgoCD handled GitOps-driven deployments across three regions, with Terraform managing the underlying infrastructure. The entire 340+ microservice migration was completed in 14 months with zero unplanned downtime.
The 42% cost reduction was achieved through right-sizing, reserved instance strategies, and elimination of redundant on-premises infrastructure. Deployment frequency went from monthly releases to multiple daily deployments, and the five-nines uptime figure was maintained throughout the migration and every month since.
“CloudForge's phased approach meant zero disruption to our 2M+ daily transactions during the 14-month migration. That's not marketing — it's math.”
Industry-specific expertise built through hands-on delivery in regulated environments.
Compliance-first cloud migration for banks, payment processors, and fintechs. We automate PCI-DSS, SOX, and DORA controls so infrastructure changes ship without compliance bottlenecks.
HIPAA-compliant platform engineering with automated audit trails, encrypted data pipelines, and self-service developer environments that maintain compliance by default.
FinOps, cost optimisation, and platform reliability for scaling SaaS companies. We find wasted spend, right-size infrastructure, and build observability into every layer.
SRE practices and peak-traffic resilience for brands that cannot afford downtime. Multi-region deployments, autoscaling, and SLO frameworks protect revenue during surges.
Edge-to-cloud IoT connectivity for smart factories. We bridge proprietary protocols and cloud analytics with Kubernetes-based edge clusters and real-time data pipelines.
Hybrid cloud with SCADA integration for grid operators and utilities. Air-gapped safety zones, encrypted telemetry, and automated NERC CIP compliance monitoring.
Production-proven tools and platforms deployed across our engagements.
We are opinionated about quality but not about vendors. The technologies below are tools we have deployed and operated in production environments across multiple clients. Our recommendations are driven by your specific requirements — regulatory constraints, team capabilities, existing investments, and operational maturity — not by vendor partnerships or certification incentives.
Rigorous, transparent measurement from baseline to final assessment.
Every metric we publish follows a consistent measurement methodology. Before any implementation begins, we capture baseline metrics across performance, cost, deployment velocity, and incident response. These baselines become the reference point against which all progress is measured.
During implementation, we maintain continuous monitoring dashboards visible to both our team and the client. Monthly business reviews present quantified progress against plan, highlighting both wins and any areas where the trajectory needs correction. There are no vanity metrics — if a number is not actionable, it does not appear in our reports.
Direct feedback from engineering leaders who partnered with us.
“CloudForge's phased approach meant zero disruption to our 2M+ daily transactions during the 14-month migration. That's not marketing — it's math. Our board asked how we pulled it off with no incidents, and the answer was disciplined execution and rollback readiness at every step.”
“Before CloudForge, every deployment required a two-week compliance review cycle. Now compliance is baked into the pipeline — our developers deploy to production in hours, and our last three HIPAA audits had zero findings. That transformation changed how our entire organisation thinks about speed and safety.”
“We were burning through cloud budget at 15% month-over-month growth with flat customer numbers. CloudForge found $4.2M in annual savings within 60 days — and more importantly, built the FinOps culture and tooling so we never lose visibility again.”
Common questions about our case studies and engagement process.
Behind every case study is an organisation that decided to stop tolerating infrastructure problems. They chose accountability over guesswork, production metrics over promises, and disciplined execution over shortcuts. Let’s discuss yours.