Searching for advice on cloud costs quickly leads you to the same marketing jargon from agencies.
The same types of pages occupy the top of search engine results and say the same thing: it will cost you between $50,000 and $150,000 to build an MVP.
This is not a cost for infrastructure; it is a pitch for outsourced developers to take over your company.
As an early-stage founder, CTO, or technical product manager, you are not looking for a wide-ranging estimate of what your company's capital expenditures will be.
You want to know specifically what your AWS bill will look like if, for example, you go from 50 users in beta testing and launch to 1,000 users with active accounts.
You need to understand what the implementation of IPv4 charges in 2024 will do to your base costs.
You also need to figure out why your staging environment continues to generate bills without any paying customers signing up.
These users are interested in actual cost projections and a methodology for generating them repeatedly.
They want to know the cost of an identified workload, not a calculator tool on the internet.
Cloud spending over a four-to-five-year period from now will look very different than it does today.
After this analysis, you will have access to specific benchmarks, the different architectural stages of a company's cloud infrastructure, and how to model your cloud infrastructure cost using modern FinOps practices.
Infrastructure cost projections for 2026 SaaS
If you are looking for the numbers right now, the baseline numbers for the way that you will operate your business in 2026 is:
Staging environments should be set up so that they have an approximate monthly cost of $80, instead of $300, when set up with proper scheduling.
Early monthly operating expenses (OpEx) for live MVPs typically range between $400 and $2,500, depending on the type of workload involved.
AWS Lambda/DynamoDB provides the most cost effective way for initial validation.
Overprovisioning compute resources continues to be the largest financial error being made by teams early in their development process, and accounts for a large percentage of cloud waste (27% is the average).
Do not sign up for Reserved Instances or Savings Plans until you have operated your service for at least three to six months.
Signing up for these services too early will likely restrict the startup into paying for an architecture that does not match their needs.
Multi-tenant architectures require specific metrics to track costs associated with individual users.
Without this data you will have no ability to make sound decisions as you scale your revenues.
Standard advice regarding cloud costs for SaaS is broken.
There are countless articles from 2022, 2023 and even 2014 that rank at the top of Google search rankings — and provide no value in understanding how modern pricing models are structured.
Most of these articles lump together capital expenditures (CapEx) like software licenses and engineering payroll along with operational expenditures (OpEx) like AWS infrastructure.
This creates confusion for those trying to build an accurate financial model for their software product.
The $100,000 estimate given by the typical agency blog for MVPs hides the actual monthly server costs, which are typically only $150.
Generic estimates such as these are unreliable because they fail to take into account the different ways a workload can utilize resources.
For example, a simple B2B customer relationship management (CRM) application does not use the same amount of compute resources as a real-time video conferencing application.
Therefore, estimating a flat rate across all SaaS categories will lead to all types of budgeting problems.
Additionally, the standard advice fails to take into account many recent, very significant pricing changes.
In the past two years, the cloud landscape changed very quickly.
AWS started charging for all public IPv4 addresses.
The amount spent on AI and GPU computing jumped by 62% from year to year only in 2025.
If you built your budget using outdated blog posts, then your first quarter budget has already been inaccurate.
Real examples of AWS bills by level in the real world
Do not rely on guesses; instead, here is what actual AWS Bills appear to be for a SaaS application as it manipulates from its pre-launch phase to 100,000 customers.
Phase 1: Pre-launch and staging level ($80-$300/mo)
There are really no customers as of now, but you have all the hardware you need available for development, and therefore have your servers powered up all the time.
This is where the staging stage "trap" sneaks up on many founders.
If you have a decent application load balancer setup that runs 24/7 at this early stage (with a single small EC2 instance and a minimal rds instance, usually called a "production" database, your bill will generally float around $300/month.
This is waste!
The solution here is simple to implement:
When you shut down your staging environment overnight and on weekends using basic scheduling scripts, your exact configuration will cost you no more than about $80/month.
Simply implementing these scripts has the potential of saving you $2,760/year by the time your first product goes live.
Your architecture for your MVP should look heavily towards managed services and serverless architecture where it is feasible to do so.
An MVP utilizing a t3.small compute, a db.t4g.medium database, and a cache.t4g.small node can easily be expected to stay under $100/month.
Phase 2: 1,000 Active users benchmark (~$400/mo)
At 1,000 active users, you will have consistent daily usage and traffic.
Only having a single server isn't going to work anymore!
This is also where you are going to start transitioning into load balancing and high availability for your architecture.
In Phase Two, you'll set up an Application Load Balancer, and create auto-scaling groups with multiple EC2 Instances or Fargate Containers along with a Multi-AZ RDS database for redundancy.
You can expect to see your monthly bill for this service climb to approximately $400 per month.
Typically, the breakdown of costs will look something like this:
$180 for compute resources, $100 for the relational database, about $50 for Outbound Data Transfer, and the balance of $70 spread across Object Storage, Caching Layers and Managed DNS.
This is a significant point of inflection for a Cloud-based Application, as the cost structures begin to solidify, and you'll start to create a baseline for your cost per user metric.
Phase 3: Scaling to 10K users
Scaling from 1,000 to 10,000 users will create a major change in how we manage the database and deliver content.
To relieve some of the load from the primary database instance, you will likely need to set up Read Replicas of your database, and you will want to implement a Content Delivery Network (CDN), such as Amazon CloudFront, to serve your static assets and prevent your Data Transfer costs from killing your business.
By the time you scale to 10,000 users, your monthly spend will have increased to approximately $1,800.
At this stage, roughly 15% - 25% of your revenue will be directed towards Cloud-related expenses, and as you grow to maximum scale, this percentage should decrease down to approximately 5% -10%.
Up until this point, it can be very difficult to track the source of an unanticipated spike in your bill, so it is vitally important that when you reach 10,000 users you have established a per-tenant cost attribution system in place.
Phase 4: Enterprise scale (100,000 Users)
Upon reaching 100,000 users, your infrastructure enters into Enterprise territory.
The monthly cost will generally be around $6,000.
As with the 10,000 user tier, the actual amount you spend will depend heavily on how well your code and caching layers are designed.
The number of compute clusters will significantly increase in size from the previous tiers.
If your business is using SQS, a managed Kubernetes service (such as EKS), or building serverless event-driven architectures on a large scale, it is crucial to focus on FinOps, as a 10% reduction in compute cost can have a direct impact on a company's gross margin.
Estimating infrastructure expenses based on user counts is ineffective; it is far more accurate to calculate costs based on workload types that make up your application.
A flat rate of $1 per user implies nothing about the usage level associated with a heavy media-streaming application.
Determining the unit economics of an application can help calculate the gross margin opportunity.
Knowing the cost per month for each active user will identify when your SaaS becomes profitable.
Basic CRUD and CRM applications ($0.30-$1.00/User)
Most B2B SaaS products, as well as many text-based CRMs and basic project management tools, can be operated on very low budgets.
While these types of applications are read-focused, the payload sizes are typically small, and most users interact infrequently.
If you have built a good architecture for this type of application using Aurora Serverless or small EC2 instances, your estimated cost per month for an active user should be between $0.30 and $1.00.
Collaboration and workflow tools ($1.00-$2.50/User)
When applications allow for multiple editors to work simultaneously, sync with users in the background, and use more API calls than simple CRUD applications, the cost of supporting these applications will increase.
Document collaboration systems, as well as any type of complex tasks and project management software, will require extensive caching (using ElastiCache) and heavy computation to maintain quick updates and keep up with state changes from multiple concurrent users.
Users can expect to spend between $1.00 and $2.50 per month, on average, to operate these types of collaborative and workflow tools.
Data-intensive applications ($2.00-$5.00/User)
Applications that process large data sets, such as analytics dashboards, financial models, inventory control applications, etc., and analytics dashboards require heavy database performance and memory.
The primary cost associated with these types of applications is not compute but rather performance/performance and memory, as memory usage is required for large analytic queries and IOPs for analytic query execution time is usually high.
Costs to maintain data warehouses and data pipelines will yield a monthly expense of $2-$5 per user.
$3-$8 per user for real-time media and artificial intelligence
Real-time audio/video streaming (via video conferencing software) and generative artificial intelligence capabilities will be on the upper end of the cost continuum.
Constantly running web socket connections require high levels of computing, while the use of generative AI--either through internal models running on graphics processing units (GPUs) or through heavy use of external application programming interface (API) calls--will result in massive spikes in your monthly charges due to high compute requirements and/or excessive outbound data transfers.
Expect to pay between $3 and $8 per user, and carefully monitor usage limits.
Cloud cost killers of 2026
Basing your budget solely on server capacity will be detrimental to your success.
Several cloud cost drivers can often eliminate a company's financial forecast, especially in its early stages.
Public IPv4 Fees
Amazon Web Services (AWS) eliminated their policy of providing free public IPv4 addresses in February 2024.
They now charge a monthly fee of $3.65 for each public IP address associated with your AWS account.
While this appears to be a negligible amount initially, there is a multiplier effect when creating microservice architectures.
For instance, if you create 50 load balancers, network address translation (NAT) gateways, or other independent servers that needs a public IP address, you will incur a monthly fixed cost of $182 for your AWS account.
Such charges substantially alter your operational and cash flow models.
GPU charge waste associated with AI workloads
The average annual growth rate for infrastructure expenses associated with AI workloads (36%) is dwarfed by growth in AI workload-related charge waste due to equipment idling.
AI Founders are running up large monthly bills on "p4" or "g5" servers creating AI features due to tests executed before abandoning the servers.
Running a single high-performance GPU on a cloud provider over a weekend can cost many thousands of dollars.
As a result, it’s extremely important to tightly control how long instances are terminated for when building an MVP for AI.
Heavy data transfers
One of the biggest impacts to your bottom line when using AWS is data transfer out to the Internet.
Most calculators will fail to highlight this impact.
For example, if you plan on transferring 100 TB of data from AWS to your users, you will incur a bill of approximately $9,000 for that transfer if it originates directly from your servers.
While most standard guides from third-party agencies recommend that you keep an eye on your data transfer out to the Internet, they do not provide you with an example calculation.
Therefore, the value of having aggressive CDN caching through CloudFront cannot be emphasized strongly enough.
The use of edge caches when serving data from the Internet will significantly reduce the amount of core data transfer fees that you incur.
Accurate projections of server and AWS infrastructure costs
Knowing what benchmarks to use is one thing; accurately projecting your overall costs based on your operational reality is another.
Early mapping of your unit economics
You should take your total monthly AWS bill and divide that amount by the number of monthly active users you have.
From that point forward, you should keep close tabs on this ratio each month.
If your cost-per-user is increasing while your user base is also increasing, then your architecture is not scaling effectively.
In other words, Economies of Scale suggest that your cost per user will decrease as your number of tenants increases on the same infrastructure.
If you are a multi-tenant SaaS service, without proper tagging and instrumentation, you will be unable to see your cost per customer.
Don’t rush into savings plans or reserved instances
The most common mistake founders make when creating an AWS account is rushing into purchasing AWS Reserved Instances or Compute Savings Plans on day one.
While both of these plans offer discounts of up to 40 to 66%, in the early stages of building your architecture, you are likely to change it frequently.
Establishing a (typically) long-term arrangement for an instance family at the time you commence operations can be a mistake because production traffic is often not fully developed until two to six months after initial launch.
It is best not to enter into long-term pricing agreements until after the production environment is validated and the traffic patterns have matured.
Create a 15-Minute FinOps process
There is no requirement for you to have a dedicated FinOps engineer during the seed phase; however, discipline is essential to this stage.
If you can allocate 15 minutes at the start of every month for a simple FinOps process, you can help control your overall costs and be more astute regarding AWS costs.
Start by running AWS Cost Explorer each month, sorting your view by service and answering the following specific questions: 1) Which service saw the largest growth over the past 30 days? 2) Does the growth align with our acquisition of new users?
Your increased RDS costs may reflect 40% growth, but only 5% increase in users.
If your RDS costs jumped 40% with only 5% new users, it may reflect inefficient queries utilizing CPU resources, not an increase in costs associated with user growth.
This process is an example of a straightforward workflow that can expose a large number of infrastructure leaks.
How to forecast cloud expenses
Forecasting AWS infrastructure costs for early-stage SaaS companies requires you to move beyond overly simplistic agency calculators and generic budget estimates.
You must focus solely on your particular workload types.
A basic CRUD application generally would remain under $1.00 per month for each user, while an application with real-time data could typically achieve figures around $8.00 per month.
In addition to a lack of optimization, new applications face significant challenges regarding IPv4 pricing, poorly optimized staging environments, etc.
Building your muscle of tracking costs as soon as possible is critical.
Infrastructure bills directly tied to active users with basic FinOps workflows allows a previously frightening variable cloud expense to become a predictable, controlled metric of success.
With the proper planning and preparation prior to launch, your cloud bill will be an asset rather than a liability and will follow your asset growth as you scale.
Frequently Asked Questions (FAQs)
At what point should SaaS companies migrate from Serverless to EC2?
Serverless designs using Lambda and Aurora Serverless are ideal for MVPs since they have a zero scaling feature during periods of inactivity.
However, once an application develops a continuous consistent 24/7 baseline of users, it becomes an expensive proposition to pay for execution times rather than operate dedicated compute resources.
When Lambda expenditure exceeds the monthly equivalent of always-on EC2 instances or if cold starts degrade the user experience, it is time to transition.
What is the actual cost of my AWS MVP?
The actual infrastructure cost of an AWS MVP excluding salaries (engineering) or software licenses should be quite manageable.
A properly architected staging area with a lightly utilized production system using t3.small instances and managed services would typically cost between $100-$250/month.
Don't let agencies convince you that basic cloud hosting must cost tens of thousands of dollars to operate successfully.
What do I do if my AWS bill spikes without an increase in users?
Bill spikes without increases in users typically indicate architectural inefficiencies rather than scale.
Common sources of spikes include having staging environments on 24/7, runaway background processes absorbing compute cycles, badly indexed database queries using significant memory to execute or un-cached data leaving AWS.
To narrow it down to a service responsible for the spike, go to AWS Cost Explorer and sort by usage type.
How accurate are the AWS Pricing Calculators?
For exact line-item pricing estimates, the AWS Pricing Calculator is an excellent tool if you know exactly what architectural design you require.
The Pricing Calculator will accurately show you all projected unit prices listed.
The inaccuracy comes from user error, such as founders not including the cost for Data Transfer Out and costs for Public IPv4 IPs, Managed NAT Gateways, and storage overheads when estimating.
