We’ve talked at length about what informs the choice between public vs. private cloud for organizations. Today, we’ll zoom in on the financial implications of that choice.
Infrastructure design is never neutral. Whether you pick public or private cloud directly affects workload economics, operational overhead, and security posture. It’s not about comparing features in isolation; it’s about how cost and control evolve over time.
That’s why Total Cost of Ownership (TCO) is a realistic, balanced, and comprehensive metric for assessing infrastructure options. It forces us to look at the full economic profile: compute, storage, network transfers, service-level pricing, management overhead, compliance, and the trade-offs between elasticity and predictability.
Two Models, Two Philosophies
Public Cloud
Public cloud providers, such as AWS, offer unmatched elasticity, a global presence, and a service catalog broad enough to cover nearly any workload pattern. Compute, storage, databases, analytics, observability, and security are available as ready-to-use managed services. For organizations that aim to build something rapidly, this convenience is decisive.
But the underlying billing model is service-fragmented and consumption-driven. Components come with their own metering logic. Costs grow in non-linear ways as workloads touch more services. As the business matures and expands, these costs pile up.
The result: flexibility is maximized, but cost predictability is minimized. Without dedicated optimization, TCO in public clouds such as AWS can drift 2–3× above initial projections.
Private Cloud
Private cloud, by contrast, takes the opposite stance. At Advanced Hosting, for instance, OpenStack-based deployments are billed at a flat per-node rate with core services already included. (+ a little elaboration on additional services)
This simplicity enables a different optimization strategy:
- Flat node pricing – architectural complexity doesn’t translate into financial one.
- High resource density – an experienced private cloud vendor can expertly consolidate workloads into fewer, larger nodes, reducing cost per VM.
- CPU overcommit policies increase logical compute capacity per physical core, extracting more value from the same hardware.
- Custom VM shapes (CPU-optimized, RAM-optimized, storage-rich) ensure resources align directly with workload demands, eliminating wasted capacity.
For predictable, resource-dense workloads, this model produces a lower and more stable TCO curve than public cloud, without the overhead of managing long-term commit strategies.
Optimize costs without compromising performance with Private Cloud
What Makes Up the Real TCO
The real economics of cloud infrastructure emerge once workloads are mapped, services are layered, and scaling patterns are established over months or years. That’s where TCO separates the public and private models – and why mature businesses tend to converge on private cloud when predictability, compliance, and optimization outweigh the appeal of short-term elasticity.
In a public cloud like AWS, the baseline infrastructure bill is inherently fragmented. Compute, storage, databases, observability, and data transfers are each priced differently, each having its own scaling logic. Even with 1-, 2-, or 3-year commit models designed to tame variability, the problem is structural: as architectures grow more complex, the bill grows non-linearly.
Yes, for engineering-heavy startups chasing speed, this flexibility is worth the price. But not so much for organizations with stable or growing workloads. For them, the chaos becomes a budgeting liability, and entire FinOps teams often exist just to manage it.
By contrast, a private OpenStack cloud runs on a flat per-node model. Services like Keystone, Cinder, or Neutron don’t appear as separate billing items – they simply consume node resources. Of course, adding databases, logging stacks, and additional services means allocating more hardware, so the cost will reflect that. But the expenses are vastly more linear and forecastable: more capacity means more nodes, and the company isn’t getting an expanding lattice of metered services.
For a business operating at scale, that linearity is financially strategic. It shifts optimization from bill parsing to infrastructure design: how well you consolidate workloads, tune overcommit policies, and right-size nodes.
There’s also a divide in security and compliance. Hyperscalers like AWS can point to an impressive list of global certifications – SOC, ISO, PCI DSS, HIPAA – which reduces audit burden. But security in a public cloud is still standardized and shared. You inherit controls, but you don’t dictate them. Private cloud environments, by contrast, allow for isolation at the rack level, encryption where even the provider’s staff can’t access data, and governance tailored to the exact compliance framework the business must meet.
Scaling is where philosophies diverge most clearly. Public cloud scaling is instantaneous: new instances spin up in minutes, traffic bursts are absorbed automatically. But elasticity, as we’ve pointed out, always comes at a cost. In a private cloud, scaling is slower, tied to hardware expansion and capacity forecasting.
At first glance, this may look like a disadvantage. Yet for enterprises with predictable baselines, this discipline enforces cost efficiency. And there are also hybrid models that offer a compromise: keep the bulk of workloads in a private cluster for cost stability, and burst into public cloud only during traffic spikes.
At the same time, it’s rarely the case that private cloud replaces public cloud outright, given the two advantages the latter offers:
- Breadth of advanced services – managed AI/ML platforms, enterprise-grade analytics, serverless execution engines, and global-scale identity management.
- Universal certifications – ISO, SOC, HIPAA, PCI DSS, across virtually every jurisdiction.
That’s why many enterprises often keep their core workloads on hyperscalers, where global compliance and specialized services are non-negotiable – and shift cost-heavy, predictable, or highly-sensitive layers into private cloud. This hybrid strategy gives them the best of both worlds: hyperscaler services where necessary, and private cloud efficiency and isolation, where economics or security demand it.
This is why we always stress: when it comes to cloud comparisons, you don’t judge platforms – you compare business cases. Public and private clouds can be complementary. But for mature businesses with steady, resource-dense workloads, moving the right components into a private cloud is often the difference between spiraling costs and sustainable growth.
Why Real-World Scenarios Matter
What matters here is how pricing models behave once mapped against specific workloads, scaling patterns, and compliance requirements. A line-item cost calculator rarely tells the full story; only real-world cases do.
One of our clients in the iGaming sector illustrates this well. They had grown quickly on AWS, using a mix of EC2, RDS, and S3 to power a regulated betting platform spanning multiple jurisdictions. The speed of the hyperscaler was valuable during the expansion phase, but as workloads stabilized, the economics began to break down. Monthly infrastructure spend ballooned from $35,000 to more than $90,000 in a year. Long-term planning became almost impossible as every additional service – database replication, CloudWatch logging, cross-AZ transfers – introduced new billing variables.
The company wasn’t new to Advanced Hosting; they had already used our bare metal for archival storage, keeping production in AWS. That hybrid setup became their safety valve. When AWS costs surged, they revisited the model and migrated their core workloads into a hosted OpenStack-based private cloud.
The architecture mirrored key AWS services one-to-one:
- EC2 workloads were replatformed on OpenStack Nova.
- RDS instances were replaced with managed PostgreSQL and MSSQL from Severalnines.
- S3 was swapped for Ceph Object Gateway.
- CloudWatch was replicated with Prometheus, VictoriaMetrics, and Grafana.
From a functionality standpoint, they retained cloud-native features. From a cost standpoint, they cut their projected five-year TCO by more than 50% – from over $8 million on AWS to just over $4 million on private cloud, even after factoring in the one-time migration effort.
It’s important to be clear: this wasn’t about abandoning public cloud altogether. The client continues to use the hyperscaler where its services and certifications are indispensable, especially when entering new regulated markets. But by moving steady-state, resource-dense workloads – databases, compute clusters, storage – into our private cloud, they gained predictable economics without losing flexibility.
That’s why the right lens is never “AWS vs. OpenStack” or “public vs. private.” The real question is: where should each workload live to balance cost, compliance, and flexibility?
Flexibility Doesn’t Equal Complexity: How Private Cloud Can Lower TCO
One misconception about private cloud is that moving away from hyperscalers inevitably means losing flexibility. In practice, a well-designed private cloud can deliver predictable economics with enough architectural freedom to tune resources precisely to workload needs. And this flexibility lowers TCO without introducing unnecessary complexity.
Turn infrastructure into a predictable cost curve
High Resource Density: Fewer, Larger Nodes
Instead of fragmenting workloads across many small instances, private cloud encourages packing more threads and resources into larger, more powerful nodes. With careful sizing, this reduces the number of physical servers required, lowers cost per VM, and simplifies operational overhead.
Client-Controlled CPU Overcommit for Better Utilization
Unlike in AWS, where instance oversubscription policies are opaque to the customer, private cloud users have direct control over CPU overcommit ratios. This autonomy allows teams to push utilization levels in line with their workload profiles, achieving better efficiency per dollar spent without being constrained by pre-defined instance types.
Custom-Optimized VM Configurations
Private cloud eliminates the rigidity of hyperscaler catalogs. Clients can provision CPU-heavy machines for stateless services, RAM-optimized nodes for in-memory databases, or storage-rich instances for throughput-intensive workloads.
In practice, these levers – flat pricing, large-node density, client-controlled overcommit, and tailored VM types – give mature businesses the ability to optimize infrastructure around actual workload behavior, rather than bending workloads to fit a hyperscaler’s billing model.
When Private Cloud Is the Smart Choice
Private cloud becomes the obvious choice when workloads are predictable and long-term. Applications that run continuously – iGaming platforms, financial transaction systems, ERP backends – benefit from flat, node-based economics that avoid the billing volatility of hyperscalers. Over multi-year horizons, this stability translates into significant TCO savings.
It’s also better suited for organizations with specialized DevOps and infrastructure needs. In private environments, engineering teams have full control over networking, observability, gateways, and CI/CD pipelines. That level of autonomy reduces reliance on vendor-managed services and enables tighter optimization around workload behavior.
Public cloud still delivers unmatched value for mid-sized companies and rapidly growing projects, where instant scalability and a broad service catalog outweigh cost predictability. The trade-off is volatility, which smaller teams often accept as the price of speed.
Conclusion
Public and private cloud models aren’t adversaries – they serve different stages of business maturity. Public cloud excels at elasticity, supporting businesses that prioritize speed, global reach, or niche managed services. Private cloud excels at predictability, giving mature enterprises the ability to stabilize costs, enforce isolation, and optimize TCO over the long term.
For most organizations, the real answer is hybrid: keep critical, globally certified workloads on hyperscalers, and shift steady-state, resource-heavy operations into private cloud. The point isn’t to choose a winner, but to map workloads to the right cost and control model. Mature businesses increasingly discover that private cloud is where sustainable economics and operational sovereignty meet.
At Advanced Hosting, we help enterprises map their real usage, identify where hyperscalers still make sense, and where private cloud delivers measurable TCO advantages. Curious to learn how our Private Cloud can optimize your performance and costs.