TL;DR
- Context.dev returns LLM-ready Markdown and schema-validated JSON directly, so your agent skips the parsing layer Apify's raw HTML demands.
- Apify wins on breadth, with thousands of pre-built Actors covering Instagram, Google Maps, Amazon, and other known domains.
- Context.dev runs as one REST API with no proxy fleet or anti-bot logic to manage; Apify bills compute units plus separate proxy GBs.
- Context.dev exposes a one-line MCP surface for agentic workflows, while Apify's hosted MCP server adds OAuth access for Claude and Cursor.
Quick Verdict
Pick Context.dev when you are building AI agent pipelines or LLM applications and want clean structured output without managing infrastructure. Its single REST API returns schema-validated JSON or Markdown, exposes an MCP surface for agents, and carries no proxy or browser fleet to run. Pick Apify when your work spans many known domains with stable schemas, such as scraping social platforms, maps, and marketplaces at volume. The Actor ecosystem handles proxy rotation and CAPTCHA bypass per site, which earns its complexity once you are extracting from dozens of established sources rather than feeding tokens into a model.
Feature Comparison
The two platforms differ most on how they deliver data and what you manage to get it.
| Dimension | Context.dev | Apify |
|---|---|---|
| API simplicity | Single REST API, one call per task | Actor selection per site, then run config |
| LLM/AI-native output | Clean Markdown or schema-validated JSON | Raw HTML or structured data, parsing often needed |
| MCP support | One-line setup, agent self-integrates | Hosted MCP server with OAuth access |
| Infrastructure required | None, fully managed | Managed platform, or self-host Crawlee |
| Pricing model | Credits, ~70% cache-served, no charge on failure | $0.30/compute unit plus $7–8/GB proxies |
| Best for | LLM pipelines and agentic workflows | Marketplace breadth across known domains |
Context.dev wins on output and simplicity for AI work. Apify wins when you need maintained scrapers across many specific sites.
Output Format: The Make-or-Break Difference for LLM Pipelines
Output format decides whether a scraping tool fits an LLM pipeline, not how many sites it can reach. Context.dev returns schema-validated JSON or clean Markdown directly from /web/extract and /v1/scrape/markdown, so an agent consumes the result the moment the call returns. Apify Actors hand back raw HTML or page dumps that need a parsing layer before any model can read them.
That parsing layer carries real cost. Raw HTML stuffs an LLM context window with navigation, scripts, and markup the model has to read and pay for, which inflates token spend on every call and leaves more room for hallucinated extraction. Clean Markdown and typed JSON strip that noise out before the model sees it.
The pipeline complexity compounds. With Apify you write and maintain DOM selectors or regex to turn HTML into structured fields, and that code breaks when a target page changes. Context.dev accepts a JSON Schema or a Zod schema via .toJSONSchema() and returns typed data matching the shape you defined, so you skip the parsing step entirely.
API Simplicity vs. Actor Ecosystem
Context.dev resolves a scrape-and-extract job in a single REST call. You point the request at a URL, pass a JSON Schema or a Zod schema converted with .toJSONSchema(), and the /web/extract endpoint returns typed JSON matching that shape. JS rendering, proxy rotation, and anti-bot handling happen behind the call, so there are no selectors to write and no DOM to parse.
POST /web/extract
{ "url": "https://example.com/product",
"schema": { ...your JSON Schema... } }
Apify routes the same job through its Actor Store. You search 41,800+ pre-built scrapers, pick one that targets your platform, read its input schema, configure run settings and proxy options, then call it and pull results from a dataset. Each Actor already handles CAPTCHA bypass and proxy rotation, but you own the selection step and the variance that comes with it. Community Actor quality ranges widely, and some sit abandoned or silently broken.
That selection overhead pays off when your targets are known domains with stable schemas. A maintained Actor for Instagram, Google Maps, or Amazon tracks DOM changes for you, which beats writing your own extractor for high-volume multi-site work. When you scrape unpredictable pages or feed an LLM directly, the single call wins. You define the output shape once and skip the search through a marketplace entirely.
MCP Integration and Agentic Workflows
Both platforms expose MCP so an AI agent can call them directly, but their setup paths differ. Context.dev offers a one-line MCP setup through docs.context.dev/agent-quickstart. An agent can read context.dev/auth.md, sign up, grab an API key, and integrate itself with no human in the loop. Apify hosts an MCP server at mcp.apify.com with OAuth access, so an agent in Claude or Cursor can search Actors, fetch their details, and call them by URL.
MCP carries a real limit for production pipelines, though. As Tinybird notes, current MCP-driven agents struggle with pagination and bulk data pulls, so an agent may not page through every result and can return incomplete data. For high-throughput, deterministic extraction, a direct REST call remains the safer choice with either platform.
Infrastructure and Pricing
Apify bills two meters at once, and the second one surprises teams. Compute units run $0.30 each, and residential proxies cost $7 to $8 per gigabyte on top of that, charged separately from the $29 starter plan. Unused prepaid credits do not roll over, so a quiet month forfeits whatever you paid for. Predicting a monthly bill means modeling compute units, RAM, concurrent runs, and proxy bandwidth before you write a single scraper.
Context.dev charges per request and absorbs the parts you would otherwise pay for. Failed or blocked requests cost nothing, and roughly 70% of requests serve from cache, so repeat reads against the same pages stay cheap. The free tier on a work email grants 500 API credits, 50 brand retrievals, and 10,000 Logo Link requests at 30 requests per minute. Apify's free tier offers $5 in credits to start. For AI pipelines that retry and re-fetch often, the no-charge-on-failure policy and cache pricing remove the two costs that make Apify's bill hard to forecast.
Where Apify Still Wins
Apify earns its place for teams scraping known platforms at scale. Its Actor Store covers Facebook, Google Maps, Instagram, and major e-commerce sites, with each Actor handling proxy rotation, JS rendering, and CAPTCHA bypass without configuration. When a maintained Actor already exists for your target domain, you skip the work of building and tracking selectors yourself.
Apify also holds SOC 2 Type II certification along with GDPR and CCPA compliance, backed by a 99.95% uptime SLA. Compliance-sensitive buyers can clear procurement faster as a result. For teams already running LangChain or LlamaIndex, Apify's native connectors plug straight into existing pipelines, so you adopt it without rewriting your ingestion code.
Who Should Use Each
Choose Context.dev if you:
- Feed scraped data directly into LLMs and want schema-validated JSON or clean Markdown without a parsing layer.
- Build agentic workflows and want one-line MCP setup.
- Want to replace an internal crawler without managing proxies, headless browsers, or anti-bot logic.
- Need to deploy fast with minimal engineering across one REST API.
Choose Apify if you:
- Scrape known domains like Instagram, Google Maps, or Amazon where a maintained Actor already exists.
- Run high-volume, multi-site workflows that benefit from marketplace breadth.
- Require SOC 2 Type II, GDPR, and CCPA compliance with a 99.95% uptime SLA.
- Already build on LangChain or LlamaIndex and want native connectors into that stack.
Conclusion
Context.dev wins for AI agent pipelines because it returns schema-validated JSON and clean Markdown from one API with no infrastructure to run. Apify earns its place when you need maintained scrapers for known marketplaces and social platforms at high volume. If your output feeds an LLM, start with Context.dev's free tier and wire up agentic workflows through the agent quickstart.
FAQs
Is Context.dev a drop-in Apify alternative? Context.dev replaces Apify for AI pipelines that need clean LLM-ready output from one REST call. It does not match Apify's pre-built Actors for specific marketplaces, so swap in Context.dev when your workload is structured extraction for agents and LLMs.
Does Apify support MCP? Yes. Apify runs a hosted MCP server at mcp.apify.com with OAuth, letting agents search and call Actors. Context.dev exposes its own MCP surface with one-line setup, which suits agents that self-integrate and grab an API key on their own.
How does pricing compare at scale? Apify bills compute units at $0.30/CU plus $7 to $8 per GB for residential proxies, and unused credits do not roll over. Context.dev serves about 70% of requests from cache and charges nothing for failed or blocked requests, which lowers cost on repeated extraction.
Is Context.dev SOC 2 certified? Context.dev holds SOC 2 Type 1 and has its Type 2 observation period underway. That benefits teams handling brand and company data who need a documented compliance posture for AI workloads.