LLMs in Prod: The Reality of AI Outages, No LLM is Immune

Part 2 of the Portkey series on large language model (LLM) deployments explores provider reliability data from over 650 organizations, highlighting the critical nature of infrastructure readiness in the face of recurring outages and error spikes across providers like OpenAI, Anthropic, and Google Vertex AI. It discusses the impact of rate limits on user experience, emphasizing that customers are intolerant of downtime, and reports error rates that translate into thousands of failed requests at scale. The article underscores that selecting a provider isn't as crucial as implementing strategies to mitigate failures, such as diversifying providers, incorporating caching, and building robust systems to maintain functionality amid disruptions. Caching is particularly highlighted for its role in performance optimization, with benefits including faster response times and cost savings, positioning it as an essential component in managing LLM infrastructure effectively.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.