Platform engineering in a multi-cloud environment requires strategic orchestration of Azure, AWS, and GCP to build resilient, scalable infrastructure that supports modern data storage and AI workloads. By adopting a unified approach to cloud-native architectures, organisations can leverage the unique strengths of each provider—Azure’s enterprise integrations, AWS’s breadth of services, and GCP’s AI/ML capabilities—while avoiding vendor lock-in and maximising operational flexibility.
Why Multi-Cloud Platform Engineering Matters Now
The modern enterprise no longer relies on a single cloud provider. According to recent industry trends, over 85% of organisations now operate in multi-cloud environments, driven by the need for resilience, cost optimisation, and access to best-of-breed services. Platform engineering teams are tasked with building internal developer platforms (IDPs) that abstract complexity while delivering consistent experiences across Azure, AWS, and GCP.
At 200OK Solutions, we’ve seen firsthand how organisations struggle with fragmented cloud strategies. Legacy systems sit alongside modern cloud-native applications, creating integration challenges that slow innovation. Our approach centres on building unified platform layers that enable teams to deploy, scale, and manage workloads seamlessly—regardless of the underlying cloud provider.
Key Challenges in Multi-Cloud Platform Engineering
1. Data Storage Complexity Across Providers
Each cloud platform offers distinct data storage solutions with different performance characteristics, pricing models, and integration patterns:
- Azure: Azure Blob Storage, Azure Data Lake Storage Gen2, Cosmos DB for NoSQL
- AWS: S3, EFS, DynamoDB, Aurora for relational workloads
- GCP: Cloud Storage, BigQuery, Firestore, AlloyDB
The challenge: How to implement consistent data governance, backup strategies, and disaster recovery across these heterogeneous environments while optimising for cost and performance.
Our solution approach: We implement data fabric architectures using tools like Apache Iceberg or Delta Lake that provide unified table formats across clouds. This enables organisations to query and analyse data consistently, implement cross-cloud replication strategies, and maintain compliance without rebuilding infrastructure for each provider.
2. AI Innovation Requires Specialised Cloud Services
Modern AI and machine learning workloads demand access to specialised hardware (GPUs, TPUs) and managed services that vary significantly across providers:
- Azure: Azure Machine Learning, Cognitive Services, Azure OpenAI Service
- AWS: SageMaker, Bedrock, Rekognition, Comprehend
- GCP: Vertex AI, AutoML, TPU access, Gemini integration
The strategic question: How do you build AI pipelines that can leverage GCP’s TPUs for training, AWS SageMaker for inference, and Azure OpenAI for generative AI applications—all within a unified MLOps framework?
Platform engineering answer: We design abstraction layers using Kubeflow, MLflow, or custom orchestration that allows data scientists to focus on model development while the platform handles deployment, scaling, and monitoring across clouds. This approach accelerates AI innovation cycles from weeks to days.
Best Practices for Multi-Cloud Platform Engineering
Establish a Golden Path for Developers
Create standardised workflows that reduce cognitive load:
- Infrastructure as Code (IaC): Use Terraform or Pulumi with modular configurations that work across Azure, AWS, and GCP
- Container orchestration: Deploy Kubernetes clusters with consistent networking, security policies, and observability stacks
- Service mesh: Implement Istio or Linkerd for traffic management and security across multi-cloud services
- GitOps workflows: Adopt Argo CD or Flux for declarative, version-controlled deployments
Implement Cloud-Agnostic Storage Strategies
Build data platforms that transcend individual cloud boundaries:
- Object storage layer: Create unified S3-compatible APIs using MinIO or cloud-native services with cross-region replication
- Data lakehouse architecture: Implement Apache Iceberg or Delta Lake for ACID transactions across distributed storage
- Caching strategies: Deploy Redis or Memcached clusters that serve multiple cloud regions for low-latency access
- Backup and disaster recovery: Automate cross-cloud backup pipelines with tools like Velero for Kubernetes workloads
Design for Observability and Cost Management
Multi-cloud environments create blind spots without proper instrumentation:
- Unified monitoring: Deploy Prometheus, Grafana, or Datadog across all cloud providers for consistent metrics
- Distributed tracing: Implement OpenTelemetry to track requests across multi-cloud microservices
- Cost visibility: Use tools like Kubecost or CloudHealth to track spending per workload, team, and cloud provider
- FinOps practices: Establish tagging conventions, rightsizing recommendations, and automated resource cleanup
How to Migrate Legacy Data Warehouses to Multi-Cloud Environments
Legacy data warehouse migration represents one of the most complex multi-cloud challenges. Here’s our proven approach:
- Assessment and discovery: Inventory existing data sources, ETL pipelines, reporting dependencies, and user access patterns
- Design target architecture: Choose between Azure Synapse Analytics, AWS Redshift, Google BigQuery, or Snowflake based on workload characteristics
- Implement data fabric: Create abstraction layers using Trino, Dremio, or Starburst for federated queries across old and new systems
- Incremental migration: Use change data capture (CDC) tools like Debezium to replicate data in real-time while validating accuracy
- Cutover and optimisation: Switch production traffic gradually, monitor performance, and optimise queries for cloud-native storage formats
This methodology minimises downtime and allows teams to validate each migration phase before committing to the new platform.
AI-Driven Automation in Multi-Cloud Platform Engineering
The future of platform engineering lies in intelligent automation that learns from operational patterns:
- Auto-scaling with ML: Deploy predictive models that forecast demand and pre-scale infrastructure before traffic spikes
- Anomaly detection: Use AI to identify unusual patterns in logs, metrics, and traces that indicate security threats or performance degradation
- Self-healing systems: Implement automated remediation workflows triggered by AI-detected incidents
- Intelligent resource placement: Optimise workload placement across clouds based on cost, latency, and compliance requirements
At 200OK Solutions, we’ve implemented AI-driven platform capabilities for clients across fintech, healthcare, and retail sectors, reducing operational overhead by up to 40% while improving system reliability.
The 200OK Solutions Advantage
As a trusted digital transformation partner operating globally from our UK headquarters, we bring deep platform engineering expertise across Azure, AWS, and GCP. Our team has guided organisations through complex migrations, built cloud-native architectures that scale to millions of users, and implemented AI systems that drive real business value.
We don’t just deploy infrastructure—we become long-term partners in your digital evolution. Whether you’re a VC-backed startup building your first cloud platform or a global enterprise modernising legacy systems, our mission remains constant: help you innovate faster, operate smarter, and deliver meaningful value to your customers.
Our work in platform engineering spans hospitality platforms serving millions of travellers, fintech systems processing billions in transactions, healthcare data lakes supporting clinical research, and public-sector services used by entire populations. Each engagement reinforces our belief that resilient, scalable technology foundations are essential to modern business success.
Frequently Asked Questions
Q: Should we choose one cloud provider or adopt multi-cloud from the start?
A: Start with the cloud provider that best matches your immediate needs and team expertise. Design your architecture with abstraction layers (containers, IaC, cloud-agnostic services) that enable multi-cloud expansion when business requirements demand it—such as regulatory compliance, disaster recovery, or access to specialised services.
Q: How do we manage security and compliance across multiple cloud providers?
A: Implement policy-as-code using tools like Open Policy Agent (OPA) or Cloud Custodian. Establish centralised identity management with federation, deploy consistent security controls through infrastructure templates, and use cloud security posture management (CSPM) tools to continuously audit configurations across all providers.
Q: What’s the most cost-effective way to implement multi-cloud data storage?
A: Use tiered storage strategies that match data access patterns to appropriate storage classes. Archive infrequently accessed data in low-cost tiers (Azure Cool/Archive, AWS Glacier, GCP Coldline), implement lifecycle policies to automate transitions, and use cross-cloud replication only for business-critical data requiring high availability.
Ready to build your multi-cloud platform engineering strategy?
Contact 200OK Solutions to discuss how we can help accelerate your digital transformation journey with resilient, scalable cloud-native architectures designed for long-term success.
