Monte Carlo Simulation for Business Professionals: AI Makes It Practical.

Choosing the right cloud storage provider can be difficult. IT procurement teams often feel overwhelmed by tiered pricing, data transfer fees, and different storage needs.

This blog will study how Monte Carlo simulation can streamline decision-making. By utilizing public pricing from AWS, Azure, and Google Cloud, we’ll demonstrate how simulation aids in forecasting costs, evaluating risks, and determining the best value for your company.

Implementing Monte Carlo Simulation in cloud cost analysis involves identifying needs, gathering provider pricing, applying the simulation, calculating costs, analyzing data, and comparing outcomes.

What is Monte Carlo Simulation?

Monte Carlo simulation is a mathematical method for predicting possible outcomes in uncertain situations. It runs a process thousands of times with various random inputs, generating a spectrum of potential consequences that can be examined to grasp probabilities, trends, and risks.

The Fundamental Principles of Monte Carlo Simulation

At its core, the Monte Carlo method relies on the principles of randomness and statistical sampling. By simulating a model that reflects the real-world situation, the method generates a multitude of potential outcomes.

Each run, or iteration, of the simulation uses different random values for the uncertain parameters, which helps to capture the inherent variability in the system. This randomness is typically drawn from known probability distributions, such as normal, uniform, or binomial distributions, depending on the nature of the variables involved.

Applications of Monte Carlo Simulation

Monte Carlo simulations are utilized across various fields, including procurement, finance, engineering, project management, and scientific research.

In finance, for instance, it is often used to assess the risk and return of investment portfolios, helping investors make informed decisions based on the potential variability of returns.

In project management, it helps evaluate the likelihood of completing projects on time and within budget by simulating scenarios that account for potential delays and cost overruns.

Benefits of Using Monte Carlo Simulation

One of the primary advantages of Monte Carlo simulation is its ability to incorporate uncertainty and variability into the analysis.

Unlike deterministic models, which yield a single outcome from fixed inputs, Monte Carlo simulation reveals the range of possible outcomes and their associated probabilities.

This comprehensive view allows decision-makers to better understand the risks involved and develop more robust strategies in the face of uncertainty.

Practical Application: Choosing a Cloud Provider

Our Needs

• Storage: Monthly usage fluctuates between 50 TB and 100 TB, with an average of 75 TB.

• Data Transfer: Varies from 5 TB to 20 TB per month.

Provider Options

We analyzed three top providers with the following pricing:

Provider	Storage Cost (per GB)	Data Transfer Cost (per GB)	Volume Discount
AWS	$0.023	$0.09	10% above 90 Tb
Azure	$0.0184	$0.087	5% above 80 Tb
Google Cloud	$0.020	$0.12	8% above 85 Tb

How Monte Carlo Simulation Works

Calculating Costs: For each simulation:

• Base storage cost = Storage Usage x Storage Cost.

• Data transfer cost = Data Transfer x Transfer Cost.

• Volume discounts were applied if storage exceeded the provider’s threshold.

Running Simulations: We asked ChatGPT to run 10,000 iterations to generate a realistic range of costs.

Cost Analysis by Provider

Provider	Median cost	95th percentile cost	Key observations
AWS	$9,263.83	$10,752.97	Moderate variability, competitive pricing.
Azure	$8,513.53	$9,825.29	Lowest cost with stable pricing.
Google Cloud	$9,890.24	$11,164.55	Higher costs due to transfer fees.

What is the 95th Percentile?

The 95th percentile represents the cost below which 95% of the simulations fall. For example:

• For AWS, 95% of the simulations show costs below $10,752.97.

• This critical metric reflects the upper limit of expected costs in most scenarios.

Monte Carlo Analysis Outcome

Azure offers the lowest median cost, making it an excellent choice for cost-conscious customers.

Why Monte Carlo Simulation is Applicable

Monte Carlo simulation is perfect in this case because it:

• Accounts for Uncertainty: Models fluctuating needs and complex pricing.

• Highlights Risks: Provides insights into cost variability and worst-case scenarios.

• Supports Decisions: Offers data-driven logic rather than relying on assumptions.

Other Considerations Beyond Costs

When selecting a provider, we also have to consider the following:

• Performance and Uptime: Does the provider meet our reliability requirements?

• Service Integration: Do we need additional AI or machine learning tools?

• Vendor Lock-In Risks: How easily can we switch providers if needed?

Ad Astra

Monte Carlo simulation helps procurement manage uncertainty. In our case, Azure was the most cost-effective choice for our needs.

Why not add yet another tool to our arsenal? It's not universal, but it will surely be helpful when the situation presents itself.