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

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

This is Part 2 of our series analyzing Portkey's critical insights from production LLM deployments. Today, we're diving deep into provider reliability data from 650+ organizations , examining outages, error rates, and the real impact of downtime on AI applications. From the infamous OpenAI outage to the daily challenges of rate limits, we'll reveal why 'hope isn't a strategy' when it comes to LLM infrastructure

šŸšØ LLMs in Production: Day 3

ā€œHope isnā€™t a strategy.ā€

When your LLM provider goes downā€”and trust us, it willā€”how ready are you?

Today, weā€™re sharing fresh data from 650+ orgs on LLM provider reliability, downtime strategies, and how to keep things running smoothly (whileā€¦

ā€” Portkey (@PortkeyAI) December 13, 2024

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Before that, hereā€™s a recap from Part 1 of LLMs in Prod:

ā€¢ @OpenAI dominance is eroding, with Anthropic slowly but steadily gaining ground

ā€¢ @AnthropicAI requests are growing at a staggering 61% MoM

ā€¢ @Google Vertex AI is finally gaining momentum after a rocky start.

Now,ā€¦ pic.twitter.com/4MjD63EWyJ

ā€” Portkey (@PortkeyAI) December 13, 2024

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Remember the OpenAI Outage?

In just one day, they reminded the world how critical they areā€”by taking everything offline for ~4 hours. šŸ˜›

But hereā€™s the thing: this wasnā€™t an anomaly.

Outages like these are a recurring pattern across ALL providers.

Which begs the question: whyā€¦ pic.twitter.com/HYNVeZlSpo

ā€” Portkey (@PortkeyAI) December 13, 2024

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šŸ“Š Over the past year, error spikes hit every providerā€”from 429s to 5xxs, no one was spared.

The truth?

Thereā€™s no pattern, no guarantees, and no immunity.

If youā€™re not prepared with multi-provider setups, youā€™re inviting downtime.

Reliability isnā€™t optionalā€”itā€™s tableā€¦ pic.twitter.com/MDpSfSrYft

ā€” Portkey (@PortkeyAI) December 13, 2024

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Rate Limit Reality Check:

ā€¢ @GroqInc : 21.11%

ā€¢ @Perplexity: 12.24%

ā€¢ @AnthropicAI : 5.60%

ā€¢ @Azure OpenAI: 1.74%

Translation: If you're not handling rate limits gracefully, you're gambling with user experience.

Your customers wonā€™t wait for infra to catch up. Are youā€¦ pic.twitter.com/GiJwXdPMuQ

ā€” Portkey (@PortkeyAI) December 13, 2024

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But rate limits are just the tip of the iceberg.

Server Error (5xx) rates this year:

ā€¢ Groq: 0.67%

ā€¢ Anthropic: 0.56%

ā€¢ Perplexity: 0.39%

ā€¢ Gemini: 0.32%

ā€¢ Bedrock: 0.28%

Even "small" error rates = thousands of failed requests at scale.

These arenā€™t just numbersā€”theyā€™reā€¦ pic.twitter.com/0CqdEGfYc0

ā€” Portkey (@PortkeyAI) December 13, 2024

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So, whatā€™s the solution?

The hard truth? Your users don't care why your AI features failed.

They just know you failed.

The key isnā€™t choosing the ā€œbestā€ providerā€”itā€™s building a system that works when things go wrong:

šŸ’” Diversify providers.

šŸ’” Implement caching.

šŸ’” Build smartā€¦

ā€” Portkey (@PortkeyAI) December 13, 2024

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6/ Why caching matters:

Performance optimization is critical, and hereā€™s where caching delivers results:

ā€¢ 36% average cache hit rate (peaks for Q&A use cases)

ā€¢ 30x faster response times

ā€¢ 38% cost reduction

Caching isn't optional at scaleā€”it's your first line of defense. pic.twitter.com/YX7YvwkmMS

ā€” Portkey (@PortkeyAI) December 13, 2024

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Thatā€™s it for today! Follow @PortkeyAI for more on LLMs in Prod Series

ā€” Portkey (@PortkeyAI) December 13, 2024

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https://t.co/54QiUNDZx2

ā€” Portkey (@PortkeyAI) December 13, 2024

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