What is Apify?
Quick answer
Apify is a cloud platform for web scraping and automation. It provides 30,000+ ready-to-use scrapers (Actors), managed proxy infrastructure, and a complete data pipeline platform. You pick a Store Actor or build with Crawlee, run jobs on Apify’s cloud, and export structured rows to datasets, APIs, webhooks, or integrations, all without provisioning servers or proxy pools yourself.
Apify is built for teams that need repeatable extraction: same inputs, monitored runs, stored output, and hooks into n8n, Make, Zapier, LangChain, or your own stack. If your question is “how do we get structured web data to production without owning scraping infra?”, Apify is the shortest path for many shops, especially when a maintained Store Actor already exists for your target.
Platform overview
| Layer | What you get |
|---|---|
| Runtime | Containerized Actors execute on Apify’s cloud with RAM limits per plan. |
| Marketplace | Apify Store hosts community and official scrapers for major sites and workflows. |
| Data plane | Datasets (tabular output), key-value stores (files, screenshots), request queues (crawl state). |
| Connectivity | API, webhooks, schedules, and MCP for AI agents. |
How a typical run works: choose an Actor → pass structured input (URLs, keywords, limits) → Apify bills compute units (and sometimes per-result Actor fees) → you read rows from a dataset or push them downstream.
Key features (summary)
| Feature | Why it matters |
|---|---|
| Store Actors | Ship faster when someone already solved the target site’s DOM, pagination, and rate limits. |
| Crawlee + SDK | Same crawling primitives many Store tools use (queueing, retries, browser automation) when you need custom logic. |
| Proxies & anti-blocking | Rotation and fingerprinting are first-class; you are not glueing a separate proxy vendor into every job. |
| Scheduling & monitoring | Cron-style schedules, run history, and alerts fit “always-on” pipelines, not one-off scripts. |
| Integrations | 30+ integrations (Sheets, Slack, vector DBs, automation tools). |
| MCP server | Lets Claude, Cursor, and other MCP clients invoke Actors with OAuth, useful for agent workflows. |
Pricing overview (2026)
| Plan | Monthly | Monthly credits | Typical fit |
|---|---|---|---|
| Free | $0 | $5 | Prototype runs, small recurring jobs, learning the platform |
| Starter | $29 | $29 | Individuals and small teams with steady workloads |
| Scale | $199 | $199 | Higher concurrency and memory for scheduled pipelines |
| Business | $999 | $999 | Large teams, heavier automation, lower per-CU rates |
| Enterprise | Custom | Custom | SLAs, security reviews, bespoke limits |
Credits pay for compute units (GB × hours) and may combine with Actor-level per-event pricing on some Store tools. Always confirm on the Actor’s Pricing tab and the official pricing page.
See full Apify pricing guide → · Free plan details →
Use cases overview
| Use case | What teams usually automate | Deep dive |
|---|---|---|
| Lead generation | Maps, directories, B2B lists → CRM | Lead generation |
| E-commerce | Price and availability monitoring | Price monitoring |
| Market intel | News, SERPs, competitor sites | Market intelligence |
| AI / RAG | Crawl → clean text/Markdown → embeddings | Data for AI & RAG |
| Social analytics | Public profiles, posts, engagement signals | Social media analytics |
How Apify compares to alternatives
| Apify | Bright Data | Firecrawl | Self-hosted (e.g. Crawlee on your VPS/k8s) | |
|---|---|---|---|---|
| Primary product | Scraping platform: Actors, runtime, storage, scheduling | Proxy + scraped datasets / collector products | Crawl & extract for LLM/RAG (API, MCP) | Libraries + your infra and ops |
| Prebuilt coverage | 30,000+ Store Actors | Managed datasets & scraper marketplace (vendor-specific) | APIs tuned for crawl → Markdown/JSON | None; you implement everything |
| Best when | You want end-to-end jobs, monitoring, and Store coverage | You need enterprise proxy scale or vendor-managed collections | You need LLM-ready pages fast with minimal glue | You need full control and accept ops burden |
| Pricing model | Plan credits + CUs (+ optional per-result Actor fees) | Volume-based data & proxy SKUs | API credits / plans | Servers + proxies + engineering time |
| Ops burden | Low (managed cloud) | Low–medium (integrate their stack) | Low (hosted API) | High (you own reliability) |
Practical rule: pick Apify when Store Actors or Apify-hosted Crawlee cover your targets and you value integrated storage and schedules. Pick Bright Data when proxy/data-network scale is the bottleneck. Pick Firecrawl when the main deliverable is clean page text/Markdown for models. Self-host when compliance or unit economics force you to own the stack.
Compare more Apify alternatives →
Core concepts (official terminology)
Actors
Actors are containerized programs: structured input in, structured output out. Run them from the console, REST API, or MCP.
Runs, tasks, and schedules
| Term | Meaning |
|---|---|
| Run | One execution of an Actor with a given input |
| Task | Saved input + Actor pairing you can re-run or schedule |
| Schedule | Cron-style trigger for tasks or Actors |
Storage
- Datasets: rows (JSON, CSV, JSONL, Excel)
- Key-value stores: arbitrary files and blobs
- Request queues: frontier URLs and deduplication for crawls
Ready-made vs custom Actors
| Path | Choose when |
|---|---|
| Store Actor | A maintained Actor exists, output schema fits, time-to-value beats custom build |
| Custom Actor | You need strict schemas, proprietary enrichment, or no Store match |
Many teams hybridize: Store Actors for common targets, custom Crawlee Actors for long-tail or regulated flows.
How to get started
- Create a free account → (no card; $5 monthly credits).
- Open the Apify Store, filter by your target (e.g. Google Maps, Amazon).
- Run a small sample (low limits); inspect the dataset schema.
- Save a task, add a schedule or webhook, wire Sheets or your API consumer.
- If you outgrow Store output, follow Apify for Developers and ship a custom Actor.
Start scraping for free → · Open the Apify Store →
Sources
- Apify docs: Actors
- Apify docs: Storage
- Apify docs: Schedules
- Apify docs: Integrations
- Apify docs: Monitoring
- Apify MCP
Apify is a cloud platform for web scraping and automation that runs prebuilt or custom Actors, provides managed proxies and storage, and ships data to your apps via API, webhooks, and integrations.
An Actor is a packaged scraper or automation job (often a Docker container) with defined input and output. Store Actors are maintained by Apify or the community; you can also build private Actors with Crawlee and the Apify SDK.
Yes. The Free plan includes $5 in monthly platform credits (no credit card). That is enough for many small tests and light recurring jobs; credits reset each billing cycle. See the free plan guide for realistic run examples.
You pay for a plan that includes monthly credits, then spend credits on compute units (GB RAM × hours). Some Store Actors add per-result or per-event fees. Always read the Actor Pricing tab and the platform pricing page before scaling.
Apify is an end-to-end scraping platform with 30,000+ Actors and integrated scheduling. Bright Data centers on proxy and enterprise data products. Firecrawl focuses on crawling and formatting content for LLMs. Self-hosting trades lowest marginal cost for the highest operational burden.
Often yes: you can run equivalent logic as custom Actors using Crawlee, or find a Store Actor that already handles the site. You still own compliance with site terms and robots.txt; Apify supplies the runtime, queues, and proxy plumbing.
Yes. The Apify MCP server exposes Actors to MCP-compatible clients, and datasets integrate with RAG and automation stacks. See the MCP documentation and the data-for-AI use case for patterns.
Common mistakes and fixes
I understand Actors conceptually but do not know what to run first.
Pick one Store Actor for your exact target site and run a small sample input.
I can run Actors but cannot automate the workflow.
Use tasks, schedules, and webhooks so runs trigger and deliver output automatically.
Data quality is inconsistent across runs.
Standardize input templates and validate output schema before downstream ingestion.



