Zyte vs. Apify vs. Crawlbase: Which Scraping Platform Fits?
TL;DR
Apify is the strongest fit when a team wants deployable Actors, scheduling, storage, integrations, and a marketplace in one platform.
Zyte is a strong fit for Scrapy-centered teams and managed extraction workflows that value its crawler and data-service ecosystem.
Crawlbase is a simpler fit when the primary need is an API-oriented crawling or scraping layer rather than a hosted application platform.
The platforms are not interchangeable: compare who owns crawler code, browser behavior, storage, scheduling, extraction, and incident response.
Use a representative corpus and calculate cost per accepted record; request or credit prices alone are not comparable.
What is the main difference between Zyte, Apify, and Crawlbase?
The main difference is platform scope and ownership. Apify centers on Actors and cloud execution, Zyte combines scraping infrastructure with Scrapy-oriented and managed data services, and Crawlbase emphasizes API-based page acquisition and selected data products. Nstdata Crawl is another managed acquisition option for teams that want page and bounded-site artifacts behind an API rather than a full marketplace runtime.
The web-data stack guide helps separate discovery, acquisition, extraction, storage, and delivery. A fair comparison starts by deciding which of those layers the provider should own.
How do Zyte, Apify, and Crawlbase compare?
Decision field
Zyte
Apify
Crawlbase
Primary model
Scraping APIs, Scrapy cloud tooling, managed data
Actor runtime, marketplace, schedules, storage
Crawling and scraping APIs plus selected data APIs
Zyte is the better fit when the organization already uses Scrapy, wants a Scrapy-focused cloud workflow, or needs a provider to take on more of a managed data project. Its API and extraction offerings should be evaluated separately because they represent different responsibility boundaries.
Key strengths include alignment with the Scrapy ecosystem, managed acquisition options, and services for organizations that want delivered data rather than only raw pages. The limitations are decision complexity and portability: a team must determine which product owns rendering, parsing, maintenance, and delivery, then test that exact combination.
When is Apify the better fit?
Apify is the better fit when teams want to package crawlers as Actors, schedule and run them in the cloud, store outputs, connect integrations, or adopt an existing marketplace Actor. It supports both custom development and reusable automation.
The main advantage is breadth: runtime, storage, queues, schedules, API access, and marketplace distribution can live together. The limitation is variability. An Actor is a separate dependency with its own maintainer, schema, pricing, and update cadence; marketplace availability does not guarantee production quality.
When is Crawlbase the better fit?
Crawlbase is the better fit when the application mainly needs an HTTP-facing crawling or scraping service and prefers to own downstream parsing, queues, and storage. This can keep the integration small for page acquisition and selected target-oriented workflows.
The trade-off is that teams seeking a general code-hosting platform, broad marketplace, or deep Scrapy workflow may need additional components. Evaluate dynamic rendering, geography, response evidence, and failure semantics on the exact pages rather than inferring them from product categories.
How do the platforms handle dynamic pages and extraction?
All three vendors describe ways to retrieve modern pages, but the unit of control differs. Zyte can place extraction and browser behavior behind its API or services. Apify lets Actor code use browser and crawler libraries inside its runtime. Crawlbase places more emphasis on the request API. These differences affect debugging: provider-managed extraction is simpler to call, while custom code provides more control and more maintenance.
Create a corpus with static, rendered, long, localized, duplicate, and expected-failure pages. Measure main-content completeness, field accuracy, artifacts, diagnostics, latency, retries, and billable units. The dynamic scraping tools guide supplies related method-selection context.
How do pricing and operations differ?
Pricing should be compared by model rather than old plan numbers. Possible units include requests, credits, compute time, storage, data transfer, platform usage, or managed project scope. Normalize everything to accepted business records after failures and review.
Operationally, ask who patches browsers, controls concurrency, owns retries, stores raw artifacts, deploys parser updates, and responds when a target template changes. The cheapest successful demo can become the most expensive production path if it leaves those responsibilities undefined. Nstdata's scaling web scraping guide provides an operational checklist.
Where does Nstdata Crawl fit?
Nstdata Crawl fits teams that want managed page scraping and bounded site collection with task state and multiple artifact types, but do not need a marketplace for arbitrary hosted automations. Nstdata Crawl should be compared on content completeness, rendering accuracy, diagnostics, and maintenance removedβnot on a claim of universal superiority.
Bounded collection: Define page, depth, inclusion, and exclusion limits for site jobs.
Artifact-oriented output: Keep machine-readable content and visual review artifacts connected to the same source record.
Selection boundary: Teams needing a marketplace or Scrapy-specific cloud workflow may prefer Apify or Zyte.
Check current Crawl pricing and run the same test corpus used for the three competitors. The comparison is meaningful only when acceptance rules are identical.
Which platform should you choose?
Choose Zyte for a Scrapy-centered or managed-data operating model, Apify for a broad hosted automation platform and marketplace, and Crawlbase for a narrower API-first acquisition layer. Choose Nstdata Crawl when page and bounded-site collection with reviewable artifacts matches the required boundary. Do not migrate solely because another platform lists more features.
Run a dual-write pilot before switching. Preserve canonical URLs, source hashes, timestamps, error classes, and parser versions. The new provider should feed the same validation and storage contract so provider differences remain observable.
Conclusion
Zyte, Apify, and Crawlbase solve overlapping but different problems. Write down the operating boundary first, then test content quality, failure evidence, and cost per accepted record. Keep storage and business schema portable. For managed page acquisition, include Nstdata Crawl in the same corpus test; evaluate Nstdata Proxy Manager separately if centralized proxy routing is the actual requirement.
Experience Nstdata β Start Your Free Trial Today
Apify is generally a better fit for Actor-based cloud automation and marketplace workflows, while Zyte is a better fit for Scrapy-centered and managed-data workflows. The answer depends on the operating model.
Q: Is Crawlbase a full replacement for Apify?
Not for every workload. Crawlbase can cover API-based acquisition, while Apify also provides a code runtime, marketplace, schedules, storage, and other platform components.
Q: Which platform is best for Scrapy?
Zyte has the strongest direct relationship with the Scrapy ecosystem, but teams should verify the current cloud and API products that match their workflow.
Q: How should these platforms be benchmarked?
Use the same authorized URLs, expected fields, rendering conditions, timeouts, and acceptance rules, then compare accepted records, diagnostics, latency, retries, and total billable units.
Q: Should a team migrate all crawlers at once?
No. Start with a representative low-risk workflow, dual-run it, compare outputs, and migrate only after recovery and cost behavior are understood.
Ivy Lin
Sep. 22nd 2026
Crawl entire websites with a single API request
99.8% success rate with JavaScript rendering
Get clean, LLM-ready data in multiple formats
Turn any website into Markdown, HTML, JSON, links, PDFs and more β without managing crawling infrastructure.
