Oxylabs is a solid proxy and scraping platform. But if you've been using it for a while, you've probably noticed the same things I have: the pricing gets steep fast, the API surface is broad but sometimes confusing, and for certain developer workflows (AI pipelines, structured extraction, RAG), there are tools that fit better out of the box. I spent time testing four alternatives across real scraping tasks — pulling product pages, crawling documentation sites, extracting structured data into JSON. Here's what I found, with honest trade-offs for each. 1. NeuroAPI — Best for developers building AI-powered data pipelines NeuroAPI is a web data platform designed from the ground up for developers working with AI agents, RAG pipelines, and structured extraction workflows. It's not just a scraping API with a proxy layer bolted on — it combines scraping, crawling, mapping, search, extraction, screenshots, and browser interaction into a single unified API. Where NeuroAPI stands apart is the /extract endpoint. You pass a URL and a JSON schema, and it returns structured data directly. No writing CSS selectors, no parsing HTML yourself, no maintaining selectors when a site redesigns. For teams feeding scraped content into LLM pipelines, this is a significant time-saver. It also has a dedicated MCP server, which means you can integrate it into agent-based workflows natively — something Oxylabs doesn't offer. Here's what a basic scrape looks like: curl -X POST https://neuroapi.me/api/v1/scrape \ -H "Authorization: Bearer $NEUROAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com", "format": "markdown"}' And structured extraction is just one more field: curl -X POST https://neuroapi.me/api/v1/extract \ -H "Authorization: Bearer $NEUROAPI_KEY" \ -H "Content-Type: application/json" \ -d '{ "url": "https://news.ycombinator.com", "schema": { "type": "object", "properties": { "stories": { "type": "array", "items": { "type": "object", "properties": { "title": {"type": "string"}, "url": {"type": "string"}, "points": {"type": "integer"} } } } } } }' Best for: Developer teams building AI agents, RAG systems, or data products that need structured web data without maintaining scraping infrastructure. Trade-offs: It's a newer platform compared to Oxylabs, so the proxy pool breadth (residential, ISP, mobile) isn't as extensive. If you need raw proxy access for custom scraping stacks, Oxylabs has more variety. If you need a scraping platform, NeuroAPI is the sharper tool. 2. Firecrawl — Best for open-source-first teams Firecrawl gained traction in the developer community by open-sourcing its core scraper and pairing it with a hosted API. It converts web pages to clean markdown, which is exactly what you want for LLM ingestion. The API is clean and well-documented. You can scrape single pages, crawl entire sites, and extract structured data with a schema-driven approach similar to NeuroAPI. It also supports batch operations and has SDKs for Python and Node.js. import requests response = requests.post( "https://api.firecrawl.dev/v1/scrape", headers={"Authorization": "Bearer YOUR_KEY"}, json={"url": "https://example.com", "formats": ["markdown"]} ) print(response.json()["data"]["markdown"][:500]) Best for: Teams that want the option to self-host their scraping infrastructure or contribute to the tool itself. Trade-offs: The self-hosted version is functional but requires more maintenance than you'd expect. The hosted version is convenient but pricing can add up at scale. Browser-based rendering (JavaScript-heavy sites) works but can be slower than NeuroAPI or Oxylabs's dedicated browser infrastructure. The structured extraction feature exists but I've had mixed results with complex schemas — nested objects sometimes return incomplete data. 3. Bright Data — Best for enterprise proxy needs Bright Data (formerly Luminati) is the closest direct competitor to Oxylabs in terms of raw proxy infrastructure. They offer residential, ISP, datacenter, and mobile proxies across a massive IP pool. If your workflow depends on IP diversity and geo-targeting at scale, Bright Data is the main alternative. They've expanded beyond proxies with a scraping browser, a datasets marketplace, and a Web Scraper IDE. The scraping browser is useful — it handles fingerprinting, CAPTCHA solving, and retries automatically. Best for: Enterprise teams that need extensive proxy coverage, geo-targeting, and compliance tooling (KYC, data collection policies). Trade-offs: Complexity and cost. Bright Data's dashboard has a learning curve, and pricing is not transparent — you often need to talk to sales to get a real number. For developers who just want an API to call, the proxy-centric model means more assembly required. You're buying raw infrastructure, not a developer experience. If you're building a product on top of scraping, expect to build significant middleware yourself. 4. ScraperAPI — Best for simple, high-volume scraping ScraperAPI takes a different approach: it's a proxy aggregator with a simple API layer. You pass a URL, it handles proxies, browsers, and CAPTCHAs behind the scenes. It's not trying to be a platform — it's a single-purpose tool that does one thing well. The API is about as simple as it gets: curl "http://api.scraperapi.com?api_key=YOUR_KEY&url=https://example.com" You can also pass rendering parameters, geotargeting, and session flags as query parameters. It supports both synchronous requests and asynchronous batch processing for higher volumes. Best for: Teams that already have their own parsing and data pipeline and just need reliable proxy rotation and rendering. Trade-offs: ScraperAPI returns raw HTML...