Cloud Forecasting

From all the financial forecasting models out there, three are extremely relevant to forecasting cloud (AWS, Azure, GCP, etc.) spend. This article goes into detail of those models, how they correlate to cloud cost forecasting, examples, and how Vega Cloud can help.

1. Time-Varying Parameter Models (TVP)

• Overview: TVP models are designed to handle situations where relationships between variables change over time. Unlike static models, TVP allows the parameters that define the relationships between variables (like cost and usage) to evolve. This makes them particularly useful for environments where conditions are constantly changing.
• Correlation to Cloud Cost Forecasting: Cloud costs can fluctuate based on factors like sudden increases in usage, changes in vendor pricing, or the introduction of new services. TVP models can adjust to these changes by allowing the impact of these factors on costs to change over time, rather than assuming a fixed relationship.
• Example 1: If your company introduces a new cloud-based application, TVP can help forecast how this might change your overall cloud costs over time, considering both initial spikes in usage and potential stabilization later.
• Example 2: If a cloud provider changes their pricing structure, a TVP model could adjust your cost forecast to account for this change, predicting how it might impact your budget next quarter versus a year from now.
• Vega Cloud Example: Vega Cloud can help simplify the use of TVP models by providing real-time monitoring and analytics of your cloud usage, allowing you to feed accurate, up-to-date data into your TVP model. This ensures that your forecasts reflect the most current usage trends and pricing changes.

2. Bayesian Econometric Models

• Overview: Bayesian models start with a prior belief or estimate about what the future might look like, based on historical data. As new data becomes available, the model updates these beliefs to improve accuracy. This approach is especially useful when dealing with uncertainty or when new information is continually emerging.
• Correlation to Cloud Cost Forecasting: Cloud costs are often uncertain and can change rapidly. A Bayesian model allows you to start with an estimate based on historical cloud spending and then refine this estimate as more data comes in, like new usage patterns or changes in cloud service offerings.
• Example 1: If your company has historically seen cloud costs increase by 10% year-over-year, a Bayesian model would use this as the starting point. But if halfway through the year you notice a steeper rise due to unexpected usage, the model would adjust your forecast accordingly.
• Example 2: When a cloud provider offers a new discount or pricing model, a Bayesian model can quickly incorporate this new information, giving you an updated forecast that reflects these changes.
• Vega Cloud Example: Vega Cloud can automate the data collection and analysis needed for Bayesian models, continuously feeding new data into the model and helping you adjust your forecasts without needing to manually update your assumptions. This ensures that your budgeting stays accurate even in a rapidly changing environment.

3. Agent-Based Models (ABM)

• Overview: ABM simulates the interactions of individual agents (e.g., users, departments, or applications) to understand how these interactions lead to complex, emergent behaviors at the macro level. Each agent follows simple rules, but together they can produce surprisingly complex outcomes.
• Correlation to Cloud Cost Forecasting: In a cloud environment, different teams or applications may use resources differently. ABM can simulate how changes in one part of the organization (like a new project requiring heavy cloud usage) might impact overall costs, including how these effects might compound over time.
• Example 1: If one department starts using a new cloud-intensive tool, ABM could simulate how this change might lead to higher overall cloud costs, especially if other departments follow suit or if the increased load requires scaling up infrastructure.
• Example 2: If your organization introduces a policy that encourages cost-saving measures, ABM could help forecast how these measures might reduce overall cloud spending by simulating how each team would likely respond.
• Vega Cloud Example: Vega Cloud can provide the detailed, agent-level data needed to build accurate ABMs. By tracking usage at a granular level, Vega Cloud helps you understand how individual actions contribute to overall cloud costs, allowing you to run simulations that forecast how changes in behavior could impact future spending.

Summary with Vega Cloud

Vega Cloud acts as a facilitator for these advanced forecasting models by offering comprehensive data collection, real-time monitoring, and detailed analytics. This makes it easier to feed accurate, up-to-date information into models like TVP, Bayesian, and ABM, ensuring that your forecasts are as precise and actionable as possible, even in a complex, dynamic cloud environment.