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Clients, Testimonials & Case Studies

Outcome-Focused Delivery

Each case study highlights the core problem, implementation approach, and measurable results.

B2B SaaS Platform

Problem: AWS costs grew 70% in 8 months while release velocity slowed.

Approach: Built hybrid architecture with private-cloud core services, AWS burst layer, Terraform, and automated CI/CD.

Results:

  • 42% lower monthly infrastructure spend
  • 2x faster release cycles
  • 99.95% uptime over 12 months

Mayan.Host gave us predictable infra economics and fewer incident escalations. - VP Engineering

Fintech Payments Provider

Problem: Required stricter isolation, faster audit readiness, and lower incident risk.

Approach: Designed private-cloud Kubernetes with encrypted replicas, WAF, audited access controls, and SRE runbooks.

Results:

  • Audit prep time cut by 55%
  • MTTR dropped by 48%
  • Improved latency consistency in peak hours

Their team behaves like an extension of our reliability org. - Head of Platform

Algorithmic Trading Platform

Problem: Needed compliant infrastructure for automated trading while keeping monitoring and logs inside its own environment.

Approach: Implemented Pulumi-based infrastructure, Nomad workload orchestration, and self-hosted observability on AWS for SEBI-aligned monitoring and log aggregation.

Results:

  • Infrastructure provisioned as code
  • Simplified container orchestration with Nomad
  • Self-hosted metrics and logs without external data egress

Mayan.Host took ownership of a time-sensitive compliance infrastructure build and delivered under tight timelines without moving operational data outside our environment. - CTO

Regional E-commerce Brand

Problem: Checkout instability during promotions and year-round overspend on peak capacity.

Approach: Implemented hybrid split: autoscaled AWS frontend, core systems on Mayan.Host Private Cloud, and unified observability.

Results:

  • Checkout failure rate down 63%
  • Page load time improved 31%
  • Off-peak infra spend down 37%

Mayan.Host moved our core systems to their private cloud and gave us the stability we needed during sales, without paying peak-cloud tax every month. - CTO

Industries & Use Cases

Different Industries Break Infrastructure in Different Ways

We design around real constraints: scale, compliance, data locality, latency, and reliability under failure.

SaaS Platforms

Challenges

  • Unpredictable traffic spikes
  • Rising cloud costs as usage grows
  • Downtime impacting revenue and churn
  • CI/CD pipelines that slow teams down

Typical Deployment Design

  • Users -> Load Balancer -> Kubernetes Cluster (Private Cloud or AWS)
  • Application services, background workers, API services
  • Managed database + object storage
  • Monitoring, logging, backups

Outcomes

  • 99.9%-99.99% uptime
  • Faster release cycles
  • 30-60% infrastructure cost reduction on private or hybrid cloud
  • Stable performance under growth

Fintech

Challenges

  • Regulatory compliance and audits
  • Data security and isolation
  • Low-latency transactions and API flows
  • Audit-ready logs for financial operations

Typical Deployment Design

  • Secure client access -> WAF + firewall
  • Private cloud for trading, payments, risk, and analytics
  • Broker/API integrations with controlled network access
  • Encrypted databases, audit logs, monitoring, and DR

Outcomes

  • Stronger data isolation
  • Compliance-ready infrastructure patterns
  • Predictable latency for critical workflows
  • Centralized logs and monitoring for audits

Healthcare

Challenges

  • Sensitive patient data and compliance
  • Legacy systems mixed with modern apps
  • High availability for critical services
  • Data residency requirements

Typical Deployment Design

  • Clinical apps and patient portals -> secure gateway
  • Private cloud with residency controls
  • Application layer, integration services, analytics
  • Encrypted databases, secure storage, backups and compliance logs

Outcomes

  • Compliance-aligned infrastructure
  • Secure sensitive-data handling
  • Reduced downtime for critical systems
  • Long-term cost control vs public cloud

E-commerce

Challenges

  • Traffic spikes during sales
  • Cart and checkout reliability
  • Performance affecting conversions
  • Overpaying for peak capacity year-round

Typical Deployment Design

  • Customers -> CDN + Load Balancer
  • Hybrid cloud architecture
  • Frontend + APIs with AWS autoscaling, core services on private cloud
  • Databases + caching + monitoring + DR

Outcomes

  • Stable performance during peak traffic
  • Faster page loads
  • Lower infrastructure spend outside peak seasons
  • Improved checkout reliability

IoT & Data-Intensive Workloads

Challenges

  • High-volume data ingestion
  • Real-time processing needs
  • Storage costs growing faster than compute
  • Complex pipelines across regions

Typical Deployment Design

  • Devices/sensors -> ingestion layer -> Kubernetes cluster
  • Stream processing, data aggregation, analytics services
  • Object storage + databases
  • Monitoring, scaling, and cost controls

Outcomes

  • Scalable ingestion without runaway costs
  • Reliable data pipelines
  • Lower storage and compute spend
  • Improved system observability

AI, ML & Data Platforms

Challenges

  • GPU and compute cost control
  • Large data pipelines and model artifacts
  • Reliable batch and inference workloads
  • Security for sensitive training data

Typical Deployment Design

  • Users/apps -> API gateway -> model services
  • Batch jobs and inference on Kubernetes or Nomad
  • Object storage for datasets, models, and artifacts
  • Monitoring for latency, cost, and job health

Outcomes

  • Predictable infrastructure cost
  • Reliable model serving and batch processing
  • Secure data and artifact handling
  • Better visibility into workload performance

Discuss Your Use Case

Bring your constraints and targets. We will map architecture choices to reliability and cost outcomes.

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