Why Engineers Should Think Like CFOs
Every API call is a financial decision.
Most engineers don’t realize this, and they aren’t trained to think this way, because the cost of modern software is very rarely clear when a decision is made. Instead the cost becomes visible later as usage scales, as traffic spikes, or as systems begin to strain in ways that require more infrastructure, more safeguards, and more operational attention. By then though the architecture is already locked in and we’re likely committed for an extended period of time - sometimes years.
Engineering culture trains people to optimize for correctness, performance, and elegance. CFOs optimize for predictability, durability, and margin. Historically, those two concerns had no overlap, but today they live inside the system itself. Metered infrastructure, shared services, and operational overhead mean that technical decisions now directly shape financial outcomes, even when there is no explicit per-transaction fee.
APIs Create Cost Through Load, Not Just Price
It’s a mistake to think APIs only matter financially when they’re priced per call. Many APIs (even internally developed) are “flat-priced” or bundled, but can still result in very real costs. Excessive load drives increased infrastructure requirements, traffic spikes can result in over-provisioning, inefficient request patterns increase operational complexity and result in failure modes that require engineering time to manage. Cost doesn’t just show up as a line item per transaction but as headcount, stability work, and defensive engineering.
From an engineering perspective, an API call is treated like a black box: request goes in, response comes out. From a CFO’s perspective, that same call contributes to capacity planning, reliability risk, and future spend. Multiply it by retries, refresh behavior, or poorly bounded fan-out, and the system becomes more expensive to operate…even if the API itself is “free.”
Caching is an Economic Control
This is why caching, batching, and rate limiting should be understood as economic controls, not merely performance optimizations. Caching can reduce redundant load and smooth out demand, and rate limits prevent runaway usage patterns which could force premature scaling. Observability exposes not just slow paths, but when well understood it can highlight expensive ones. These techniques can be used to determine whether growth increases cost smoothly or violently. However, it is critical that meaningful and clear baselines are established for volume and bright red alerts are thrown when usage moves past those expectations…especially when hitting a “per call” API.
A Better Example: Massive API + Proxy Caching
Take the Massive API (for financial data related to stocks, options, and indices), which is not billed per request. At first glance, this might suggest that request volume doesn’t matter. In practice though it still does: excessive or poorly structured calls increase proxy load, increases the amount of downstream processing, and increases the operational cost of running the system that depends on it. Fortunately Massive doesn’t appear to force rate limits, however hitting the API repeatedly for the same operation does result in wasted time in the consuming app and, to a less visible extent, it consumes resources at Massive that others might need. The failures of this can cascade quickly.
A disciplined proxy layer can significantly change this equation though. Centralizing access allows identical requests to collapse into a single upstream fetch. Caching with intentional TTLs reduces unnecessary refreshes and faster response. Request shaping makes usage predictable rather than “bursty.” The result isn’t just better performance, it’s lower operational drag, clearer incident management, and less time spent fighting behavior the system allowed but never really needed.
The Mindset Shift That Actually Matters
None of this requires engineers to become finance experts. It does however require recognition that architecture incorporates economic assumptions whether you acknowledge them or not. The most important question should rarely be “is this API expensive?” but should always be “what does this design force us to pay for over time…and where will it present itself?”
The strongest engineering teams already think this way, at least at the leadership level. They design systems where operational cost doesn’t surprise leadership six months later, ensuring that growth is deliberate, load is measured, and in both cases well understood. Technical excellence and financial discipline should be used to reinforce each other because they are solving the same problem from different angles.
Thinking like a CFO doesn’t make engineers less technical, it makes their decisions accountable to reality. In systems that need to scale, reality doesn’t care how elegant a solution is, it only cares what it costs to keep running.
