Sonny d.

Hey everyone, I’m running into a scaling dilemma with my current setup using n8n, Apify, and Clay.com. At the moment I’m processing around 5,000 new leads per day. This works fine for one country, but I’d like to expand to about 10 additional countries, which will significantly increase the volume. Here’s the issue: Clay has a 50,000 row limit per table. If I put everything into one table, I hit that limit in just a single day. If I split them into separate tables per country, I’d need to manage 10+ tables, and each of those would also hit the limit within a few days. That quickly becomes a lot of manual overhead. What I’m looking for is a way to make this scalable and manageable, without constantly hitting limits or manually juggling multiple tables. Maybe I’m thinking about this too narrowly and need a more creative or alternative approach. My question: How would you tackle this kind of scale issue in Clay? Are there best practices, workarounds, or integrations to keep things manageable while still working with large volumes? ( I don't have the money for the enterprise version unfortunately )

Hey everyone, I am facing a problem currently. The Clay job scraper has not been getting any results for the last week. Eventhough it was getting a 1000+ results everyday with the exact same scraping query. Can anyone help me with this?

Hey everyone, I have just started using Clay and I have ran into an issue: I’m using an Apify actor (Text Scraper (Free)) to extract job descriptions from LinkedIn links in my Clay table. I have over 300 rows, but Apify only allows me to run 2 at a time due to RAM limitations. Does anyone know a good workaround? For example: • Running the actor in batches via Clay • Or using a different scraping method to process all rows more efficiently? I’m mainly trying to extract the job descriptions linked to each row. Any ideas or suggestions would be really helpful!