Scale • Cloud & Performance Engineering
Cloud & Performance
Engineering
Optimize cloud infrastructure for performance, reliability and cost efficiency - ensuring applications run fast, stay available and scale economically.
25+
Engineers
40+
Clients
58
Case Studies
Cloud
Infrastructure • DevOps • Performance
P95 Latency
<50ms
Throughput
3x Faster
Cost Reduced
40%
Infrastructure
Auto-Scale
Cloud Engineering Services
From cost optimization and performance tuning to auto-scaling architecture and full observability - we engineer cloud infrastructure for efficiency at scale.
Cloud Architecture Optimization
Review and redesign cloud architecture for performance, cost and operational efficiency
Auto-Scaling Design
Implement horizontal and vertical auto-scaling based on CPU, memory, request rate and custom metrics
Cost Optimization & FinOps
Audit cloud spend, implement right-sizing, reserved instances, spot instances and cost governance
Performance & Load Testing
Design and execute load testing strategies that establish baselines and identify breaking points
CDN Optimization
Implement and optimize content delivery for static assets, API responses and media files
Database Performance Tuning
Optimize through query analysis, indexing, connection pooling, read replicas and caching layers
Caching Strategy Design
Multi-layer caching - CDN, API response caching, application-level caching and cache invalidation strategies
Monitoring & Observability
Comprehensive monitoring covering infrastructure metrics, application performance and cost tracking
CI/CD Pipeline Optimization
Optimize deployment pipelines for speed, safety and reliability with automated testing and canary deploys
Security Hardening
IAM policies, network security, encryption, secrets management, vulnerability scanning and compliance
Engineering Capabilities
We combine performance analysis, cost optimization, infrastructure architecture, database tuning and SRE practices into a unified cloud engineering capability.
Performance Analysis
- Application profiling
- Bottleneck identification
- Latency analysis
- Resource utilization audit
- Capacity planning
Cost Optimization
- Resource right-sizing
- Reserved instance planning
- Spot instance strategies
- Auto-scaling optimization
- Cost allocation and governance
Infrastructure Architecture
- Multi-AZ and multi-region design
- Container orchestration (ECS/K8s)
- Infrastructure as Code (Terraform)
- Environment management
- Disaster recovery planning
Database Optimization
- Query performance tuning
- Index optimization
- Read replica and caching strategies
- Connection pool management
- Database scaling patterns
Observability & SRE
- Monitoring stack implementation
- SLO/SLA definition and tracking
- Alerting and escalation policies
- Incident response procedures
- Capacity planning and forecasting
Cloud Optimization Approach
A structured approach to cloud optimization - from performance audit through infrastructure tuning, load testing and continuous improvement.
01
Performance & Cost Audit
Analyze current infrastructure, identify waste, measure performance baselines
02
Optimization Strategy
Prioritize improvements by impact - quick wins and architectural changes
03
Infrastructure Optimization
Implement right-sizing, auto-scaling, caching and cost governance
04
Performance Tuning
Application-level optimization - query tuning, caching, CDN and connection management
05
Load Testing
Comprehensive testing to validate improvements and identify remaining bottlenecks
06
Monitoring & Alerting
Implement observability stack with dashboards, alerts and SLO tracking
07
Ongoing Optimization
Continuous cost reviews, capacity planning and performance improvement
Technology Stack
The exact stack is selected based on infrastructure requirements - not a fixed technology mandate.
Cloud Platform
☁️AWS EC2 / ECS
⚡AWS Lambda
🗄️AWS RDS / ElastiCache
🌐AWS CloudFront / S3
Infrastructure
🏗️Terraform
🐳Docker / Kubernetes
📦AWS CDK
🔧Pulumi
Performance Testing
📊k6
🎯Artillery
🦗Locust
⚙️AWS Load Testing
Monitoring
🐕Datadog
📈Prometheus / Grafana
👁️CloudWatch
🔔PagerDuty
Cost Management
💰AWS Cost Explorer
🏥CloudHealth
📉Custom FinOps Dashboards
Related Solutions
Explore related capabilities that complement cloud performance engineering.
Reliability & Performance Engineering
SRE practices, observability, incident response and SLO management for production systems.
ExploreBackend & Distributed Systems
Scale backend architecture with event-driven systems, microservices and distributed processing.
ExploreCloud Application Modernization
Migrate on-premise systems to cloud infrastructure before optimizing performance.
ExploreCloud Performance Use Cases
We optimize cloud infrastructure across industries and use cases - from cost reduction and auto-scaling to multi-region deployment and full observability.
Cloud Cost Reduction
Reduce monthly cloud spend through systematic optimization
Application Performance
Improve response times and throughput for production applications
Auto-Scaling Design
Implement demand-based scaling that balances performance and cost
Database Optimization
Resolve database bottlenecks through tuning, caching and architecture
CDN & Caching Strategy
Optimize content delivery and caching for global performance
Load Testing Program
Establish continuous load testing to prevent performance regression
FinOps Implementation
Build cost governance practices, budgets and optimization reviews
Infrastructure Right-Sizing
Match compute resources to actual workload requirements
Multi-Region Deployment
Distribute applications for global latency and availability
Observability Implementation
Full monitoring, alerting and SLO tracking
Engineered for Efficiency
We engineer cloud infrastructure for production - optimized for cost, performance, scalability and reliability.
Cost Reduction
Systematic optimization to reduce cloud spend while maintaining performance
Performance
Latency optimization, caching, CDN and efficient resource utilization
Scalability
Auto-scaling architecture that grows and shrinks with demand
Reliability
Multi-AZ deployment, health checks, failover and disaster recovery
Observability
Complete monitoring with metrics, logs, traces and custom dashboards
Governance
Cost allocation, budget alerts, optimization reviews and compliance
Production Reference
Optimized cloud infrastructure for production applications, reducing monthly costs while improving response times and establishing auto-scaling for demand fluctuations.
Engineering Team Model
The team is structured around your infrastructure needs, not a fixed package. Team composition adapts based on optimization requirements.
2-Person Squad
Focused optimization initiative. Performance audit, cost reduction or specific infrastructure improvement.
3-Person Squad
Comprehensive cloud optimization. Infrastructure modernization, auto-scaling and observability implementation.
5-Person Squad
Full cloud engineering with Technical Lead, Cloud Engineer, SRE, Backend Engineer and DevOps.
Custom Team
Team composition adapts to infrastructure requirements. Can include database, security or FinOps specialists.
How We Work Together
A flexible engagement model that grows with your infrastructure - from initial audit to long-term cloud engineering partnership.
1
Performance & Cost Audit
Analyze infrastructure, identify optimization opportunities, measure baselines
2
Optimization Plan
Prioritize improvements, design architecture changes, estimate impact
3
Implementation
Execute optimizations with a dedicated cloud engineering team
4
Cloud Engineering Team
Ongoing team embedded in your infrastructure for continuous optimization
5
Long-Term Partnership
Strategic infrastructure partner for scaling, reliability and cost management
Fixed-scope engagements are available when requirements are sufficiently defined. The right investment depends on infrastructure scope, technical complexity, team composition and roadmap.
Relevant Case Studies
Video Encoding
Serverless Video Processing Pipeline with AWS MediaConvert
The video platform needed a scalable, cost-effective way to handle variable encoding workloads, from quiet periods with few uploads to peak times with hundreds of simultaneous jobs.
AWS LambdaAWS MediaConvertAWS S3+6
Read Case Study
Vector Databases
Milvus Autoscaling on Kubernetes with EC2 and S3-Backed Persistent Storage
An AI platform with rapidly growing vector data (embeddings for search, recommendations, and RAG) needed their Milvus vector database to scale automatically based on query load and data volume — with durable, cost-effective storage that wouldn't be lost if pods restarted or nodes were replaced.
MilvusAmazon EKSKubernetes HPA+8
Read Case Study
GPU Infrastructure
On-Off Scaling Pattern for AI & Video Processing Workloads
An AI-powered video processing platform needed to handle highly variable workloads — from zero jobs during off-hours to hundreds of concurrent video processing and AI inference tasks during peak times — without paying for idle GPU and compute resources.
Node.jsMongoDBRunPod API+7
Read Case Study
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Tell us about your cloud infrastructure, performance challenges and cost concerns. We will audit your setup and recommend optimizations.