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Every startup’s journey is unique, and the road to success is never
linear, but cost is a narrative in every business at every point in time,
especially during economic downturns. In a startup, the conversation around
cost shifts when moving from the experimental and gaining traction
phases to high growth and optimizing phases. In the first two phases, a
startup needs to operate lean and fast to come to a product-market fit, but
in the later stages the importance of operational efficiency eventually
Shifting the company’s mindset into achieving and maintaining cost
efficiency is really difficult. For startup engineers that thrive
on building something new, cost optimization is typically not an exciting
topic. For those reasons, cost efficiency often becomes a bottleneck for
startups at some point in their journey, just like accumulation of technical
In the early experimental phase of startups, when funding is limited,
whether bootstrapped by founders or supported by seed investment, startups
generally focus on getting market traction before they run out of their
financial runway. Teams will pick solutions that get the product to market
quickly so the company can generate revenue, keep users happy, and
In these phases, cost inefficiency is an acceptable trade-off.
Engineers may choose to go with quick custom code instead of dealing with
the hassle of setting up a contract with a SaaS provider. They may
deprioritize cleanups of infrastructure components that are no longer
needed, or not tag resources as the organization is 20-people strong and
everyone knows everything. Getting to market quickly is paramount – after
all, the startup might not be there tomorrow if product-market fit remains
After seeing some success with the product and reaching a rapid growth
phase, those previous decisions can come back to hurt the company. With
traffic spiking, cloud costs surge beyond anticipated levels. Managers
know the company’s cloud costs are high, but they may have trouble
pinpointing the cause and guiding their teams to get out of the
At this point, costs are starting to be a bottleneck for the business.
The CFO is noticing, and the engineering team is getting a lot of
scrutiny. At the same time, in preparation for another funding round, the
company would need to show reasonable COGS (Cost of Goods Sold).
None of the early decisions were wrong. Creating a perfectly scalable
and cost efficient product is not the right priority when market traction
for the product is unknown. The question at this point, when cost starts
becoming a problem, is how to start to reduce costs and change the
company culture to sustain the improved operational cost efficiency. These
changes will ensure the continued growth of the startup.
When a company uses multiple service providers (cloud, SaaS,
development tools, etc.), the usage and cost data of these services
lives in disparate systems. Making sense of the total technology cost
for a service, product, or team requires pulling this data from various
sources and linking the cost to their product or feature set.
These cost reports (such as cloud billing reports) can be
overwhelming. Consolidating and making them easily understandable is
quite an effort. Without proper cloud infrastructure tagging
conventions, it is impossible to properly attribute costs to specific
aggregates at the service or team level. However, unless this level of
accounting clarity is enabled, teams will be forced to operate without
fully understanding the cost implications of their decisions.
Engineers consider various factors when making engineering decisions
– functional and non-functional requirements (performance, scalability
and security etc). Cost, however, is not always considered. Part of the
reason, as covered above, is that development teams often lack
visibility on cost. In some cases, while they have a reasonable level of
visibility on the cost of their part of the tech landscape, cost may not
be perceived as a key consideration, or may be seen as another team’s
Signs of this problem might be the lack of cost considerations
mentioned in design documents / RFCs / ADRs, or whether an engineering
manager can show how the cost of their products will change with scale.
Companies sometimes maintain custom tools that have major overlaps in
capabilities with third-party tools, whether open-source or commercial.
This may have happened because the custom tools predate those
third-party solutions – for example, custom container orchestration
tools before Kubernetes came along. It could also have grown from an
early initial shortcut to implement a subset of capability provided by
mature external tools. Over time, individual decisions to incrementally
build on that early shortcut lead the team past the tipping point that
might have led to utilizing an external tool.
Over the long term, the total cost of ownership of such homegrown
systems can become prohibitive. Homegrown systems are typically very
easy to start and quite difficult to master.
Having multiple tools with the same purpose – or at least overlapping
purposes, e.g. multiple CI/CD pipeline tools or API observability tools,
can naturally create cost inefficiencies. This often comes about when
there isn’t a paved
and each team is autonomously picking their technical stack, rather than
choosing tools that are already licensed or preferred by the company.
Choosing managed services for non-differentiating capabilities, such
as SMS/email, observability, payments, or authorization can greatly
support a startup’s pursuit to get their product to market quickly and
keep operational complexity in check.
Managed service providers often provide compelling – cheap or free –
starter plans for their services. These pricing models, however, can get
expensive more quickly than anticipated. Cheap starter plans aside, the
pricing model negotiated initially may not suit the startup’s current or
projected usage. Something that worked for a small organization with few
customers and engineers might become too expensive when it grows to 5x
or 10x those numbers. An escalating trend in the cost of a managed
service per user (be it employees or customers) as the company achieves
scaling milestones is a sign of a growing inefficiency.
In any architecture, the cost is correlated to the number of
requests, transactions, users using the product, or a combination of
them. As the product gains market traction and matures, companies hope
to gain economies of scale, reducing the average cost to serve each user
or request (unit
as its user base and traffic grows. If a company is having trouble
achieving economies of scale, its unit cost would instead increase.
Figure 1: Not reaching economies of scale: increasing unit cost
Note: in this example diagram, it is implied that there are more
units (requests, transactions, users as time progresses)