AI inference is when a trained model processes real-time data to generate an output. As companies mature from using basic copilots to deploying AI-native systems, inference requires much lower latency and higher reliability.
Key takeaways
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This infographic is based on data from Akamai’s State of AI Inference report, which surveyed over 200 AI engineers and architects.
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Moving to AI-native infrastructure requires 61% of teams to achieve sub-100 ms response times.
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Teams are shifting to distributed cloud inference to place workloads closer to users and data.
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Workloads are scaling past employee automation into customer trust and fraud protection.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
Centralized inference routes all user requests to a single distant data center, causing lag. Distributed cloud inference deploys AI models across smaller servers located closer to users and data.
These are automated network management techniques. Screening analyzes incoming AI traffic for security, while steering automatically routes user requests to the most efficient cloud server to guarantee the fastest possible AI response.