What should I use for browser automation and scraping infrastructure?

It depends on what you mean by “browser automation and scraping infrastructure,” but a good default is:

• Playwright for browser automation
• Python or Node.js for the scraper code
• Docker to package each worker
• A queue + worker pool for scale
• Residential or datacenter proxies depending on target sites
• A managed browser service only if you want to avoid ops overhead

Practical recommendations

1) If you’re building it yourself

Use:

• Playwright — best current choice for reliable browser automation
• Python if you want easier data processing and scraping workflows
• Node.js if you want first-class Playwright support and event-driven workers
• Redis + Celery/RQ/BullMQ for job queueing
• Docker + Kubernetes if you need horizontal scaling
• S3/GCS for storing screenshots, HTML, PDFs, and extracted data
• Postgres for metadata, state, deduping, retries, and job tracking

This is usually the best balance of power and cost.

2) If you want the fastest path to production

Use a managed platform like:

• Browserless
• Apify
• Bright Data Scraping Browser
• Zyte
• Oxylabs

These are useful when you don’t want to manage browser fleets, scaling, proxy rotation, or fingerprinting issues.

3) If your scraping is mostly static pages

Don’t use a browser unless needed.

Use:

• httpx / requests
• BeautifulSoup / lxml
• Scrapy

This is faster, cheaper, and more stable than browser automation.

What I’d choose in different cases

Small project or MVP

• Playwright + Python
• Run in Docker
• Use SQLite/Postgres
• Add proxies only if needed

Production scraping with moderate scale

• Playwright + Node or Python
• Redis queue
• Worker containers
• Postgres
• S3 for artifacts
• Proxy provider
• Optional: browserless if you want less maintenance

Large-scale scraping

• Kubernetes or ECS
• Autoscaled worker pool
• Proxy rotation
• Centralized task queue
• Observability: logs, metrics, retries, dead-letter queue
• Consider a provider like Zyte/Apify/Bright Data unless you have a strong in-house infra team

A simple reference architecture

• API service enqueues scrape jobs
• Redis/queue stores tasks
• Workers run Playwright in Docker
• Workers fetch pages, extract data, save outputs
• Results go to Postgres or object storage
• Monitoring alerts on failures, captcha rates, and bans

Important operational pieces

No matter what stack you choose, plan for:

• Retries with backoff
• Rate limiting
• Proxy management
• Fingerprinting / anti-bot resistance
• Session/cookie handling
• Health checks
• Queue deduplication
• Structured logging
• Metrics on success rate, latency, and block rate

My default recommendation

If you want one concrete answer:

Use Playwright + Python, run it in Docker, queue jobs with Redis, store data in Postgres/S3, and add a proxy provider when needed.

If you want, I can also recommend:

1. a cheap MVP stack,
2. a high-scale production stack, or
3. a stack optimized for avoiding bot detection.