Cloud Infrastructure Strategy

Architecture, migration, and cost optimization

Architecture design, migration planning, security posture, and cost-aware infrastructure decisions for modern cloud environments.

Multi-cloud and hybrid architecture design
Migration sequencing and cutover planning
Security boundaries and compliance posture
Cost modeling and resource optimization
IaC strategy (Terraform, Pulumi)

Distributed Systems Architecture

Resilience, availability, and scale

Availability patterns, resilience engineering, scalability tradeoffs, and performance optimization for systems operating under real-world constraints.

High availability and disaster recovery patterns
Consistency models and data synchronization
Fault tolerance and failure mode analysis
Performance tuning and capacity planning
Event-driven architectures and async patterns

Database Modernization

Upgrades, migrations, and operational excellence

MySQL upgrades, replication planning, migration runbooks, and operational readiness for critical data systems.

MySQL major version upgrades (5.7 → 8.x)
Cloud SQL migration and optimization
Replication topology and failover strategy
Schema evolution and backwards compatibility
Backup, recovery, and validation procedures

Kubernetes & Platform Engineering

Container orchestration and developer platforms

Platform design patterns, networking strategy, deployment safety, and operational best practices for Kubernetes and GKE.

GKE cluster architecture and multi-tenancy
Service mesh evaluation (Istio, Linkerd)
Progressive delivery and safe rollouts
Platform API design and developer experience
Security policy and workload identity

Reliability & Observability

SRE practices and production excellence

SLO strategy, monitoring architecture, alert quality, incident response planning, and systematic reliability improvement.

SLI/SLO definition and error budgets
Observability strategy (metrics, logs, traces)
Alert design and on-call sustainability
Incident management and post-mortem culture
Chaos engineering and resilience testing

AI-Native Infrastructure

Reliable systems for AI-adjacent workloads

Guardrails, boundaries, and reliability controls for systems interacting with AI services and LLM-powered features.

API gateway and rate limiting strategy
Cost controls and quota management
Fallback patterns and graceful degradation
Content filtering and safety boundaries
Observability for non-deterministic systems

Let's discuss your infrastructure needs

Whether you're planning a major migration, evaluating distributed systems strategy, or improving production reliability—we can help.