Cloud costs keep rising
Oversized workloads, unused resources, weak visibility, and poor scaling rules quietly drain budget.
Cloud Infrastructure • Reliability Engineering • AI Infrastructure
Cloud & AI Infrastructure, built for scale and reliability
Cloudico helps SaaS, AI, and engineering teams design, automate, optimize, and operate production-ready infrastructure across AWS, GCP, Azure, Kubernetes, Terraform, CI/CD, observability, and GPU workloads.
Built for teams dealing with scaling pressure, rising cloud costs, reliability gaps, and complex production infrastructure.
Kubernetes
Terraform
AWS / GCP / Azure
Observability
FinOps
GPU Workloads
Terraform
AWS / GCP / Azure
Observability
FinOps
GPU Workloads
Production Infrastructure Map
Cloud, delivery, observability, cost, and AI workloads
Cloud, delivery, observability, cost, and AI workloads
Review ready
AWS / GCP / Azure
Landing zones, networking, migration, environment standards
Landing zones, networking, migration, environment standards
CI/CD + IaC
Terraform, GitHub Actions, GitLab CI, Argo CD
Terraform, GitHub Actions, GitLab CI, Argo CD
Kubernetes Reliability Core
Clusters, autoscaling, policy, deployment safety, runbooks
Clusters, autoscaling, policy, deployment safety, runbooks
Observability
Prometheus, Grafana, Datadog, OpenTelemetry, alerts
Prometheus, Grafana, Datadog, OpenTelemetry, alerts
AI Workloads
LLMs, RAG, vector stores, GPU workloads, fine-tuning
LLMs, RAG, vector stores, GPU workloads, fine-tuning
Cost-Aware Operations
FinOps review, right-sizing, waste detection, usage visibility
FinOps review, right-sizing, waste detection, usage visibility
Trusted by teams that need infrastructure to stay stable, scalable, and cost-aware.
Proof areas are ready for real Upwork badges, certifications, logos, and client results.
Proof areas are ready for real Upwork badges, certifications, logos, and client results.
Cloud PlatformsAWS, GCP, Azure, multi-cloud environments
Delivery StackKubernetes, Terraform, CI/CD, containers
OperationsObservability, SRE, incident readiness
AI InfrastructureLLM apps, RAG, GPUs, vector databases
The real problem
Infrastructure problems do not stay technical for long.
When cloud systems are not designed carefully, the damage shows up everywhere: rising bills, unstable deployments, slow release cycles, weak observability, incident chaos, and teams stuck in firefighting mode.
Deployments feel risky
CI/CD gaps, manual releases, and fragile environments make every deployment slower than it should be.
Kubernetes is hard to operate
Clusters work at first, but scaling, governance, resource limits, and observability become painful later.
Incidents are reactive
Without proper monitoring, alerts, runbooks, and readiness, teams discover problems after users do.
AI workloads need stronger infrastructure
LLMs, RAG systems, vector databases, GPU jobs, and fine-tuning need more than basic hosting.
Core services
Specialized cloud engineering for production teams
Each service is packaged around what the buyer actually needs: clarity, implementation, reliability, cost control, or AI workload readiness.
Cloud Infrastructure Engineering
01
Design, automate, and modernize cloud environments using AWS, GCP, Azure, Kubernetes, Terraform, and CI/CD pipelines.
- Cloud architecture and migration
- Kubernetes setup and optimization
- Terraform infrastructure automation
- CI/CD pipeline implementation
- Environment standardization
Cloud Cost Optimization
02
Find and reduce cloud waste without weakening performance, reliability, or engineering velocity.
- FinOps review and reporting
- Kubernetes resource right-sizing
- GPU workload optimization
- Idle resource analysis
- Scaling and autoscaling review
Reliability Engineering
03
Improve production stability through observability, incident readiness, scaling strategy, and performance optimization.
- Monitoring and alerting
- Observability architecture
- SRE practices and runbooks
- Incident readiness
- Performance review
AI Infrastructure
04
Deploy and scale AI workloads with infrastructure designed for LLMs, RAG systems, vector databases, GPU workloads, and fine-tuning.
- LLM and RAG infrastructure
- Vector and graph databases
- GPU infrastructure planning
- AI deployment pipelines
- Fine-tuning environments
Measured outcomes
Infrastructure outcomes your team can measure
The goal is not more tools. The goal is infrastructure that makes delivery faster, incidents clearer, and cloud spend easier to control.
Cost visibilityHigh priority
Deployment confidenceProduction focus
Incident readinessOperational lift
AI workload readinessScale path
Lower cloud waste
Find avoidable spend across workloads, Kubernetes resources, GPU usage, and idle infrastructure.
More reliable deployments
Reduce manual release risk with cleaner CI/CD, environment discipline, and rollback paths.
Faster provisioning
Turn repeated infrastructure work into versioned, reviewable, reusable automation.
Production-ready Kubernetes
Improve resource requests, limits, autoscaling, observability, and operational governance.
Better incident readiness
Add dashboards, alerts, runbooks, ownership, and signals your team can actually act on.
AI workload confidence
Move LLM, RAG, vector, and GPU workloads toward a more reliable production foundation.
How Cloudico works
A clear engineering process from review to production
Cloudico’s buying journey should feel calm and concrete: assess the current system, design the right target state, then build and hand over with clarity.
Assess
Review infrastructure, workloads, costs, reliability gaps, tooling, and deployment flow.
Design
Create the target architecture, roadmap, risk areas, and success metrics.
Build
Implement cloud infrastructure, automation, Kubernetes, CI/CD, observability, or AI deployment systems.
Optimize
Improve cost, performance, scalability, security posture, and operational readiness.
Handover / Operate
Provide documentation, runbooks, knowledge transfer, and optional ongoing support.
Project snapshots
Proof slots for real client work without inventing claims
These cards are written as honest project-snapshot placeholders. Once you have verified client details, we can turn them into full case studies.
Scaling workloads without losing operational control
Review cluster architecture, workload sizing, autoscaling, deployment flow, and observability gaps.
Reducing cloud waste while protecting reliability
Identify idle resources, oversized workloads, weak scaling policies, and unclear cost ownership.
Moving AI systems from prototype to production
Plan infrastructure for LLM apps, RAG pipelines, vector databases, GPU workloads, and deployment reliability.
Client video testimonials go here
Use real 20 to 30 second founder, CTO, or engineering lead clips. The layout is already prepared so videos can be added without redesigning the section.
“Cloudico understood the infrastructure problem behind the surface symptoms and gave us a practical path forward.â€
Placeholder quote until a real testimonial is approved
“The review focused on reliability, cost visibility, and the deployment risks our internal team had been carrying.â€
Placeholder quote until a real testimonial is approved
“The work felt like engineering partnership, not generic consulting.â€
Placeholder quote until a real testimonial is approved
“We left with clearer architecture decisions, better observability priorities, and a roadmap our team could execute.â€
Placeholder quote until a real testimonial is approved
Technical fit
Built around the stack modern infrastructure teams already use
The stack section gives technical buyers confidence without turning the homepage into a tool dump.
Cloud
AWS, GCP, Azure, multi-cloud architecture, migration, networking, managed services
Containers
Kubernetes, Docker, cluster operations, workload sizing, autoscaling, deployment flow
Infrastructure as Code
Terraform, OpenTofu, reusable modules, environment standards, reviewable infrastructure changes
CI/CD
GitHub Actions, GitLab CI, Jenkins, Argo CD, deployment safety, rollback discipline
Observability
Prometheus, Grafana, Datadog, OpenTelemetry, alerts, dashboards, incident readiness
AI Infrastructure
LLM deployment, RAG, Graph RAG, vector databases, graph databases, GPU workloads
Databases
PostgreSQL, Redis, managed cloud databases, performance review, availability planning
Cost Control
FinOps review, idle resource analysis, right-sizing, GPU utilization, cost visibility
Start with clarity
Ready to make your infrastructure more reliable, scalable, and cost-aware?
Start with a focused infrastructure review. Cloudico will help identify where your cloud setup is slowing delivery, increasing cost, or creating reliability risk.