Inventory
We map your infrastructure estate — Terraform modules, CloudFormation stacks, Kubernetes manifests, and the environments they provision — so we know exactly which infrastructure changes carry release risk.
Appsierra runs cloud infrastructure testing through AI-accelerated, expert-supervised QA pods that validate the infrastructure itself — not just the app on top of it. We test Terraform, CloudFormation and Kubernetes manifests, catch configuration drift, prove environment parity, and gate every infrastructure change in your CI/CD pipeline, so DevOps and platform teams ship changes without downtime surprises.
Infrastructure testing runs continuously alongside your pipeline, though it can also be a one-time engagement after a cloud migration or a large platform re-architecture.
We map your infrastructure estate — Terraform modules, CloudFormation stacks, Kubernetes manifests, and the environments they provision — so we know exactly which infrastructure changes carry release risk.
We define what "correct" means for your estate: assertions per module, policy-as-code rules, environment-parity baselines, and the drift thresholds worth alerting on.
We build the test layer — static analysis, plan-time checks, ephemeral environments provisioned from your own IaC and asserted against, plus failover rehearsals and scheduled drift scans.
We wire the suite into your pipeline so a bad infrastructure change fails a CI job instead of your production environment, and report on drift, parity, and coverage as you go.
Application tests tell you your code works. They tell you nothing about whether the environment it lands on was provisioned correctly, whether staging still matches production, or whether last month's emergency console edit has quietly drifted your cluster away from what your repository claims. Most outages attributed to "a bad deploy" are really a bad environment.
That is why the object under test on this page is the infrastructure code and configuration itself — not the application running on it. It is a deliberately different discipline from automation testing services, which automate tests of your application, and from performance testing services, which measure how your app behaves under load. It is also distinct from cloud infrastructure management: that team builds and runs your cloud estate, while this pod independently tests it before and after it runs. By treating infrastructure as a testable artefact, you can:
Configuration drift between what your IaC declares and what is actually running stays invisible until an incident. Scheduled scans surface it while it is still cheap to fix.
Every Terraform or CloudFormation change is applied to a throwaway environment and asserted there before it ever touches staging or production.
Dev, staging, and production diverge quietly. Parity checks prove that the environment you tested against is the environment you actually shipped to.
A broken infrastructure change should fail a CI job during working hours, not page your on-call engineer at 3am after it has already rolled out.
Static analysis, plan-diff review, and Terratest-style assertions run against really provisioned resources — catching bad module inputs, missing tags, and unintended resource replacement before an apply reaches your account.
Schema validation, resource limits, health probes, rollout strategy, and namespace policy — so a manifest merge cannot quietly take a cluster down on the next deploy.
Policy-as-code checks that configuration matches your own standards: encryption at rest, network exposure, IAM scope. This is reliability and correctness validation, not penetration testing — for security assessment work, see our enterprise IT security solutions.
We rehearse node loss, availability-zone failover, and restore paths against the infrastructure you actually run, so your disaster-recovery runbook becomes a tested artefact rather than a hopeful document.
Static analysis and policy-as-code run on every pull request that touches infrastructure code. A violation fails the check, so review time goes to design decisions instead of typos and missing tags.
The pipeline provisions a throwaway environment from your IaC, asserts the resources came up exactly as declared, then destroys it. Our DevOps consulting engineers wire this into the pipeline you already run.
Most drift arrives after the merge — from console edits, autoscaling, and manual hotfixes. A recurring scan diffs live state against declared state and reports exactly what moved, and when.
Infrastructure testing that de-risks every cloud change for global SaaS and enterprise platform teams, pipeline run after pipeline run.
Pods drawn from our own pre-vetted talent network and evaluation platform start delivering in days, not weeks.
We agree measurable targets for IaC coverage, drift detection, and environment parity up front, not just billed hours.
AI-augmented engineers move faster while senior engineers review every result before it reaches you.
ISO 27001 and CMMI Level 3 aligned, SOC 2-ready, and NDA-first, so your code and data stay protected.
Direct access to technical leadership, not a faceless bench or a marketplace of strangers.
1250+ engineers deployed, 300+ projects delivered, 60+ global brands, and a 4.8/5 rating.
Cloud infrastructure testing validates the infrastructure itself — the code, configuration, and provisioned resources that your application runs on — rather than the application. It covers infrastructure as code correctness, environment parity, configuration drift, failover behaviour, and whether a change is safe to apply. The object under test is the infrastructure, not the app.
IaC testing checks that infrastructure definitions written in tools like Terraform, CloudFormation, or Kubernetes manifests produce the resources they claim to. It typically layers static analysis, plan-time review, and assertions against a really provisioned environment. The goal is to catch a bad module input or an unintended resource replacement before an apply reaches a live account.
Terraform is tested in layers: static analysis and policy-as-code on the source, a review of the plan diff for destructive changes, then assertions against resources provisioned in a throwaway environment using a framework such as Terratest. Kubernetes manifests are validated for schema correctness, resource limits, probes, rollout strategy, and namespace policy before they merge.
Configuration drift is the gap that opens between what your infrastructure code declares and what is actually running, usually caused by console edits, autoscaling, or manual hotfixes during an incident. It is caught with recurring scans that diff live state against declared state and report what moved. Drift is invisible between scans, which is why cadence matters more than tooling.
It runs as gates. Pre-merge checks fail a pull request that violates policy, ephemeral environment tests provision and assert the change before promotion, and scheduled drift scans run independently of merges. The point of gating is that a bad infrastructure change fails a CI job rather than a production environment.
No. They are different disciplines with different goals. Infrastructure testing is reliability and correctness validation: does this infrastructure come up as declared, stay in sync, and survive failure? Penetration testing is a security assessment that actively attempts to exploit weaknesses. A configuration policy check can confirm encryption is enabled, but it is not a substitute for a security audit.
Tell us what you're building, testing or scaling — a senior engineer sends a short, honest read and a low-risk way to start.
A senior engineer will review your note and reach out shortly with an honest read and a low-risk way to start.
It's time to stop finding out about broken environments from your on-call rota. Appsierra validates your infrastructure as code, catches drift before it becomes an incident, and gates every change in your pipeline. Contact us to begin your continuous infrastructure testing journey today.
Vetted pods, productive in 7 days.
Tell us what you need to build, test, scale or hire for — QA, software, AI/LLM engineering or a full pod. A senior engineer reviews it and sends a short, honest read, plus a low-risk way to start.
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