Failover routing strategies for LLMs in production
Blog post from Portkey
Running large language models (LLMs) in production is prone to challenges such as provider outages, latency spikes, and rate limits, which can disrupt user experience and business workflows. Delays, often perceived by users as failures, can degrade the reliability of AI applications, necessitating strategies to mitigate risks associated with relying on a single provider. Using multiple providers and implementing failover strategies, such as automatic retries or rerouting based on status codes and latency thresholds, can enhance system resilience and performance. Portkey offers a solution to manage these complexities by providing a unified API that abstracts away the intricacies of handling different providers, thus ensuring reliable and seamless AI application performance. This platform allows for effective load balancing, conditional routing, and failover management without the operational overhead of developing custom infrastructure, making it an attractive option for teams looking to scale AI systems efficiently.
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