What I like most about Apify is how quickly it allows you to start collecting data from public websites. The platform offers a large library of ready to use actors, reliable cloud execution, and excellent scalability. It eliminates much of the complexity involved in building and maintaining custom scraping infrastructure, which saves a significant amount of development time. Review collected by and hosted on G2.com.

The pricing can become expensive as usage scales, especially for large scraping workloads. Some advanced use cases also require familiarity with web scraping concepts, and debugging failed runs can occasionally take time. However, these are relatively minor drawbacks compared to the value the platform provides. Review collected by and hosted on G2.com.

I love Apify because it saves me from having to build scrapers from scratch, and I can write my scrapers in Python, which fits seamlessly into my existing workflow. I also appreciate that I can publish my own Actors to the marketplace and potentially earn from them as well. Review collected by and hosted on G2.com.

I end up paying for compute time even when a big chunk of that time is just spent waiting for slow pages to load, which feels pretty wasteful. I also can’t always rely on a third-party Actor for sensitive scraping tasks, because I don’t fully control what it’s doing behind the scenes. And for simple, small-scale scraping, it honestly feels like overkill—I’d rather just run my own script. Review collected by and hosted on G2.com.

It bypasses strong anti-spam protections on websites and lets me scrape useful data for my business. It’s also very intuitive. The pricing is OK for what it does. I also like the AI chatbot, which gives me answers whenever I need them. On top of that, I can use it very easily with N8N to build complex, powerful workflows. Review collected by and hosted on G2.com.

Sometimes it fails on a few scrapes, but it’s marginal and rare. In some cases, I think the issue is the website I’m scraping rather than Apify. Also, the AI bot sometimes disappears and then comes back the next day. It’s a bit weird, because I need it at times and it’s just not there. Review collected by and hosted on G2.com.

The API is clean and the JSON output is predictable. I connect it to LLMs in minutes without fighting parsers or handling edge cases. Pre-built actors for websites, YouTube, and Spotify save hours compared to building scrapers from scratch. It just works. Review collected by and hosted on G2.com.

The dashboard UX feels clunky. Finding actors, managing runs, and navigating the platform isn't smooth. I haven't tested deep linking much yet, but it hasn't worked well when I tried. The core functionality is solid even if the interface needs polish. Review collected by and hosted on G2.com.

I’ve been building scrapers on Apify as a solo developer for about a year now, and what keeps me here is the infrastructure I don’t have to think about. Proxies, retries, dataset storage, and scheduling are all handled. I run daily scrapes against fintech sites, app stores, and regulator pages for a competitive intelligence product I’m building, and Apify’s universal actors cover most of what I need without me having to write custom code. Review collected by and hosted on G2.com.

For now, nothing. I am very satisfied with the services and also the prompt attention gotten from the support team Review collected by and hosted on G2.com.

Easy to set up actors, flexible automation tools, and good integration options for scraping workflows. Review collected by and hosted on G2.com.

Our experience with Apify’s Twitter scraper started positively, but over time it became extremely disappointing and costly due to multiple issues that directly affected reliability and budget control.

1. Parameters not working as expected

Many of the parameters provided by the actor did not behave consistently. Even when we configured limits such as maximum items to fetch, the scraper did not always respect them. This made it very difficult to rely on the actor in a production workflow where predictable behavior is critical.

2. Unnecessary fetching that wasted our budget

On several occasions the scraper fetched thousands of tweets even though strict limits were configured. These runs consumed a large amount of resources and unexpectedly increased our costs. What made this worse was that these fetches were not intentionally triggered by us, yet the platform still charged for them. When we raised the issue, there was no meaningful resolution or refund, even though the behavior clearly went beyond the configured limits.

3. Fetching outdated tweets instead of recent ones

Another recurring issue was that the scraper frequently returned old tweets instead of the latest ones, even when using options intended to retrieve the most recent results. For time-sensitive workflows this makes the data unreliable. We often saw situations where tweets from the previous day appeared while tweets posted within the last hour were missing entirely.

4. Uncertain and inconsistent scraper behavior

The overall behavior of the Twitter scraper felt unpredictable. Identical configurations would sometimes produce completely different results between runs. Some runs would miss relevant tweets, while others would return irrelevant or outdated data. This level of inconsistency makes it difficult to trust the tool for automated systems.

While Apify provides a capable platform and a developer-friendly interface, the lack of strict control over limits, unreliable scraping results, and poor cost safeguards created serious operational issues for us. For any system that depends on predictable data collection and controlled spending, these problems can become very costly very quickly.

Until stronger safeguards, clearer parameter behavior, and better cost protection mechanisms are implemented, it is difficult to recommend relying on the Twitter scraper for production-critical workflows. Review collected by and hosted on G2.com.

What I like best about Apify is how it combines powerful, scalable web scraping with a clean, developer-friendly workflow. It lets me automate complex data pipelines—like extracting YouTube data at scale—without worrying about infrastructure or maintenance. The combination of ready-made actors, API access, and seamless integrations saves a huge amount of time while still giving me full control when I need it. Review collected by and hosted on G2.com.

Some actors need fine-tuning, and initial setup can be slightly complex for advanced workflows and scaling needs. Review collected by and hosted on G2.com.

I use Apify to scrape Instagram profiles at scale pulling follower counts, engagement, and bio links for DTC brands. That data feeds into my n8n pipeline for enrichment, scoring, and outreach. It replaced manual prospecting entirely. No API, no problem. Review collected by and hosted on G2.com.

Some actors stop mid-run without a clear error message. Debugging why a scrape failed takes more time than it should. Review collected by and hosted on G2.com.

What I like most about Apify is that it makes web data collection scalable and repeatable without forcing us to build everything from scratch. The ready-made actors are especially useful for use cases such as Google Search monitoring, LinkedIn research, lead generation, and competitor tracking.

The biggest benefit for us is the time savings. Rather than manually collecting and organizing data, we can automate the process, schedule recurring runs, and export results in formats like CSV, Excel, JSON, or Google Sheets. The API access is also a major plus, since it lets us connect scraped data to our internal workflows and reporting tools.

Overall, Apify stands out because it brings flexibility, automation, and usability together in one platform. It helps our team get more consistent data, move through research cycles faster, and scale data collection without adding extra manual effort. Review collected by and hosted on G2.com.

What I dislike about Apify is that some workflows can still feel quite technical, especially for non-engineering users. Setting up certain actors, configuring inputs properly, choosing the right proxy settings, and understanding usage limits often takes some trial and error.

Another downside is that scraper reliability can vary depending on the target website. With sources like Google Search or LinkedIn, changes in page structure, access rules, or rate limits can lead to failed runs or incomplete results. That’s understandable with web scraping, but it still means you need to monitor runs and do occasional troubleshooting.

The cost structure can also be hard to predict before running larger jobs. More upfront cost previews, clearer error messages, and more guided setup steps would make the platform easier to use and reduce uncertainty, especially for business users. Review collected by and hosted on G2.com.

Apify makes it incredibly easy to scale our web scraping operations. Their built-in proxy rotation is highly reliable and handles IP bans flawlessly. It has saved our team countless hours that we would have otherwise spent managing custom scraping infrastructure, allowing us to focus on extracting and analyzing the data rather than dealing with request blocks. Review collected by and hosted on G2.com.

The learning curve can be a bit steep for absolute beginners. While the pre-built Actors are extremely helpful for standard use cases, building a custom scraper from scratch requires a solid understanding of Node.js or Python, which might be a bit challenging if you don't have a technical background. Review collected by and hosted on G2.com.