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You can move a Scrapy project to the cloud without automatically rewriting its spiders—but “migration” can mean three different things: hosting Scrapy elsewhere, routing its requests through a managed scraping API, or wrapping it in another platform’s SDK and runtime. Choose the smallest change that solves your problem, then test a representative spider before moving scheduled production crawls.
Choose what you actually need to migrate
Scrapy separates crawl logic from several operational concerns. Your spiders define what to request and how to parse it; settings, middleware, pipelines, deployment tooling, scheduling, and storage determine how the crawl runs and where its results go. A cloud move can change one of these layers without replacing all the others.
| Path | What changes | What can stay Scrapy | Best fit |
|---|---|---|---|
| Move hosting | Deployment, scheduling, monitoring, and possibly resource management. | Spiders and much of the project configuration. | You want managed execution but do not need a new request-fetching layer. |
| Add a managed request/API layer | How Scrapy downloads pages, through an integration that handles requests. | The Scrapy runtime, spiders, scheduler, and output pipeline, subject to project compatibility. | Your current runtime works, but fetching or access to target sites is the problem. |
| Wrap Scrapy in a platform SDK/runtime | Project packaging and platform-specific lifecycle, storage, and configuration. | Scrapy spiders and crawl logic may remain, but platform integration is added. | You want a particular cloud platform’s Actors, storage, or execution model. |
These paths are not interchangeable. A hosted Scrapy service runs your crawl; an API integration changes how requests are handled; an SDK wrapper connects a project to a platform’s runtime conventions. Compare the scope you need rather than treating every option as a full framework replacement.
Can you keep your existing Scrapy spiders?
Usually, that is a reasonable starting assumption—not a guarantee that the whole project will transfer unchanged. Standard spider code is the most reusable part. Deployment scripts, custom middleware or extensions, environment variables, persistent state, export destinations, and assumptions about scheduling or shutdown can still need adjustments.
#1 Best Overall
Scrapy’s deployment documentation describes Scrapyd, an open-source server for running and monitoring spiders, and Zyte Scrapy Cloud, a hosted service. It says Scrapy Cloud is compatible with Scrapyd and can use the same scrapy.cfg configuration approach as scrapyd-deploy. Zyte documents a self-hosted deployment route using shub: install it, log in, and deploy. Its product page describes scheduling, monitoring, dashboards, and capacity controls. See the Scrapy deployment documentation and Zyte Scrapy Cloud.
Vendor statements that a deployment requires “no rewrites” should be treated as a starting point for evaluation. Confirm that your project’s own scripts, dependency pins, secrets, storage, and operational assumptions work in the target environment.
Path 1: move hosting and retain Scrapy
For a hosting-only migration, keep the Scrapy project as the unit you deploy. Start by checking your root configuration, deployment command, dependency installation, runtime settings, and where crawl output is written. If adopting a hosted option, validate its current deployment procedure and limits against your workload rather than assuming a local or Scrapyd deployment behaves identically.
- Inventory the current project. Record Python and Scrapy versions, pinned packages, custom middleware and extensions, pipelines and exporters, environment variables, secrets, persistent state, scheduler assumptions, and request volume and concurrency.
- Deploy a low-risk spider. Use the target service’s documented deployment route. For Zyte Scrapy Cloud, its product page describes a
shubflow; follow the provider’s current installation and login steps. - Compare a real run. Check item schema and counts, duplicate handling, logs, exit status, crawl duration, and schedule behavior against the existing deployment.
- Expand in small groups. Migrate spider by spider, retaining the prior deployment configuration until the new runs and downstream data delivery are verified.
Zyte’s Scrapy Cloud page, accessed 2026-09-29, lists a free Starter plan with one hour of crawl time, one concurrent crawl, and seven-day data retention. Its Professional plan is listed from $9 per unit per month with unlimited crawl time and concurrent crawls and 120-day data retention; one Scrapy Unit is listed as 1 GB RAM and one concurrent crawl. These are vendor-published terms, not an independent like-for-like cost comparison, and can change. Check the current plan page before budgeting or switching.
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An API integration can replace or augment the request/download handling layer while your spiders and Scrapy runtime continue to control crawl logic. Zyte’s scrapy-zyte-api package is an example. Zyte distinguishes Scrapy Cloud, which runs spiders, from Zyte API, which provides request-fetching capabilities; it says the API can also be used with a self-hosted runtime. That does not mean an API integration moves your scheduler, deployment, or output storage into the cloud. See the Zyte API tutorial.
The stable setup documentation is labeled version 0.34.0. It lists Python 3.10 or later, Scrapy 2.0.1 or later, and a Zyte API subscription; it describes a free trial. The scrapy-poet integration requires Scrapy 2.6 or later. Check the setup documentation for the exact requirements in effect for your project.
Basic setup for Scrapy 2.10 and later
Install the package in the project’s environment:
pip install scrapy-zyte-apiFor Scrapy 2.10 or later, the documented add-on entry is:
ADDONS = {
"scrapy_zyte_api.Addon": 500,
}The add-on enables transparent mode by default. Configure the API key as the ZYTE_API_KEY environment variable and use the integration’s secure environment configuration guidance; do not commit a live key to source control. Then run a test crawl in a fresh process and inspect the request behavior, errors, output, and logs.
Check Twisted reactor and asyncio assumptions
The setup guide warns that changing to twisted.internet.asyncioreactor.AsyncioSelectorReactor may require project changes. Imports can install Twisted’s default reactor before it can be changed, and Deferred-based code may need integration with asyncio. A reactor already installed in a process cannot be swapped for that run, so test changes in a fresh process. Audit imports and custom event-loop code before enabling the integration; if reactor initialization fails, identify what imported or installed it first and revise the initialization path before retrying.
Managed fetching may help with a specific target-site access problem, but it is not a promise that every request will succeed or that bans disappear. Run a pilot against the actual sites, request types, and failure cases that matter to you.
Path 3: wrap Scrapy in a cloud platform SDK
Apify documents a CLI flow that can convert an existing Scrapy project into an Apify Actor when it follows a standard Scrapy layout, including a root scrapy.cfg. The conversion creates Actor files and directories, installs the SDK and dependencies, and updates Scrapy settings with platform components. This is a platform-specific wrapper, not a universal, unchanged deployment path.
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The Apify Python SDK overview identifies version 4.0 and requires Python 3.11 or later. It describes Scrapy support along with Actor lifecycle, storage, platform events, and proxy capabilities. Review the Apify Scrapy guide and Python SDK overview for current details.
Before adopting this route, check the guide’s limitations and test platform input handling, storage destinations, request-queue behavior, and graceful shutdown. The Apify guide presents an AsyncCrawlerRunner/asyncio bridging approach and platform-specific settings; use the current guide for the exact procedure rather than copying snippets from previews or older documentation.
What to test before switching production crawls
A tiny spider that fetches one static page cannot show whether a migration is safe for a production workload. Choose one representative crawl with the behaviors your system depends on.
- Exercise realistic pages. Include a normal page, a JavaScript-rendered page if your workload needs one, pagination, and retry cases.
- Keep the real output path. Run the actual pipeline or exporter and verify schema, item counts, duplicate handling, and downstream delivery.
- Compare run behavior. Record crawl duration, memory and concurrency, retry and error rates, logs, and exit status. These are validation dimensions to measure on your workload, not published cross-vendor benchmarks.
- Test operational edges. Confirm schedule behavior, secrets, persistent state, queue or storage expectations, and graceful shutdown where relevant.
- Make rollback explicit. Keep the existing deployment configuration and define how to restore it. Migrate a small group of crawls before changing the full production schedule.
Estimate cost and operational fit from your own workload
There is no supported like-for-like price or performance comparison across the hosting, request API, and SDK paths here. They charge for and manage different parts of the work. Compare what you actually need: runtime resources and concurrency, crawl time, data retention, API usage, scheduling and monitoring, proxy or browser-rendering needs, and the effort of adapting storage and operations.
Best Value
Use a representative pilot to estimate resource use and request volume, then check each provider’s current pricing and limits. Treat vendor plan terms as changeable product details; do not infer that a free tier or a per-unit plan will fit a particular crawl until its actual duration, concurrency, and retention needs are known.
Or skip the browser setup
If one of your migration tasks is capturing site screenshots rather than crawling and parsing pages, ScreenshotNeo is a website screenshot API and MCP server; it is not a replacement for Scrapy’s crawl scheduler or item pipelines. One GET request returns an image or PDF. Its capture workflow accepts consent banners like a visitor and removes 60+ known consent platforms, newsletter popups, and chat widgets; each step can be turned off. Only clean shots are billed: bot checks, CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, with response headers identifying the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf for Claude, Cursor, and other MCP clients. Plans include 1,000 screenshots monthly free with no card, and paid plans start at $5 for 3,000.
For example, this cURL request captures a page as WebP; see the ScreenshotNeo API documentation for request options:
curl -G "https://api.screenshotneo.com/v1/shot"
-d access_key=YOUR_API_KEY
--data-urlencode url=https://stripe.com
-o shot.webpSign up for ScreenshotNeo to get 1,000 screenshots a month free, with no card required.
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Do I need to rewrite my Scrapy spiders to use a cloud service?
Not necessarily. Hosting and request-API migrations can retain the Scrapy project, while an SDK wrapper adds platform-specific files and settings. Validate your project’s custom integrations and operational assumptions before treating it as unchanged.
Does adding a scraping API move my scheduler and output storage to the cloud?
No. An integration such as scrapy-zyte-api handles request/download behavior; it does not by itself move Scrapy deployment, scheduling, or output storage.
Which path should I pilot first?
Pilot the smallest change that addresses the actual issue: managed hosting for execution and operations, an API layer for request fetching, or a platform SDK when you want that platform’s runtime and services.
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