How to Forecast Cloud Costs Accurately

Tired of cloud budget shocks? Here's what works: knowing your cloud costs well can save money, cut waste, and fit your budget to your business needs. Gartner says that 60% of companies go over their cloud budgets, often from bad planning. Here are steps to gain control:

• Know major cost areas: Computing (30–70% of budget), storage (10–20%), networking (5–15%), and managed services.
• Check fixed vs changing costs: Fixed costs stay the same, while changing costs go up and down (like pay-as-you-go services).
• Look at past data: Check at least 12 months of costs to see trends and time-based changes.
• Use tools to predict costs: AWS Cost Explorer, Azure Cost Management, and Google Cloud Billing Reports can find trends and cut mistakes.
• Team up across groups: IT, finance, and operations need to work together to keep spending in line with business goals.

Quick Tip: Use machine learning tools to get better by up to 30% and make fewer mistakes. Start small, keep an eye on it, and change plans based on new data.

The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present. – Paul Saffo

Want to cut costs on cloud use? Read the article for clear plans and real steps to take.

Ep#147 Cloud Budgeting and Forecasting Done the Right way, The #FinOps Way with Jeff Blume

Key Parts of Cloud Cost Made Simple

It's key to know how your cloud money splits up to plan your budget better. Cloud bills are not just one big number; they break into different parts, each with its own way of adding up. By knowing these cost parts, you can spot trends and guess better what you'll need to spend later.

Main Cost Parts: Computing, Storing, Links, and Managed Help

Computing costs often use 30–70% of your total cloud budget, based on your tasks [4]. These costs cover things like virtual machines, on-demand functions, and container tech. For instance, AWS EC2 has costs based on type, size, and how long you use it. Also, AWS Lambda bills depend on how many requests it gets and how much computing time it uses, measured in GB-seconds [4].

Storage costs usually make up 10–20% of cloud costs, but this can go up a lot with big data sets or a lot of backup needs [4]. Cloud storage is split into three main kinds: object storage (like AWS S3), disk storage, and file services [3]. How much you pay changes based on who gives the service and what kind of storage it is:

Network costs often take up 5-15% of your full bill, with data moving fees as a big part of that [4]. These fees pay for things like load movers, VPNs, straight links, and, most of all, exit fees when data leaves the provider’s space.

Run services are more and more a big chunk of cloud cash use. For example, database help - one top Platform-as-a-Service choice - can really add to your spend [4].

Main cost parts here are space charges (often billed per gig per month), deal costs (bits of a cent per 10,000 deals), and backup costs [3]. It's key to know well how you use your data to keep from paying for space you don’t need [3].

If you cannot measure it, you cannot improve it. – Peter Drucker [2]

On an average day, businesses lose 35% of their money meant for cloud use, with users of AWS alone spending too much by £5.1 billion on cloud parts they do not need [2].

To start getting why, we need to break down these parts and look at how set and changing costs act.

Set vs Changing Costs

Money spent on the cloud is not like usual IT costs, making it key to tell set and changing costs apart for right plan making. It's worth noting that 73% of US and 81% of UK cloud users think their cloud spending is set, which may mess up their budget plans [2].

Set costs stay the same no matter how much you use. These are things like bought spots, own hosts, and yearly software passes. Bought spots, for one, can be cheaper than pay-as-you-go rates, but they make you stick to certain use levels for one to three years.

Changing costs go up or down based on how much you use them. These take in pay-as-you-go compute spots, data moving, storing stuff, and API calls. While changing costs let you move with ease, they need you to watch them close to dodge shock bills.

Mixed costs have a set fee and add more fees based on use. Many database services use this way of charging, with a set fee and extras for using more storage and power.

Knowing these types can help with better money plans. Set costs give a solid base to plan your budget, while costs that change need you to watch trends and keep track. Firms using both set and changing cost plans have cut their spending by up to 40%.

The main point is to fit your spending plan to what your company needs. For jobs that don't change much, fixed-cost choices like buying spaces ahead can save money. On the flip side, changing prices work better for jobs that go up and down or come in waves, as they let you adapt even though they might cost more per use. This setup helps you make good money plans by lining up spending types with how much you use.

How Old Costs Help Guess Future Spending

Past cloud costs can help you guess what you might pay later. By looking at old data, you can find patterns, see trends, and set clear budgets. The 2022 State Of FinOps survey said that forecasting costs is the second big problem for many companies [6]. A lot of them find it hard because they don't know how to get and study this data right. Luckily, most cloud services offer ways to look back at past use. Checking this data is key to match your future cost guesses with wider cloud spending plans we talked about before. This helps you start well when using special tools from your provider.

How to Get Data from Cloud Tools

Big cloud services have tools to see what you spent before and find spending patterns.

• AWS Cost Explorer: This lets you see your costs in detail for different services and times. You can sort costs by service, area, or specific things you use. It helps you see yearly trends.

• Azure Cost Management: This tool helps you check spending by subscriptions and groups of resources. It also lets you move data to Excel or Power BI for more digging.

• Google Cloud Billing Reports and Cost Table Reports: These tools let you move billing info to BigQuery for your own analysis [5]. They answer big questions like: How is my Google Cloud money going this month? Which project used the most money last month? What service cost the most?

Start by moving your data every month and sorting it by type of service. Look at key areas like computing and storing, as these often cost the most. Studying at least 12 months of data makes sure you see whole yearly cycles and can find long-standing trends.

Spotting Seasonal Trends and Odd Jumps

Cloud use often has clear patterns. Seasonal trends might show up, like more people online during holidays, big work at quarter-end, or less use in summer. Once you have your data, finding these trends and odd changes is vital to make your forecasts better.

Methods to find odd patterns, like SARIMA (Seasonal AutoRegressive Integrated Moving Average) or STL (Seasonal-Trend with Loess), are very good for showing seasonal changes and odd spots [8]. For example, a big jump in online store visits on Black Friday is normal, but a big jump in January might be weird [8].

Watch for these common patterns:

• Monthly cycles: Costs might jump at month-end from batch jobs or reports.
• Quarterly patterns: Stores might pay more for storage before Christmas as they get ready with new products and ads.
• Weekly cycles: Some groups might use less on weekends, while others might spend more on certain weekdays for regular fixes.

To spot these trends, chart your costs over different times - daily, weekly, and monthly - and look for patterns. Most tools from cloud providers give you different ways to view this info easily.

High jumps in your numbers need your eye. They might show issues, like a wrong set backup making data costs go up, or chances, like more compute use meaning more users. You can train AI, such as deep learning nets or tree types, on old data to spot trends that come back each year.

Write down why big jumps happen. It could be an ad push, a tech break, or a new item start. Knowing these things lets you get ready for the same stuff later. As your work grows, go back and fix your study for new patterns or times that mix.

Our goal isn’t to guess right every time - it’s to know enough to pick well. Paul Saffo put it well:

The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present [6].

Ways to Guess Cloud Costs

Guessing cloud costs is not the same for everyone - it needs special ways for each case. By using old data, you can make your budget plans better. Let's look at main ways to guess cloud spends for different use cases.

Models for Regular Use

Models based on time are good for guessing costs where the work does not change much. By looking at old spend data, these models can tell what you might use in the future, great for apps that run the same way every month.

In this group, a top way is exponential smoothing, which gives more weight to new data to adapt to changes fast. Here are three kinds:

• Simple Exponential Smoothing (SES): Best for work that does not follow a set pattern or season.
• Holt's Linear Exponential Smoothing: Works well for work that clearly goes up or down but stays the same over seasons.
• Holt-Winters' Exponential Smoothing: Good for times with clear patterns and seasons, like more sales at Christmas or higher use of school sites during term times.

Cloud companies have tools to make this easy. For example, Google Cloud’s BigQuery ML uses ARIMA_PLUS models for time-based guesses, while Amazon Forecast has a service that uses learning by machine to look at business numbers [9][10]. Start with easy models for steady work, and use more complex ones as needs grow.

Forecast Based on Drivers for New or Changing Projects

When old data is not enough - like with new starts or fast-moving projects - forecast based on drivers comes in. Instead of asking, “How much did we spend before?”, look at what will shape future costs. This way is key in places where spending can change a lot [11].

Look at several kinds of drivers:

• Internal Drivers: New products, new features, demo setups, or changes to systems.
• External Drivers: More users, sales days, deals, or big times like Cyber Monday or holidays.
• Strategic Drivers: Growing into new markets, company mergers, buys, or sales.
• Reverse Demand Drivers: Factors that cut costs like losing customers, better use of work, or stopping old systems.

For instance, think about a SaaS service that deals with 1.5 million API calls each month at £13,750 - about £0.0091 per call. If you predict 2 million more calls, use this cost per call to figure out extra costs [11]. Start by linking tech fixes to cloud services and guessing costs with company price tools. Write down all guesses so you can update them with new info. Mixing driver-based and trend ways often gives the best guesses.

Using Machines and Learning by Machine in Guessing

Adding machines and learning by machine to your guessing tools gives more exact numbers. These techs give updates right away, find odd cases, and change things based on how much you use [12].

Here’s why it's important:

• About 80% of groups go over their cloud money plans because they don't guess well and can't see all [12].
• AI can slash guess errors by around 30%, making money plans better [12].
• Firms using FinOps ways often see up to 40% more savings [12].
• One team cut their costs by 10% after checking their forecast for two hours [12].

Machine learning is good at finding trends that hand-done ways might miss, fine-tuning guesses as new data comes in. AI that spots when things don't match can show unexpected money jumps, letting teams move from fixed sheets to guessing models.

But, even though machines help, they can't take over for human thought. Like Paul Saffo said:

The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present [6].

Begin with little steps and grow bit by bit. By mixing lessons from the past, key bits about what drives a business, and smart ways to automate, you can create a tough setup to handle cloud costs well.

Easy Ways to Dodge Cloud Cost Forecast Errors

Even the top plans for guessing costs can go wrong if you miss big cost parts or if firms do not keep up with new changes. Seeing these common slips early can help you get better at guessing costs and sidestep sudden money issues that could smash your cloud plan. Let's look at some big goofs and how to fix them.

Extra Costs: Data Moving and API Use

Extra fees, like costs for moving data and using APIs, can grow fast if you do not watch them well. For example, DigitalOcean has a fixed price of about £0.008 per GB for moving data, but other firms often have more hard, tiered price setups that can shock you.

On top of those, charges for stuff not used, dead subscriptions, and copies can also make your bill jump up without warning. A big case is Capital One, which got a bill of about £800,000 after test servers kept running by mistake over a long weekend - an error that could have been dodged with good watching.

How can you skip these shocks? Begin by using steady tags for all your cloud gear. Tags help you know where costs come from by showing which project, team, or area they are for. Also, make rules for alerts and do regular checks to root out and cut off costs you do not need.

Dealing with Price Shifts from Cloud Firms

Price shifts from outside by cloud firms can also mess up even the best cost guesses. Firms change their prices often, and it can be tough to see these changes coming and even more tough to fit them into your plans. The real hard part is not just the price shifts but also the short warnings and the work in seeing how these shifts hit how you use stuff.

Stats show that 62% of places went over their cloud money plans last year, with 25% facing big overspends. Soumya Gangopadhyay, a tech thinker at EY, points out how key it is to know your strengths:

Organisations need to have a clear understanding or adherence to existing capabilities and performance - without it, engineering workload performance can be a challenge [14].

Big shocks can hit hard. More than half of all firms around the world have had to slow down their work because of high costs they did not expect from cloud storage. This has stopped projects and made work slow [14].

To fight ups and downs in prices, think about moving from yearly money plans to every three months checks. Getting along well with your cloud providers can also help, as you might learn about price changes early or even talk about better deals based on how much you use [15]. Using tools that check things in real time and set rules automatically can help you see and fix problems fast when prices change [14].

If you think of price changes as normal in cloud work, not a big shock, you can guess better what might happen next. As Andrew Smith, who leads strategy and market ideas at Wasabi Technologies, says:

Storage remains an unpredictable expense for many organisations... stalling business initiatives and slowing innovation [13].

Teamwork Boosts Cost Forecast Accuracy

Nailing cloud cost forecasts isn’t just about the digits - it’s about teamwork. Research shows a gap among teams: 51% of finance folks signal cloud overuse, but only 37% of IT folks agree [17]. More so, only a third of firms really know their operation costs [7].

Pulling Teams Together

To close these gaps, using FinOps rules and creating a Cloud Cost Hub can change the game. This method pushes IT, finance, operations, and more to work as one, making choices based on shared knowledge. For example, one company got better results by merging FinOps ways with united dashboards.

It's key for teams to share goals too. Instead of just finance watching costs and engineering aiming for features, both should eye cost effect with performance in mind. Having regular meetings every month to go over cost shifts and tune forecasts aids in keeping all aligned. This teamwork not only lifts forecasts - it might also make project finish times up to 50% faster [16].

Instant Tools for Smarter Choices

Teamwork shines with robust backing tools. Real-time cost watching tools give teams quick, useful info, leading talks based on facts, not guesses. These tools keep all in the loop about cloud spend and let tweaks happen as needed.

Look at CloudZero, for example. In 2025, Ninjacat cut their cloud costs by 40% using it [18]. Firms like MalwareBytes and Remitly save 6–10 hours weekly on cloud money handling [18]. Some top achievements include:

• Drift: Cut £3.2 million in AWS cloud costs [18].
• Upstart: Lowered costs by £16 million [7].
• Skyscanner: Found savings in a week that covered their whole year of CloudZero costs [7].

The finest tools do more than track spending - they send warnings for unexpected cost rises and let you tag costs by team or project. This keeps everyone clear on where funds are flowing.

Tim Ewald, CTO at Kevel, underlines the new mindset these tools inspire:

It's not about tradeoffs between cost and performance: cost is a need, just as important as scalability and security. CCM has empowered engineers in our organisation to understand this and act on cost data in the same way they would on performance, to maintain efficiency as we scale [19].

Also, tools that look at past data and use smart tech to spot odd trends can help groups plan their space better and use what they have wisely. With up to 32% of cloud money maybe thrown away [20], being ahead with live data and working as a team is key to keep cloud costs low.

Hokstad Consulting: Your Go-To for Cutting Cloud Costs

It's tough to know what you'll pay for cloud use. In 2023, the world will drop £480 billion on it, with services growing by 30.9% a year [22]. Hidden fees and wild price changes make it even harder. That's where Hokstad Consulting comes in. They line up what you think you'll use with what you do use.

They know a lot about cutting cloud costs and making DevOps better. Hokstad Consulting makes it easy to see what you'll spend. They use tech skills and business smarts to save you real money, not just in theory. They check everything closely.

Checking and Fixing Cloud Costs

It's easy to miss extra costs in cloud budgets. You might pay more for moving data or not using stuff you thought you would [21][22]. Hokstad Consulting digs deep to find costs you might not see, like extra storage you don't use much.

First, they look at past use and find spots where what you planned to spend doesn't match up with what you did spend. They check costs closely so they can guess better what you'll spend later [7]. They use sharp, automatic ways to do their checks.

Take a SaaS firm that saved £96,000 a year from finding wasted resources and extra data costs [23]. Or an online store that got 50% more done while spending 30% less [23].

Then, they make plans that use top tips for spending wisely on clouds. They set rules, keep an eye on budgets, and make sure no surprises mess up your spending [7]. Hokstad Consulting builds plans that fit you just right - mixing cost, how well it works, and safety for any cloud set up you have [23].

Tools that Guess Costs for You

As clouds get trickier, guessing costs by hand isn't enough. Hokstad Consulting builds tools that make forecasting better and easier. They use auto-setup, code built for the cloud, and monitors showing spending as it happens, so teams can work on big plans, not just fill out forms.

These tools do more than track costs - they also guess future costs by looking at what you do, how you grow, and what you plan [23]. For example, a tech firm cut setup time from six hours to 20 minutes and saw their spending better [23].

Hokstad Consulting fits these tools into what you have now, pulling data on its own, finding patterns, and sending up flags for big spending before it gets out of hand. They set up different ways of forecasting to lose fewer mistakes [7].

Their tools also keep resources tight, clean up what you don't use, move less used files to cheaper places, and keep data near to cut moving costs [21]. These fixes keep going, keeping guesses about costs right on track.

Hokstad Consulting trusts in their outcomes, and this shows in how they set their prices. Often, they cap their fees to a part of the real savings made. They also give out free checks to spot places that can be better [23]. With their aid, firms have seen up to 75% quicker setups, a 90% cut in mistakes, and a 95% drop in issues linked to their tools [23].

Main Points for Good Cloud Cost Guessing

Good cloud cost guessing does not just mean doing the math - it means making a plan that keeps your business safe with money. This shows why it's key to have good guesses.

To make your plan better, begin with the main cost parts. Split costs that don't change from ones that do, look back at past data, and set clear start points. These moves form the base for sharp guessing. Adding in full cost handling steps has also shown to cut cloud costs a lot.

Using past data with smart models makes better guesses. Stop using basic tools - AI tools can drop the risk of bad guesses by up to 30% [12]. As per the FinOps Foundation, keeping the gap between guessed and real costs under 20% is a solid aim when you mix human smarts and machine learning [7].

The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present. - Paul Saffo [1]

Working together across teams and keeping an eye on things as they happen are big wins for making plans work. Teams in finance, engineering, and operations should talk about future projects, seasonal shifts, and changes in the business. Watching things in real time helps by turning set plans into tools that can change costs as needed, letting us react fast to sudden price jumps.

When looking at numbers, keep an eye on how well the budget is sticking, how much cost coverage there is, and how quick we can act. Use auto tools when you can - doing things by hand just can't keep up with constant changes in cloud settings.

Lastly, see forecasting as a job that never ends, not just a once every three months thing. Top groups check their forecasts every month - or even more - to stay on top of things and dodge high costs later. By using these steps, your planning stays ready and in line with your big plan for managing cloud costs.

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