Apify and Octoparse often end up on the same shortlist, but they suit different teams. Apify assumes someone on your side can read technical documentation and connect an API. Octoparse assumes nobody will. Price, setup time and long-term reliability all follow from that. This comparison goes through the criteria that matter for the decision, quoting only the two vendors' own pages.
Ready-to-run Actors on one side, a visual no-code tool on the other
Apify is first of all an execution platform with a marketplace. Its documentation defines an Actor as a serverless cloud program that takes structured JSON input, performs a task and optionally produces structured output (Apify, official documentation, 2026). Those programs come from third-party developers as much as from the vendor, and the public Store page lists 65,882 of them (Apify Store, 2026). You are not building a scraper: you are picking one and running it.
Octoparse works the other way around. The vendor presents it as a no-code way to turn web pages into structured data: auto-detect drafts a first workflow that you adjust by drag and drop, plus a desktop application and scheduled cloud extraction (Octoparse, official product page, 2026). Its pricing page advertises 500+ preset templates for popular sites from the Standard plan up (Octoparse, official pricing page, 2026). You build the scraper yourself, without writing code.
The difference matters. With Apify, the extraction logic sits in a program someone else wrote. With Octoparse, it sits in a configuration you own.
What you are really buying: compute on one side, tasks on the other
Apify sells a subscription backed by prepaid usage. Its pricing page shows four plans: Free at $0 with $5 of usage included, Starter at $19 with $19 included, Scale at $199 with $199 included, Business at $999, with 10% off on annual billing. The underlying unit is the compute unit, priced at $0.2 on Free and Starter, $0.16 on Scale and $0.13 on Business; the same page states that unused usage credits do not roll over and expire at the end of the billing cycle (Apify, official pricing page, 2026). The formula is published: allocated memory multiplied by duration, so 1024 MB x 1 hour = 1 CU (Apify, official documentation, 2026). Each Actor also applies one of two models, a fixed price per event or pass-through platform usage, stated on its own page.
Octoparse sells a quota of tasks. The free plan gives 10 tasks, 1 device, local extraction and 50,000 rows of export per month to Excel, CSV, JSON, HTML or XML; Standard is $69 per month for 100 tasks and Professional $249 per month for 250 tasks, both rates quoted on annual billing, which the vendor puts at 16% below monthly billing (Octoparse, official pricing page, 2026).
So the limit is not in the same place, and that is the main trade-off. With Apify nothing caps the number of distinct targets, but the invoice follows machine time: a dense area, a slow page, a retry after failure all consume without guaranteeing one extra row. With Octoparse, what drives cost is the number of configurations you maintain, whatever the volume extracted. Twenty sites at low volume pull towards Octoparse; three sites at very high volume pull towards Apify.
How long before the first usable row
On a common target, Apify is the fastest: the Actor exists, you fill in an input form and press run. On a target nobody has covered, you need a developer: an Actor is a program running in a Docker container, with a build file and source code (Apify, official documentation, 2026). The gap between a covered target and an uncovered one is very large.
With Octoparse the gap is smaller: auto-detect proposes a workflow, you fix the selectors with the mouse, you add pagination or infinite scroll. Slower than a ready-made Actor on a known target, far faster than development on an unknown one. For a team with no developer, that consistency usually matters more than the best-case speed.
The day the target site moves
Feature comparisons rarely cover this, yet it decides the real cost over twelve months. A site changes its layout, and extraction either stops or keeps running and returns empty columns.
With a third-party Actor, the repair is not yours: it depends on the developer who publishes it. An advantage while they are active, a dependency once they are not. Each Actor page states its author and its pricing model, which makes that dependency checkable before you commit (Apify, official pricing page, 2026).
With Octoparse the repair is always yours, except on the templates the vendor maintains. That is more work, but you depend on nobody. So the real question is who on your team will handle it the day extraction stops working. If the answer is nobody, a third-party maintained Actor is the sensible bet. If the answer is a non-technical person already on the team, the visual tool is the only one of the two they can fix on their own.
Leaving with your work: what survives if you stop paying
Portability applies to two separate things: the data produced, and the recipe that produces it.
On data, both are fine. Octoparse exports to Excel, CSV, JSON, HTML and XML from the free plan, and exposes an API, a command line interface and an MCP connector (Octoparse, official pricing page, 2026). The same page lists a cloud data retention period of 30 to 90 days depending on plan, and none at all on the free plan. Whatever is not exported in time is gone.
On the recipe, the gap favors Apify for anyone who codes: its documentation states that Actors can be executed locally as well as on the platform (Apify SDK, official documentation, 2026). An Actor you wrote stays a program you can take with you; a Store Actor written by a third party does not; an Octoparse configuration only runs inside Octoparse. For a technical team the argument is serious; for a team that will never write an Actor, it has no effect.
Scraping or official API
Many people comparing these two tools are really undecided about the method. The target site's official API, where one exists, looks like the clean option: contractual, stable, documented. Check two things before you decide.
Cost first. On Google Maps, the Places API Text Search Pro SKU includes 5,000 free requests per month, then $32.00 per 1,000 requests in the first tier; Place Details Pro includes 5,000 free then $17.00 per 1,000 (Google Maps Platform, official pricing list, 2026). Building a list of tens of thousands of detailed records gets expensive.
Then the contract, which is the deciding factor. The Google Maps Platform terms prohibit copying and saving business names, addresses or user reviews, and set an explicit no-caching clause on Maps Content except as expressly permitted by the service specific terms (Google Maps Platform Terms of Service, section 3.2.3, 2026). Paying for the API does not give you a reusable file: the usage rules are different from scraping. Scraping does not remove the constraint either, it puts it on you, which we cover in our piece on what scraping actually covers. Choosing between API and extraction means choosing who carries maintenance and who carries risk, not just what you pay.
Side-by-side comparison, prices aside
| Criterion | Apify | Octoparse |
|---|---|---|
| Nature of the product | Actor marketplace and execution platform | Visual no-code tool, desktop and cloud |
| Billing unit | Prepaid usage, compute unit (memory times duration) | Subscription with a quota of configured tasks |
| Structural ceiling | Machine time consumed | Number of distinct configurations |
| Starting point on a covered target | An existing Actor and its input form | A preset template or auto-detect |
| Starting point on an uncovered target | Developing a containerised program | Visual workflow building |
| Who owns maintenance | The Actor's developer, or you if you wrote it | You, except on vendor-maintained templates |
| Portability of the recipe | An Actor you write also runs locally | The configuration only runs inside the tool |
| Portability of the data | Datasets exportable through the API | Excel, CSV, JSON, HTML, XML from the free plan |
Read the structural ceiling row first: it predicts your invoice at month twelve far more reliably than the headline price does.
When neither Apify nor Octoparse answers the question asked
There is one case where this comparison misses the need: when the target is a list of French businesses to prospect. Both tools do the extraction. What is missing comes after. A Google Maps export gives a trading name, an address, a front-desk phone number and sometimes a website, but not the link to the official register, while the Sirene directory records close to 25 million companies and 36 million establishments, updated daily (Insee, official page, 2026). That total is a cumulative count since 1973, closures included: the live stock is smaller, 14,351,029 active and publicly listed establishments as of 2 October 2026.
That is the scope outsend works on: extracting an area, then in the same chain the SIRET, SIREN, VAT and RCS attachment, legal form and directors, then verified email addresses. The platform is in alpha and anyone can create an account: create a free account.
outsend is not the right tool for everything: product catalogs, price monitoring on a marketplace, data outside company directories, a daily feed into a warehouse from arbitrary sites. On all of those, Apify and Octoparse remain better, and a tool specialized in French company data would be the wrong pick. What decides is the type of target.
Four questions before you sign
Does the Octoparse task count limit the volume extracted?
No: a task is an extraction configuration, not a row quota. Paid plans advertise unlimited export, while the free plan caps at 50,000 rows per month (Octoparse, official pricing page, 2026). The constraint is on the number of targets, not on how deep each one goes.
Why is the cost of an Apify run hard to announce in advance?
Because the billed unit is the resource consumed, allocated memory multiplied by duration (Apify, official documentation, 2026). Two extractions returning the same number of rows can cost differently depending on how dense the area is and how slow the pages are. Actors billed per event reduce that uncertainty without removing it.
Do you really have to choose between the two?
Not necessarily. Plenty of teams run a ready-made Actor on their two or three main targets and a visual tool for the long tail. The downside is complexity: two interfaces, two quota logics, two places to look when an extraction returns nothing. Our panorama of Apify alternatives covers the other options.
Which one for a one-off extraction on a single city?
Apify, almost always: a one-off job does not justify learning a tool you will not open again next month. The reasoning flips as soon as extraction becomes recurring on sites the Store does not cover, because the learning time pays for itself and the third-party dependency disappears.