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Why Agenix Playwright Grid

Subtitle: When to choose a self-hosted Playwright Grid over direct CI runs or BrowserStack

Date: 2025-08-25 Audience: QA/Dev Leads, SRE, Engineering Managers

TL;DR

  • Use Grid when you need consistent, scalable, cost‑efficient, and observable Playwright execution across many repos/teams.
  • Compared to running Playwright directly in CI:
  • Centralizes capacity and reduces flakiness by pre‑warming browsers and reusing pools.
  • Gives you labels (App:Browser:Env[:Region[:OS…]]) to route the right capacity automatically.
  • Adds first‑class observability (Prometheus/Grafana) and a live Dashboard with protocol/API logs.
  • Compared to BrowserStack/SaaS clouds:
  • Lowers cost at sustained scale, improves data control/compliance, and removes vendor queueing.
  • Provides LAN‑grade latency to your services and unlimited customization of browser flags/versions.

The problem with “Playwright directly in CI”

  • Cold starts and environment drift
  • Installing browsers/OS deps on every job wastes minutes and increases flake risk.
  • Multiple pipelines pinning different Playwright versions → inconsistent results across repos.
  • Fragmented capacity and under‑utilization
  • Each project over‑provisions CI runners to hit SLAs; no global pooling.
  • Peaks in one repo/star branch cause queuing while other runners idle.
  • Limited observability and forensics
  • Logs and traces distributed across countless jobs; no cross‑suite view.
  • Hard to correlate protocol‑level events, API logs, screenshots/videos, and test metadata.
  • Network and security
  • Egress patterns vary per job; hard to apply consistent network policies or IP allowlists.
  • Secrets handling is duplicated across repos and pipelines.

How the Grid solves this

  • Pre‑warmed capacity
  • Workers keep Playwright sidecars hot; Hub borrows/returns sessions in milliseconds.
  • Label‑driven routing
  • Use ordered keys App:Browser:Env[:Region[:OS…]] to match exactly, fallback by trailing segments, or expand prefixes.
  • Guarantees the right browser/channel/OS for each app/env without bespoke pipeline logic.
  • Centralized observability
  • Built‑in Prometheus metrics and Grafana dashboards.
  • Dashboard shows runs with mirrored protocol commands and runner‑forwarded API logs.
  • Simple, secure APIs
  • Minimal endpoints for borrow/return; secrets for runners and nodes; Redis‑backed state.
  • Elastic scaling
  • Add/remove workers per pool without touching test repos or CI definitions.

Why not just use BrowserStack (or similar)?

Pros of SaaS clouds - Zero infra to start; broad browser/device catalog; shared responsibility for upkeep.

Cons at scale - Cost and concurrency - Minute‑based billing and plan caps lead to queues or expensive upgrades under sustained load. - Data, compliance, and performance - Test data and traffic traverse vendor infra; IP allowlists and data residency can be limiting. - Cross‑DC latency to your services can slow tests and increase flakiness. - Limited customization - Browser flags, experimental protocols, pinned Playwright versions, and OS tuning are constrained by the vendor’s images. - Lock‑in - Switching vendors or bringing execution in‑house later can be costly.

Grid advantages - Predictable cost curve (own the hardware or autoscale in your cloud). - LAN‑close to your services → lower latency and more stable tests. - Full control over images, flags, and Playwright versions; consistent across teams. - First‑class integration with your observability stack.

Cost and capacity: simple model

  • Direct CI
  • Install browsers per run: 1–3 min overhead × N jobs/day.
  • Parallelism limited by per‑repo runner quotas; idle capacity elsewhere can’t help.
  • BrowserStack
  • Concurrency is plan‑bound; bursts cause queues.
  • Per‑minute pricing scales linearly with test time.
  • Grid (self‑hosted)
  • Pre‑warm overhead amortized; near‑zero setup time per borrow.
  • Global pool across all repos; unused capacity is reused instantly.
  • Scale horizontally: add workers labeled to the hot pools.

Tip: Start with a small node pool (e.g., 2–3 workers per hot label), measure utilization in Grafana, grow only where needed.

Security and compliance

  • Stable egress via workers; put them in the same VPC/VNet as your services or behind a controlled NAT.
  • Centralized secrets (HUB_RUNNER_SECRET, HUB_NODE_SECRET) vs spreading secrets across many CI pipelines.
  • Data locality: you choose the region/cloud; keep traffic inside your boundaries.

Developer experience

  • Consistent local vs CI behavior: the same borrow/return APIs work locally and in pipelines.
  • Faster feedback via hot pools and fewer flakes from cold installs.
  • Dashboard helps triage quickly with protocol/API logs, screenshots, and test attribution.

When to prefer alternatives

  • Direct CI may be enough if:
  • You have a small suite, few contributors, and no strong parallelism/latency needs.
  • Infrequent runs and no desire to maintain any infra.
  • BrowserStack/SaaS may be better if:
  • You need wide real‑device coverage or legacy OS/browser variants the grid doesn’t target.
  • You want a purely managed service with no ops surface area.

Adoption path (low‑risk)

1) Compose the stack locally: docker compose up --build. 2) Point a subset of suites to the Grid using Agenix.PlaywrightGrid.HubClient and labels. 3) Compare runtime and flake rates vs baseline. 4) Incrementally move suites; add workers only for hot labels. 5) Wire Prometheus/Grafana dashboards into your observability and set SLOs.

FAQ

  • How do we keep Playwright versions consistent?
  • Pin via Dockerfiles and PLAYWRIGHT_VERSION env; workers report the version to Dashboard.
  • Can we isolate teams or apps?
  • Yes, by label namespaces (e.g., AppA:Chromium:Staging) and per‑pool worker assignments.
  • What about spikes during release time?
  • Temporarily add workers or adjust pool counts; no changes in test repos.
  • Do we lose any Playwright features?
  • No. Tests connect to the worker‑proxied WebSocket exposed to the client. You can still use channels/flags.

Appendix: Feature matrix snapshot

  • Routing
  • Exact, trailing fallback, prefix expansion, optional wildcards.
  • Observability
  • Prometheus metrics; Grafana dashboards; protocol/API log mirroring.
  • Security
  • Shared‑secret auth; stable egress; optional per‑pool isolation.
  • Operations
  • Redis‑backed Hub state; horizontal scaling; Docker‑first.

Links - Root README: ../README.md - Test client usage: ./TestClient-Usage.md - Playwright .NET API logging: ./PlaywrightDotNet-pw-api.md