In today’s multi-cloud world, organizations are constantly seeking ways to optimize their cloud investments while ensuring performance and scalability. One of the most effective strategies to achieve this is Workload Planning.

What is Workload Planning?

Workload Planning is the process of modeling, analyzing, and optimizing workloads across cloud environments to ensure the best balance of performance, cost, and resource utilization.

Using Cloudability Workload Planning, teams can:

• Define workload requirements (compute, storage, databases, etc.)
• Compare service offerings across cloud providers (AWS, Azure, GCP, OCI)
• Receive cost estimates and resource recommendations
• Visualize and analyze the financial impact of deployment decisions

Some of the key benefits of implementing workload planning are:

1. Cost Optimization: Compare pricing models (On-Demand, Committed, Spot) and apply custom discounts or uplifts to reduce cloud spend.

2. Vendor Comparison: Evaluate multiple cloud providers side-by-side to choose the most cost-effective and performance-optimized solution.

3. Financial Forecasting: Accurate cost estimates improve budgeting and forecasting for migrations, expansions, and new deployments.

4. Governance and Control: Admins can enforce policies and preferences to ensure workloads align with organizational standards and compliance.

5. Scalability and Flexibility: Model workloads across different regions, lease types, and commitment terms to support dynamic deployment strategies.

Use Case: Multi-Cloud Cost Comparison for a New Application Deployment

Scenario: A FinOps team is planning to deploy a new customer-facing application. The application requires:

• 10 virtual machines
• 2 managed databases
• Object and block storage
• Load balancing

Steps Taken Using Workload Planning:

1. Model the Workload: The team logs into Cloudability, creates a new workload, and enters the resource requirements.
2. Select Cloud Providers: AWS, Azure, and GCP are selected for comparison, with preferred regions and lease types configured.
3. Apply Custom Pricing: Custom pricing is automatically applied for AWS, Azure, and OCI. For GCP, the team enables custom pricing export from their billing account.
4. Visualize Recommendations: The tool generates cost estimates for each provider, showing On-Demand, Committed, and Spot pricing.
   Note: OCI does not support committed pricing. Instead, it offers Capacity Reservation Pricing and Spot Pricing.
5. Analyze and Decide: Azure offers the lowest committed price with a 3-year term. AWS is considered for future scaling due to its broader service portfolio and internal performance benchmarks.
6. Make a Data-Driven Decision: The team selects Azure for cost efficiency and reserves AWS for potential future expansion.

Workload Planning empowers organizations to make smarter, data-driven decisions in an increasingly complex cloud landscape. By modeling workloads, applying custom pricing, and comparing cloud providers side-by-side, teams can achieve optimal cost efficiency and performance alignment.

IBM Doc - Workload Planning