Timeout errors and increased response times can occur due to server load during high-traffic periods. With serverless, users are essentially sharing a pool of GPUs with models pre-provisioned. The goal of serverless is to allow users and teams to seamlessly power their generative applications with the latest generative models in less than 5 lines of code. Deployment barriers should be minimal and pricing is based on usage. However there are trade-offs with this approach, namely that in order to ensure users have consistent access to the most in-demand models, users are also subject to minor latency and performance variability during high-volume periods. With on-demand deployments, users are reserving GPUs (which are billed by rented time instead of usage volume) and don’t have to worry about traffic spikes. Which is why our two recommended ways to address timeout and response time issues is:
  • Use on-demand deployments for more stable performance
  • Guaranteed response times
  • Dedicated resources to ensure availability
We are always investing in ways to improve speed and performance.

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Upcoming improvements

  • Enhanced SLAs for uptime
  • More consistent generation speeds during peak load times
If you experience persistent issues, please include the following details in your support request:
  1. Exact model name
  2. Timestamp of errors (in UTC)
  3. Frequency of timeouts
  4. Average wait times

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Performance optimization tips

  • Consider batch processing for handling bulk requests
  • Implement retry logic with exponential backoff
  • Monitor usage patterns to identify peak traffic times
  • Set appropriate timeout settings based on model complexity