Hey everyone,
I’m working on a project where we’re using Airtable’s REST APIs to handle our employee data. I’ve noticed that there’s a limit of 5 requests per second for each base. This is causing some issues for us as we scale.
I’m wondering if anyone knows:
- Does upgrading to a Pro or Enterprise plan give us more API requests?
- Are there any other ways to bump up the API request limit?
We really need to increase our throughput. Any tips or experiences would be super helpful!
Thanks in advance for your help. I’m looking forward to hearing what solutions you all might have come up with for this.
I’ve dealt with similar API constraints in Airtable. While upgrading plans doesn’t increase the request limit, there are strategies to optimize your usage. Consider implementing a queue system to manage requests and avoid hitting the limit. This can help smooth out spikes in API calls. Additionally, you might want to explore Airtable’s Sync API for larger datasets, as it’s designed for more efficient data syncing. Lastly, if these options don’t suffice, reaching out to Airtable support directly about your specific use case might yield some custom solutions. They’re generally responsive to enterprise needs.
hey davidw, i’ve been there. upgrading to pro/enterprise doesn’t boost api limits unfortunately. but there’s a workaround - you can batch requests to make fewer calls. also, caching frequently accessed data locally helps a ton. hope this helps, lemme know if u need more info!
As someone who’s worked extensively with Airtable’s API, I can tell you that the request limits can be a real headache. While upgrading plans won’t increase the limit, I’ve found success in implementing a rate limiter in our code. This helps manage the flow of requests and prevents us from hitting the ceiling.
Another approach that’s worked well for us is denormalization. By storing some redundant data across tables, we’ve reduced the number of API calls needed for certain operations. It’s a trade-off between data consistency and API efficiency, but it’s been worth it in our case.
Lastly, don’t underestimate the power of asynchronous processing. We’ve set up a system where non-urgent updates are queued and processed in batches during off-peak hours. This has significantly reduced our real-time API usage.
Remember, optimizing your data model and query patterns can go a long way in reducing the number of necessary API calls. It’s worth revisiting your schema to see if there are any inefficiencies you can iron out.