Principles of Building a Scalable Multi-Cloud Recommendation Engine

Overview of the Problem

‍Organizations use multiple cloud providers like AWS, Azure, and Google Cloud to avoid vendor lock-in, which offers increased flexibility, access to top-tier tools, improved performance, and lowered downtime risks. However, this diversity introduces complexity—each cloud has distinct services, APIs, pricing models, and optimization strategies. Managing resources efficiently across multiple clouds requires deep expertise in each provider’s ecosystem, making it challenging to optimize usage and costs at scale.

A well-designed recommendation engine can help address this challenge. Such an engine must abstract cloud-specific nuances, seamlessly handle data across clouds, and suggest understandable and safe optimizations amenable to automatic orchestration.

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Challenges in Building a Multi-Cloud Recommendation Engine

Developing a recommendation engine for multi-cloud optimization presents several key challenges:

·       Heterogeneous Data Sources – Each cloud provider exposes resource utilization and pricing data through different APIs and formats.

·       Varied Service Definitions – The same type of service (e.g., virtual machines) has different configurations, performance characteristics, and pricing models across clouds.

·       Dynamic Pricing and Billing Models – Costs vary by provider, region, commitment level, and usage patterns, requiring normalization before analysis.

·       Scalability– The recommendation system must handle large-scale data ingestion, storage, and processing to support enterprises operating hundreds of thousands of resources spread across multiple cloud platforms.

Due to these challenges, implementing a recommendation engine that works across multiple clouds and enables decision-making without cloud-specific knowledge can be daunting.

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A Unifying Approach: Data Abstraction and Pluggable Models

To address these challenges, PrimeOrbit’s recommendation engine is built on a unified data model that abstracts away provider-specific details, ensuring seamless cross-cloud optimization.

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Key Design Principles:

·       Data Abstraction Layer – A foundational layer normalizes disparate cloud data sources into a common schema based on common metrics for compute, storage, network, and pricing data across different providers.

·       Unified Data Model – To enable consistent comparison of resources across providers (such as AWS, Azure, and GCP), the engine organizes resource metadata, utilization, and cost data into a unified format. This prevents any silo analysis that lacks a unified picture.

·       Pluggable Recommendation Engine – A modular architecture allows optimization strategies (e.g., rightsizing, reservations, workload shifting) to be applied universally, regardless of the cloud provider.

The engine's architecture is designed to require minimal engineering effort when adding support for new services, such as Kubernetes-based workloads or additional cloud providers, ensuring both scalability and futureproofing of the system.

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The Solution in Action: Rightsizing and Reservation Optimization

To illustrate how this architecture enables effective recommendations, consider two common cloud optimization strategies:

1.     Rightsizing Instances

Rightsizing, by adjusting the cloud resources to match actual usage, can help avoid over-provisioning and unnecessary costs. However, each provider gives the utilization and pricing data in different formats. The recommendation engine:

·       Collects utilization data (CPU, memory, disk I/O) from all the cloud providers.

·       Normalizes performance metrics into a common schema.

·       Compares actual usage against optimal resource types in the unified data model.

·       Suggests a more cost-effective instance size, regardless of the cloud provider.

Because the system abstracts cloud-specific instance families and pricing models, businesses receive consistent rightsizing recommendations that can be implemented without deep cloud-provider knowledge.

2.     Reserved Instance and Savings Plan Optimization

Purchasing reserved capacity (RIs in AWS, Reserved VMs in Azure, Committed Use Contracts in GCP) can significantly reduce cloud costs. However, each provider offers different pricing terms and discounts. The recommendation engine:

·     Aggregates past usage data to forecast demand.

·     Identifies opportunities for reservations based on historical workloads.

·     Suggests an optimal commitment strategy across clouds while considering flexibility needs.

By applying pluggable analytics models on top of the unified data model, the system ensures that recommendations are precise, scalable, and cloud-agnostic.

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Benefits of This Approach

With this architecture in place, scaling the recommendation engine becomes significantly easier:

·       Adding New Cloud Providers is Faster – With GCP support being integrated now and Kubernetes planned for the future, the unified data model ensures that new clouds can be onboarded in one-tenth of the time compared to traditional approaches.

·       Cross-Cloud Optimization Without Complexity – Enterprises can optimize their multi-cloud environments without needing deep expertise in AWS, Azure, or GCP.

·       Futureproofing for Expanding Use Cases – The same architecture can extend beyond compute resources to storage, databases, and serverless workloads.

By abstracting cloud-specific details into a unified, scalable recommendation system, PrimeOrbit eliminates complexity while delivering precise, automated cloud cost optimization.

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Ready to see how PrimeOrbit’s multi-cloud recommendation engine can simplify your cloud optimization? Reach out to us for a live demo and see how our scalable, cloud-agnostic recommendations can work for your unique environment.

Whether you’re managing AWS, Azure, or GCP, our platform provides real-time, actionable insights to drive cost savings and resource optimization across your entire cloud portfolio. Start your free trial today and experience the future of cloud optimization! Let us show you how easy it is to eliminate complexity and make smarter, data-driven decisions at scale.

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