Infrastructure your auditors and engineers both sign off

The best provider depends on your workload characteristics, compliance requirements, and existing infrastructure. AWS has the broadest service catalog and is usually the default for complex architectures with many moving parts. Azure integrates well with Microsoft tools like Active Directory, Office 365, and .NET, so it is a natural fit if your organization already relies on the Microsoft ecosystem. GCP leads in data engineering and machine learning with BigQuery, Vertex AI, and managed Kubernetes (GKE). For most new projects, we recommend starting with a single provider to keep operational complexity low. Multi-cloud strategies make sense when you have specific compliance requirements (like data residency in regions only one provider covers) or when you want to avoid vendor lock-in for critical workloads. We analyze your specific situation, including traffic patterns, team skills, and budget constraints, and recommend the approach that balances cost, performance, and operational simplicity.

Cloud migrations are scoped per project and start from our published project band, depending on the number of services, data volume, and the complexity of your existing infrastructure. A straightforward lift-and-shift of a few web services to managed cloud instances sits at the lower end. A full re-architecture involving containerization, database migration, CI/CD pipeline setup, and monitoring instrumentation across dozens of services falls at the higher end. Before committing to any budget, we run a detailed assessment that maps your current infrastructure, identifies dependencies, and estimates effort for each migration step. This assessment usually takes 1-2 weeks and produces a phased migration plan with clear cost estimates per phase. Monthly infrastructure costs after migration vary widely, but a cost-optimized architecture can reduce hosting spend compared to on-premise or legacy hosting, particularly when we implement auto-scaling and right-sizing from the start.

Yes, we offer managed cloud services that function as your dedicated DevOps team. This includes infrastructure monitoring with automated alerting, incident response with SLAs scoped per engagement, regular security patching, and quarterly optimization reviews. Our monitoring covers CPU, memory, disk, network, application errors, and custom business metrics through Grafana dashboards that your team can access anytime. When an alert triggers, our on-call engineer investigates and resolves the issue, then sends you a post-incident summary explaining what happened and what we did to prevent recurrence. We also run monthly cost optimization reviews where we analyze your cloud spending, identify underutilized resources, and recommend adjustments like reserved instances or right-sizing. Reserved instance planning and removing unused resources can lower your monthly cloud bill. You can scale this support up or down based on your needs.

Security is built into every layer of our cloud architecture, not added as an afterthought. We implement least-privilege IAM policies where every service and user gets only the permissions they actually need, reducing the blast radius if credentials are compromised. All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Network segmentation isolates services in private subnets, with public access only through load balancers and WAFs. In the CI/CD pipeline, every build runs through dependency scanning (Snyk) and container image scanning (Trivy) before anything reaches production. We run security reviews as part of delivery and maintain tamper-evident audit logs. For companies pursuing compliance certifications, we can implement the controls and documentation those certifications require. For instance, we set up centralized logging, access reviews, and change management processes that satisfy auditor requirements.

We start with an audit of your existing infrastructure to understand what is working well and where the gaps are. There is no need to tear everything down and start over. Most organizations have a mix of well-configured services and areas that need attention, such as missing monitoring, inconsistent deployment processes, or over-provisioned resources. We create a prioritized improvement plan and implement changes incrementally, starting with the highest-impact items. For instance, if your servers run fine but deployments are manual and error-prone, we set up CI/CD pipelines first while leaving the rest untouched. If your infrastructure runs on bare EC2 instances without auto-scaling, we can containerize services gradually, one at a time, without disrupting production. Each change is tested in a staging environment before applying it to production. This incremental approach reduces risk and lets you see measurable improvements within weeks rather than waiting months for a complete overhaul.

Yes, we build serverless applications using AWS Lambda, Azure Functions, and Google Cloud Run. Serverless is particularly well suited for workloads with variable traffic, because you pay only for actual execution time rather than keeping servers running 24/7. This makes it cost-effective for APIs with unpredictable request volumes, scheduled data processing jobs, and webhook handlers that fire occasionally. For instance, a document processing pipeline that runs once an hour costs pennies with Lambda compared to a dedicated server running continuously. We also use Cloud Run for containerized services that need the flexibility of Docker without the operational overhead of managing Kubernetes clusters. The trade-off with serverless is cold start latency and execution time limits, so it is not ideal for long-running processes or applications requiring persistent WebSocket connections. We help you identify which parts of your system benefit from serverless and which are better served by containers or traditional compute.