Cloud-Native Test Scaling Across GCP and Azure

Playwright has quickly become the gold standard for modern end-to-end testing. It’s fast, deterministic, and designed for real-world browser automation.

But while most teams focus on writing Playwright tests, far fewer think deeply about how those tests should be executed at scale.

That’s where Agent Mantis is fundamentally different.

Agent Mantis is not just a test runner. It is a distributed execution platform that treats Playwright as an execution engine — and deploys it intelligently across multiple cloud providers to maximise scale, speed, and cost efficiency.

The Problem With Traditional Playwright Execution

Most Playwright setups fall into one of three buckets:

  • Local execution on developer machines
  • CI-only execution (GitHub Actions, GitLab CI, etc.)
  • Single-cloud, fixed infrastructure runners

These approaches work — until they don’t.

As test suites grow, teams run into:

  • Long execution times
  • Resource contention
  • Browser instability under load
  • Hard limits on parallelism
  • Rising CI costs

Scaling Playwright is not about more runners. It’s about architectural separation of concerns.

Agent Mantis: Execution, Not Just Automation

Agent Mantis was designed from the ground up to separate:

  • Test definition
  • Test orchestration
  • Test execution
  • Infrastructure scaling

This allows Playwright to run where it performs best — not where it happens to be installed.

Dual-Cloud Playwright Execution Model

Agent Mantis uses a dual-cloud execution strategy:

🔹 Google Cloud Platform (GCP) — Elastic Scale Engine

Playwright tests can be executed as ephemeral, horizontally-scaling workloads on Google Cloud Platform.

This enables:

  • Massive parallel execution
  • On-demand scaling for large test runs
  • Zero idle infrastructure
  • Cost-efficient burst workloads

Each test (or test group) can run in isolation, spin up instantly, execute, and disappear.

Perfect for:

  • Regression suites
  • Large test matrices
  • Nightly or pre-release runs

🔹 Azure App Hosting — Native Playwright Workspace Integration

Agent Mantis also integrates with the Playwright workspace environment hosted on Azure App Hosting Services, leveraging Microsoft’s deep support for Playwright’s execution model.

Microsoft Azure provides:

  • Stable browser execution environments
  • Tight alignment with Playwright’s official tooling
  • Predictable performance for long-running or stateful tests

This is ideal for:

  • Interactive debugging
  • Authoring and validating tests
  • Smaller, iterative test runs
  • Teams already embedded in the Azure ecosystem

Why This Matters: Right Tool, Right Cloud

Most platforms lock you into one execution model.

Agent Mantis lets you choose how and where Playwright runs, based on intent:

Use Case

Execution Target

Massive parallel regression

GCP elastic workloads

Developer authoring & debugging

Azure Playwright workspace

CI/CD validation

Hybrid (Azure + GCP)

Cost-optimised bursts

GCP

Stable long-running flows

Azure

This flexibility is architectural, not configurable duct tape.

Execution as a First-Class Concept

Agent Mantis treats Playwright execution as:

  • Stateless
  • Repeatable
  • Observable
  • Cloud-native

That means:

  • Tests are portable
  • Failures are reproducible
  • Infrastructure does not dictate test design
  • Scaling is automatic, not manual

You don’t “run Playwright”. You dispatch execution.

Built for the Future of QA

As QA shifts toward:

  • AI-assisted test generation
  • On-demand validation
  • Continuous verification
  • Multi-environment testing

…execution becomes the bottleneck.

Agent Mantis removes that bottleneck by letting Playwright operate at cloud scale, across cloud boundaries, without rewriting a single test.

Final Thought

Playwright is an exceptional testing framework.

Agent Mantis turns it into a distributed execution engine.

Not locked to one CI tool. Not locked to one cloud. Not limited by static infrastructure.

Just fast, scalable, cloud-native test execution — the way modern QA should work.