Managing AI Enrichment Rows: Challenges and Solutions

Petar R. · 2025-07-22T18:52:22.588Z

Does it ever become complex to run thousands of AI enrichment rows at the same time due to limits, when using API Tier 2 key? How to overcome this?

Managing AI Enrichment Rows: Challenges and Solutions | Clay

10 comments

· Sorted by Oldest

Clay
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Yes, running thousands of AI enrichments simultaneously can cause complexity due to API tier limits and performance issues. To overcome these issues: • Process in smaller batches - With lower API tiers (like Anthropic tier 1), process around 50 rows at a time instead of thousands • Test before scaling - Start with 5-10 rows, then 50, then 500 to avoid issues • Use conditional runs - Set up "Only run if" conditions in Run Settings to limit enrichments to relevant rows only • Use filtered views - Only enrich rows in your current view to manage processing load Performance issues are more likely with larger tables and complex workflows, so batching and conditional logic help manage both API limits and system performance.
Sources: Best Practices To Maximize Your Credits - Clay University Guide: Ways to save Clay credits | Documentation | Clay University Guide: Ways to save Clay credits | Documentation | Clay University
Petar R.
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What about openai tier 2?
Clay
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With OpenAI Tier 2, you'll have higher rate limits than Tier 1, allowing for better performance when processing thousands of AI enrichments. Best practices for efficient processing: • Test before scaling - Start with 5-10 rows, then 50, then 500 to ensure everything works properly • Use conditional runs - Set up "Only run if" conditions in Run Settings to limit enrichments to relevant rows only • Use filtered views - Only enrich rows in your current view to manage processing load and save credits • Monitor your limits - Check your OpenAI API usage and limits within your OpenAI account to avoid hitting rate limits While Tier 2 provides better performance than lower tiers, you should still batch your processing and use conditional logic to optimize both performance and credit usage.
Sources: Best Practices To Maximize Your Credits - Clay University Guide: Ways to save Clay credits | Documentation | Clay University
Hari K.
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Hey Petar - Just wanted to check in, were you able to figure this out?
Petar R.
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Kinda
Petar R.
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Is there a spdcific error if token limit reached?
Hari K.
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I can take a look at what might be going on - can you share the link to your table?
Petar R.
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Nothing's going on atm. I was asking since I plan on scaling. I'm looking for the most automated way to run my workflow. Can I easily start 1000 rows wigh 4 AI enrichments at once?
Hari K.
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Hey Petar! Great question - I believe this should work fine as the rate limits are on a per row/column basis. Table-wide I have seen some run 20k+ rows across a few different columns and no errors occuring.
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Petar R.
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Amazing. Thanks!

Clay
APP
·
·
Yes, running thousands of AI enrichments simultaneously can cause complexity due to API tier limits and performance issues. To overcome these issues: • Process in smaller batches - With lower API tiers (like Anthropic tier 1), process around 50 rows at a time instead of thousands • Test before scaling - Start with 5-10 rows, then 50, then 500 to avoid issues • Use conditional runs - Set up "Only run if" conditions in Run Settings to limit enrichments to relevant rows only • Use filtered views - Only enrich rows in your current view to manage processing load Performance issues are more likely with larger tables and complex workflows, so batching and conditional logic help manage both API limits and system performance.
Sources: Best Practices To Maximize Your Credits - Clay University Guide: Ways to save Clay credits | Documentation | Clay University Guide: Ways to save Clay credits | Documentation | Clay University
Petar R.
·
·
What about openai tier 2?
Clay
APP
·
·
With OpenAI Tier 2, you'll have higher rate limits than Tier 1, allowing for better performance when processing thousands of AI enrichments. Best practices for efficient processing: • Test before scaling - Start with 5-10 rows, then 50, then 500 to ensure everything works properly • Use conditional runs - Set up "Only run if" conditions in Run Settings to limit enrichments to relevant rows only • Use filtered views - Only enrich rows in your current view to manage processing load and save credits • Monitor your limits - Check your OpenAI API usage and limits within your OpenAI account to avoid hitting rate limits While Tier 2 provides better performance than lower tiers, you should still batch your processing and use conditional logic to optimize both performance and credit usage.
Sources: Best Practices To Maximize Your Credits - Clay University Guide: Ways to save Clay credits | Documentation | Clay University
Hari K.
·
·
Hey Petar - Just wanted to check in, were you able to figure this out?
Petar R.
·
·
Kinda
Petar R.
·
·
Is there a spdcific error if token limit reached?
Hari K.
·
·
I can take a look at what might be going on - can you share the link to your table?
Petar R.
·
·
Nothing's going on atm. I was asking since I plan on scaling. I'm looking for the most automated way to run my workflow. Can I easily start 1000 rows wigh 4 AI enrichments at once?
Hari K.
·
·
Hey Petar! Great question - I believe this should work fine as the rate limits are on a per row/column basis. Table-wide I have seen some run 20k+ rows across a few different columns and no errors occuring.
1
Petar R.
·
·
Amazing. Thanks!