Benchmarking AI Gateways: Debunking the I/O-Bound Proxy Myth
The article challenges the common perception of AI gateways as thin, I/O-bound proxies. It presents a detailed benchmark of 5 open-source AI gateways, revealing that their performance is primarily CPU-bound due to various processing tasks, not just I/O forwarding.
Why it matters
This benchmark provides valuable insights into the performance characteristics of different AI gateway architectures, which is crucial for building scalable and efficient AI infrastructure.
Key Points
- 1AI gateways perform more than just proxying requests, including parsing, validation, routing, and response processing
- 2Benchmark results show different failure modes, such as linear scaling, cliff-like drops, CPU ceilings, and latency plateaus
- 3The author's own gateway, Ferro Labs, demonstrates linear scaling up to 1,000 concurrent users with low latency
Details
The article argues that the common mental model of AI gateways as simple I/O-bound proxies is incorrect. In reality, these gateways perform a range of CPU-intensive tasks on each request, including parsing JSON, validating API keys, checking rate limits, resolving routing rules, selecting upstream providers, mutating headers, parsing streaming responses, logging events, and updating usage meters. The author benchmarked 5 open-source AI gateways (Ferro Labs, Kong, Bifrost, LiteLLM, and Portkey) on a GCP n2-standard-8 instance, using a Go mock server with a fixed 60ms latency as the upstream. The results revealed 4 distinct performance patterns: linear scaling (Ferro Labs, Kong), cliff-like drops (Bifrost), CPU-bound ceilings (LiteLLM), and latency plateaus (Portkey). The author's own Ferro Labs gateway demonstrated linear scaling up to 1,000 concurrent users with low latency, highlighting the importance of understanding and addressing the CPU-bound nature of AI gateway workloads.
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