Comparing AWS instance costs across processor families, regions and purchasing models is far more complex than it should be. Engineers are forced to rely on pricing pages, spreadsheets and external estimation tools that operate outside the infrastructure lifecycle. This makes architectural cost tradeoffs slow, approximate and disconnected from real system design.
This article shows how cost comparison becomes dramatically simpler when pricing is derived directly from infrastructure definitions. Using a minimal Stateless IaC template, it demonstrates how MechCloud enables precise, real-time cost comparison across Intel, AMD and ARM EC2 instances while preserving stateless, region-agnostic provisioning. The result is a workflow where cost comparison, architectural reasoning and infrastructure provisioning all happen in the same place.
Why cost comparison is still hard
Tools like Infracost and Vantage have made important contributions by improving visibility into cloud spend. However, they fundamentally operate outside the infrastructure execution model. They infer cost by analyzing Terraform plans, cloud usage data or billing exports, which introduces unavoidable approximation and lag.
This creates several practical problems. Cost comparison is rarely real time. Architectural context is often lost. Subtle differences in processor families, storage layouts, spot behavior and free tier interactions are difficult to model accurately. Most importantly, engineers are forced to context switch between infrastructure code and external cost tools, breaking the natural workflow of system design.
Effective cost comparison requires cost to be computed as a direct consequence of infrastructure state. When cost is derived from the same execution graph that provisions resources, comparisons become precise, immediate and architecture-aware.
MechCloud enables this by embedding cost computation directly into its Stateless IaC engine. Infrastructure definitions resolve into a concrete execution plan and pricing is computed in real time using live cloud pricing models. This allows engineers to compare architectural options directly inside their infrastructure workflows instead of relying on detached estimation layers.
A minimal multi-architecture template
Here is the complete Stateless IaC template used in this example.
resources:
- name: intel-vm
type: aws_ec2_instance
props:
instance_type: "t3.small"
image_id: {{Image|x86_ubuntu_24_04}}
- name: amd-vm
type: aws_ec2_instance
props:
instance_type: "t3a.small"
image_id: {{Image|x86_ubuntu_24_04}}
- name: arm-vm
type: aws_ec2_instance
props:
instance_type: "t4g.small"
image_id: {{Image|arm64_ubuntu_24_04}}
This template provisions three EC2 instances of comparable size, each backed by a different processor architecture. The only variables are the processor families themselves. This makes it possible to compare pricing, performance tradeoffs and operational implications in a controlled and reproducible way.
Another important detail is the use of image ID aliases instead of region-specific AMI IDs. The aliases x86_ubuntu_24_04 and arm64_ubuntu_24_04 represent semantic operating system identities rather than physical AMI identifiers. MechCloud resolves these automatically to the correct AMI for the target region at runtime, keeping the template portable and region agnostic.
This single abstraction removes one of the most brittle elements of cloud automation while making templates significantly easier to read and review.
From template to cost plan
The template above describes the desired state. When evaluated, MechCloud produces an explicit infrastructure plan that shows the exact resources that will be created along with a fully decomposed real-time pricing model. This plan is not an estimate derived from generic calculators. It is computed directly from the resolved infrastructure graph.
Below is the plan generated for this template.
intel-vm (action: create, monthly: $18.62, change: +100%)
=> Price (Compute - price: $0.024/Hrs, monthly: $17.86, spot-price: $0.0085/Hrs, spot-monthly: $6.32)
=> Volume 1 (/dev/sda1 - monthly: $0.76)
=> Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
=> Price (IOPS - monthly: $0.00)
=> Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
=> Price (Throughput - monthly: $0.00)
=> Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)
amd-vm (action: create, monthly: $16.83, change: +100%)
=> Price (Compute - price: $0.0216/Hrs, monthly: $16.07, spot-price: $0.0109/Hrs, spot-monthly: $8.11)
=> Volume 1 (/dev/sda1 - monthly: $0.76)
=> Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
=> Price (IOPS - monthly: $0.00)
=> Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
=> Price (Throughput - monthly: $0.00)
=> Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)
arm-vm (action: create, monthly: $15.04, change: +100%)
=> Price (Compute - price: $0.0192/Hrs, monthly: $14.28, spot-price: $0.007/Hrs, spot-monthly: $5.21)
=> Free Trial (Dec 2026) (Account - free-hours: 750, monthly-discount: $14.40)
=> Volume 1 (/dev/sda1 - monthly: $0.76)
=> Price (Storage cost (gp3) - price: $0.0952/GB-Mo, quantity: 8, monthly: $0.76)
=> Price (IOPS - monthly: $0.00)
=> Tier 1 (First 3000 IOPS-Mo - price: $0.00/IOPS-Mo, quantity: 3000, monthly: $0.00)
=> Price (Throughput - monthly: $0.00)
=> Tier 1 (First 125 MiBps-mo - price: $0.00/MiBps-mo, quantity: 125, monthly: $0.00)
This plan becomes the foundation for all further analysis. Because pricing is derived from the resolved infrastructure state, the numbers reflect real deployment semantics including region specific AMI resolution, volume defaults, free tier effects and spot market context. From here, architectural reasoning flows naturally.
When this template is evaluated, MechCloud generates a fully decomposed cost plan that maps pricing directly onto infrastructure resources. Instead of grouping cost by service or region, the plan expresses cost in terms of concrete infrastructure objects.
At the monthly on-demand level, the results are straightforward.
Intel using t3.small comes out to $18.62 per month. AMD using t3a.small reduces that to $16.83 per month. ARM using t4g.small further reduces it to $15.04 per month.
At first glance, this simply shows ARM as the cheapest option. The real value, however, lies in how the cost is broken down and contextualized.
Understanding what actually drives cost
Each instance is expanded into its constituent cost drivers. Compute, storage, IOPS, throughput, discounts and free tier credits are represented explicitly. This allows engineers to understand exactly where money is being spent and why.
For the Intel-based instance, almost the entire monthly cost comes from compute. Storage contributes only $0.76 per month for an 8 GB gp3 root volume, while compute accounts for $17.86. This makes it immediately clear that for small general-purpose workloads, optimization efforts should focus on compute choices rather than storage tuning.
The AMD-based instance follows the same pattern, but compute drops to $16.07 per month. Nothing else changes, which isolates the cost difference entirely to processor family. This makes the 10 percent saving attributable to AMD unambiguous.
The ARM-based instance further reduces compute cost to $14.28 per month. The storage profile remains identical. At this point, the cost difference is no longer marginal. For always-on workloads, ARM offers roughly 20 percent savings over Intel for the same resource profile.
This style of cost modeling allows teams to validate architectural decisions using data that directly reflects their infrastructure, rather than relying on high-level pricing tables or abstract calculators.
Spot pricing and architectural nuance
The same plan also exposes spot pricing in the same structural context.
Intel drops to $0.0085 per hour, AMD to $0.0109, and ARM to $0.007. Interestingly, this reverses part of the on-demand ordering. AMD is cheaper than Intel for on-demand usage, but Intel becomes cheaper than AMD on spot. ARM remains the lowest-cost option across both models.
These subtleties are difficult to discover using traditional cost tooling. They emerge naturally when cost is computed directly from infrastructure semantics. This enables more accurate reasoning about batch workloads, fault-tolerant systems and environments where spot capacity plays a significant role.
Region-agnostic infrastructure with image ID aliases
One of the most persistent operational pain points in cloud automation is managing AMI IDs. They differ by region, change frequently and are tightly coupled to operating system versions. This leads to brittle templates, complex parameterization and frequent failures.
Image ID aliases eliminate this problem entirely. By using semantic identifiers such as x86_ubuntu_24_04 and arm64_ubuntu_24_04, templates express intent rather than implementation detail. MechCloud resolves these identifiers to the correct AMI for each region automatically.
This has several architectural consequences. Templates become portable across regions without modification. Reviews become easier because the operating system intent is explicit. Large-scale automation pipelines avoid entire classes of failure caused by stale AMI references. Over time, this significantly reduces operational friction and cognitive load for platform teams.
Infrastructure-native cost reasoning
Most cost tooling starts from billing data and works backward toward infrastructure. This inversion creates a fundamental mismatch between how engineers design systems and how cost is reported.
Stateless IaC reverses this flow. Infrastructure definitions become the primary object and cost is derived directly from them. This aligns financial reasoning with architectural reasoning. Decisions about instance families, storage classes or deployment patterns can be evaluated in the same place they are defined.
For cloud architects, this means architecture reviews can include precise cost implications. For DevOps teams, it means experimentation becomes safer, faster and more predictable. Instead of deploying and observing cost later, teams can reason about cost before anything is provisioned.
FinOps and platform engineering implications
Traditional FinOps practices rely heavily on post-facto analysis of billing exports, dashboards and tagging strategies. While useful, this approach is fundamentally reactive. By the time cost anomalies are detected, the architecture decisions that caused them are already in production.
Infrastructure-native cost modeling shifts this workflow upstream. Platform teams can embed cost reasoning directly into provisioning workflows, architectural reviews and CI pipelines. Engineers can evaluate cost impact at design time, not after deployment. This creates a tighter feedback loop between engineering and finance without slowing delivery.
For platform engineering teams, this also simplifies governance. Instead of enforcing cost controls through complex policy layers and reporting pipelines, cost becomes a first-class property of infrastructure definitions. This enables clearer guardrails, predictable budgets and safer self-service infrastructure for application teams.
In practice, this alignment between infrastructure, cost and automation is what allows FinOps and platform engineering to scale together.
Drift detection and cost drift control
Traditional infrastructure tooling separates deployment, drift detection and cost monitoring into disconnected systems. Terraform plans describe desired state, monitoring systems detect runtime drift and cost platforms analyze billing data after the fact. This fragmentation makes it difficult to reason about how architectural changes translate into long-term cost behavior.
Stateless IaC unifies these concerns. Because the infrastructure graph is continuously reconciled and priced in real time, drift becomes both a configuration problem and a cost problem. If infrastructure diverges from the desired state, the cost model diverges with it. This allows teams to detect not only configuration drift but also financial drift.
For example, an instance family change, storage class upgrade or region migration immediately surfaces as a structural and financial delta in the plan. Engineers can see the operational and budget impact of drift before it accumulates into meaningful spend. This tight feedback loop enables proactive cost control rather than reactive investigation.
In practice, this means cost governance becomes part of infrastructure reconciliation instead of an external audit process.
Terraform plus cost estimation versus Stateless IaC
In most environments today, Terraform defines infrastructure while separate tools attempt to estimate cost. This split introduces unavoidable uncertainty. Cost tools operate on heuristics, incomplete dependency graphs and static price assumptions. As a result, estimates are often directional rather than precise.
Stateless IaC collapses this separation. The same engine that resolves infrastructure dependencies also computes pricing using live cloud billing models. Cost becomes a deterministic function of infrastructure state rather than an approximation layered on top of it.
This architectural difference matters. It allows engineers to evaluate infrastructure decisions with the same rigor they apply to system design. Instance families, storage strategies, spot adoption and regional placement can all be compared using real execution semantics instead of abstract calculators.
Over time, this reduces both operational surprises and financial volatility.
Closing thoughts
A few lines of Stateless IaC are enough to compare processor architectures, validate regional portability and surface nuanced cost tradeoffs that are otherwise difficult to uncover. More importantly, they allow infrastructure and cost to be reasoned about using the same mental model.
This shift from billing-centric to infrastructure-centric cost analysis is fundamental. It enables engineers to make architectural decisions based on real data, grounded directly in the systems they build and operate.
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