A thought-provoking question that keeps surfacing in boardrooms, engineering huddles, and founder roundtables: is the cloud becoming too expensive to sustain at scale? As a long-time tech entrepreneur, I’ve watched cloud economics evolve—from the early utility model to today’s dynamic pricing, egress costs, and multi-cloud fragmentation.
The short answer isn’t black-and-white, but there’s a real conversation to be had about total cost of ownership, control, risk, and strategic priorities.
For many years, the cloud has been the default platform for building and scaling software. It offered rapid prototyping, global reach, and a predictable operating model. But as organizations scale, the steady drumbeat of ongoing costs—compute, storage, data transfer, managed services, security, compliance—begins to look less like a lever and more like a constraint.
In conversations with fellow founders and CIOs, several themes repeat themselves:
- Cloud bill surprises as traffic and data egress grow
- Complexity and operational overhead of multi-cloud or edge deployments
- The cost of security, governance, and regulatory compliance in the cloud
- The challenge of meaningful cost optimization without sacrificing velocity
This post isn’t an outright indictment of cloud workloads. It’s a practical examination of when and why some teams begin to consider unbundling from hyperscale providers, at least for parts of their stack.
To build a grounded argument, it helps to break down cloud costs into categories and understand how each has evolved.
1) Compute and storage: the raw price vs. the efficiency premium
Raw prices for VMs and storage have trended downward in some segments, but the real-world TCO includes:
- Right-sizing and overprovisioning
- Reserved instances, savings plans, and commitment discounts
- Data transfer costs between services, regions, and the public internet
- Efficiency matters: modern serverless and containerized workloads can reduce
idle costs but add orchestration and reliability complexity.
2) Data transfer and egress
Ingress may be free or cheap; egress often carries a surprising premium, especially for data-heavy workloads,
analytics with external BI tools, or partners consuming your APIs.
Cross-region and cross-cloud transfer amplify costs, sometimes unexpectedly.
3) Managed services and fragmentation
Managed databases, queues, analytics, AI/ML services, and security tooling
are compelling but can create lock-in and creeping per-use charges.
The more you rely on PaaS abstractions, the harder it is to re-implement elsewhere,
which can raise the perceived “cost of mobility.”
4) Security, compliance, and governance
Cloud-native security tooling is powerful, but it’s not free. Identity, access management,
logging, monitoring, and compliance regimes (CISA, GDPR, HIPAA, etc.) add layers of cost and complexity.
Shared responsibility remains real: more control often means more responsibility for
configuration, drift, and incident response.
5) OpEx vs CapEx and financial optics
Cloud shifts capital expenses into operating expenses, which has advantages (cash flow, scalability) and drawbacks (uncertainty in long-term planning, budgeting fragmentation).
For some companies, cash flow is the driver; for others, total cost of ownership and risk posture matter more.
It isn’t just about price tags. Consider these decision vectors:
Predictable, high-volume workloads:
If you can forecast demand with precision, a private data center or hybrid model may yield cost
discipline and performance certainty.
Data gravity and regulatory constraints:
Jurisdictional data residency, sovereign clouds, or latency-sensitive workloads can
justify a private or on-prem deployment.
Total cost of ownership and lifecycle:
If your stack relies on high-touch, specialized hardware, bespoke networking, or custom
security postures, owning the stack end-to-end can become cost-competitive.
Talent strategy and risk management:
Some teams value the control and transparency of their own ops, especially in industries with
stringent audit requirements.
Vendor lock-in awareness:
A conscious, measured approach to portability and modular architectures
can reduce long-term risk.
If you’re seriously weighing a shift, here’s a practical framework to guide the conversation:
1) Map the current bill of materials
Inventory all cloud services, data transfer, storage classes, and managed services.
Quantify usage by workload, environment (prod/staging/dev), and region.
2) Define cost drivers by workload
Identify which workloads are most expensive to operate in the cloud.
Consider latency, data gravity, and dependency networks.
3) Model scenarios with TCO in mind
Pure cloud baseline (your current architecture with optimization).
Hybrid model (keep core services in the cloud, move particular components on-prem or to edge).
Full on-prem/private cloud (invest in hardware, facilities, and orchestration but gain control).
4) Assess risk and resilience
Reliability, disaster recovery, and business continuity implications.
Security posture, incident response time, and regulatory compliance implications.
5) Consider talent and execution risk
Do you have or need the in-house expertise to operate private infrastructure at scale?
What’s the path to migration, skills uplift, and vendor independence?
6) Build a staged plan
Start with pilot projects, strict success criteria, and measurable cost savings.
Establish guardrails to prevent scope creep and uncontrolled cost escalation.
Thanks for taking the time to read this blog post. I hope you found it informative and thought provoking.
My name is Paul Lomax, a serial entrepreneur for over 4 decades, CEO of Bluedog Cyber Security, SaaS
founder, Non-Exec Director, C# developer and SaaS Startup Advisor.
Over many years I've gained knowledge and insight into how to build a great business, and exit when the
time is right.
I'm available for freelance work, non-executive directorships and advisory roles. Check out my Services Pages for more information. Please feel free to
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