Apify and Octoparse land on the same shortlist all the time, yet they answer two different organisational questions. One assumes someone on your side can read technical documentation and wire up an API; the other assumes nobody ever will. Price, time to first result and durability all follow from that assumption. This comparison names no winner: it isolates the axes the decision actually turns on, quoting nothing but the two vendors' own pages.
A shop of ready-to-run actors against a visual assembly bench
Apify is not primarily a scraper, it is an execution platform with a marketplace attached. 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 attacks the problem from the other end. 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 is not cosmetic: with Apify the logic of the crawl sits inside 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 ceiling is not in the same place, and that is the central 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 the cost driver is not the volume extracted but the number of configurations you maintain. 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 unbeatable on delay: the Actor exists, you fill in an input form and press run. On a target nobody has covered, you change jobs: an Actor is a program running in a Docker container, with a build file and source code (Apify, official documentation, 2026). The gap between the covered case and the uncovered one is brutal.
Octoparse flattens that step: 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
This is the axis feature comparisons skip, and the one that decides the real cost at twelve months: a site changes its layout, and extraction either stops or, worse, 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, and it is also the guarantee that you depend on nobody. So the question is not which one breaks less, it is who, on your side, will be at the screen the day it breaks. 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 bench is the only one of the two they will fix alone.
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). One caveat: 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 favours 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: the question under the question
Many people comparing these two tools are in fact undecided about the method, not the brand. The target site's official API, where one exists, looks like the clean route: contractual, stable, documented. Two checks are due before you conclude.
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). Assembling tens of thousands of detailed records is therefore not a casual operation.
The contract next, and this is the decisive part. 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 make you the owner of a reusable file: it is not an expensive version of scraping, it is a different usage regime. Scraping does not remove the constraint either, it shifts the burden onto 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.
What the table says once you remove the prices
| 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 the comparison above is well run but beside the point: when the target is a list of French establishments to approach. Both tools do the extraction; what is missing comes after, and it is a scope problem, not a quality problem. 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 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 access is granted on request: request free alpha access.
It is only fair to say where we are not the right answer either: product catalogues, 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 specialised in French company data would be the wrong pick. The criterion is not execution quality, it is the nature of the 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 cost of that combination is cognitive: 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 build bench 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.