Best Practices For Reducing Infrastructure Costs at Scale

How Cloud Costs Change as Your Game Scales

Keeping in mind our discussion on the barebones stack, the costs there were based largely on fixed costs (hourly prices that you cannot fully get rid of). These are very typical when operating at a small scale, and while they take up a proportionally large part of the invoice at the beginning, in absolute terms they're still relatively small.

When scaling up, however, other cost drivers emerge that eclipse these fixed costs. Let's dive into some of those:

Additional Cost #1: The Database Layer

We can consider for example the database layer. At small scale the instance costs are clearly the key driver of cost. It's not unreasonable to say that in the beginning, instance costs effectively make up all the database costs.

However, as games start scaling, you will see a necessary increase in instance costs - either through scaling up database instance sizes, other costs from I/O pricing, or from backup/snapshot storage.

Additional Cost #2: Load Balancing

A similar pattern emerges for load balancing: at small scale the load balancers are essentially a fixed cost. However, at scale, that fixed hourly cost becomes minor compared to the costs from load balancer "capacity units".

Capacity units are AWS's way to aggregate the work done by a load balancer, capturing multiple dimensions ranging from throughput to connection counts. In some cases, we have also seen content distribution costs become quite high compared to the costs of the rest of the stack at scale - even reaching sizes of almost a quarter of total infrastructure costs.

Why Scaling Costs Are Not All Bad News

While the cost dynamics above may seem daunting, there are some positive observations to keep in mind.

First, in the greater scheme of a successful live service game's revenue, infrastructure costs are often relatively minor. A game generating significant revenue from its player base will typically find that cloud costs represent a small fraction of overall expenses — and one that's well worth the investment.

Second, cloud and infrastructure costs can actually diminish at large scale on a per-player basis. As your player base grows, you benefit from economies of scale: the fixed overhead is spread across more users, and volume discounts from cloud providers become available.

Third, most of the rising costs discussed above can be addressed through targeted optimization. Infrequently accessed data can be moved to cold storage tiers at a fraction of the cost. Content distribution expenses can be reduced by evaluating alternative CDN providers or implementing smarter caching strategies. And financial planning tools like AWS Reserved Instances or Savings Plans can significantly reduce the unit cost of compute resources for predictable workloads.

How to Reduce Game Infrastructure Costs at Scale

Prioritize Quality Code to Lower Compute Expenses

Developing a game locally and running it for a small technical demo audience is wildly different than running a game that's played by hundreds of thousands of people every single day. Quick and sloppy prototyping can often be tolerated at the beginning of game projects.

And that's understandable: the benefits from spending extra time optimizing your code are small compared to being able to quickly demonstrate new game mechanics to test out retention numbers or pass funding gates. However, when scaling up a game, these types of shortcuts together with quality assurance issues can be one of the single biggest drivers for increasing costs.

While this area is not strictly speaking only in the domain of cloud infrastructure, it's highly relevant to understand and consider when evolving and maturing a game production - and the organization that runs it. Keeping player models tight, code paths optimized, and memory management under control means less compute resources, and less costs as you grow.

Leverage Empirical Load Testing for Accurate Cost Estimates

An excellent tool in understanding resource consumption at scale is carrying out realistic load testing against the game.

This is true for any backend project, and there are various tools available, but in the Metaplay ecosystem one that we particularly like is our bot client framework (read more about that in the Metaplay docs).

This allows you to easily build bots which mimic action patterns of real players, and we provide tooling to help run bot clients at large quantities.

Running bots, besides being heaps of fun, help us in two crucial ways:

1. Hunting down regressions before they hit production.
2. Understanding the impact of new features and functionality on resource consumption.

For cost estimation, running load tests are easily the best way of getting meaningful cost estimates while taking into account the nature of the game. These load tests are also convenient for capacity planning ahead of time.

Key Takeaways for Managing Game Infrastructure Costs

Scaling up game infrastructure fundamentally changes your cost dynamics. What starts as a simple, predictable set of fixed costs evolves into a more complex picture where database layer expenses, load balancer capacity units, and content distribution costs can eclipse your initial baseline. Understanding this shift early helps you plan and budget more effectively.

The good news is that scaling also brings opportunities. Per-player costs tend to decrease as you grow, cloud provider discounts become available at volume, and the revenue generated by a larger player base typically far outweighs the infrastructure investment needed to support it.

The two most impactful levers you have for controlling costs are code quality and empirical testing. Investing in tight, well-optimized game logic — particularly around player models, memory management, and server-side code paths — directly reduces the compute resources your game consumes at scale. And running realistic load tests with bot clients gives you the data you need to forecast costs accurately, plan capacity ahead of launches, and catch costly regressions before they reach production.

Infrastructure Cost Optimization FAQ

What are the biggest cost drivers when scaling a mobile game?

At scale, the primary cost drivers shift from fixed infrastructure costs to variable costs in the database layer (instance sizes, I/O pricing, backup storage) and load balancing (capacity units based on throughput and connection counts). Content distribution can also reach nearly a quarter of total infrastructure costs.

How can load testing help reduce infrastructure expenses?

Load testing with realistic bot clients helps you understand actual resource consumption patterns before hitting production. This allows for more accurate cost estimation, capacity planning, and identification of code-level inefficiencies that would multiply costs at scale.

Should I optimize code for costs during prototyping?

No. During early prototyping, speed of iteration matters more than code optimization. However, as your game approaches scale, code quality becomes one of the single biggest drivers of infrastructure costs. Tight player models, optimized code paths, and controlled memory management directly reduce compute resource needs.

What AWS cost optimization strategies are available for game backends?

Key strategies include utilizing cold storage for infrequently accessed data, exploring alternative content distribution providers, and implementing Reserved Instances or Savings Plans for predictable workloads. These financial planning tools can significantly reduce the unit cost of infrastructure at scale.