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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
HubSpot’s API rate limits can turn machine learning model development into a frustrating waiting game. Professional accounts get just 100 requests per 10 seconds, meaning large contact datasets take hours to extract with complex retry logic.
Here’s how to eliminate rate limit headaches and focus on what matters: building better models.
Bypass rate limits with managed API connections using Coefficient
Coefficient handles all HubSpot API interactions through optimized, managed connections. Instead of building custom rate limiting logic and request queuing systems, you get unlimited contact data extraction without hitting any limits.
How to make it work
Step 1. Set up a managed HubSpot connection.
Connect your HubSpot account through Coefficient’s interface. This creates an optimized API connection that uses batch processing and intelligent request management to maximize data throughput.
Step 2. Import large contact datasets in single operations.
Pull 50,000+ contact records without worrying about rate limits. Coefficient’s managed connection handles all the API optimization behind the scenes, far exceeding what individual API calls can achieve within rate limit constraints.
Step 3. Schedule bulk updates instead of frequent API calls.
Set up automated imports that refresh your entire contact dataset daily or weekly. This approach consumes zero of your rate limit budget while keeping your ML training data current.
Step 4. Focus on model development, not infrastructure.
Skip building exponential backoff algorithms, request queuing systems, and error handling for rate limit exceptions. Your contact data stays fresh in Google Sheets through scheduled refreshes, providing a reliable foundation for model training.
Build models without the API complexity
Managed connections eliminate the technical overhead of API rate limit management, letting you focus on model architecture and feature engineering instead of data pipeline infrastructure. Start building with reliable data feeds today.
