How Kilo Runs Coding Agents up to 7× Faster with FriendliAI

Kilo Code is built around a simple idea: developers should be free to choose the models and infrastructure that best fit their workflows. Rather than locking teams into a single AI provider, Kilo is an all-in-one agentic engineering platform that enables developers to connect their preferred inference platforms and open models directly into their coding environment.

But model freedom is only meaningful if open models are a genuinely competitive choice: served fast enough to keep developers in flow, reliable enough for multi-step agent workflows, and priced for production scale. That is the role FriendliAI plays in the Kilo stack, delivering the performance, scalability, and cost efficiency that make open-weight models a first-class option for production coding agents.

Highlights:

• Up to 7× faster inference than several other providers, per Kilo's internal evaluations of GLM-5 usage
• Significantly reduced error rates, keeping multi-step agent workflows on track
• Core component of the Kilo stack for high-performance access to the latest open-weight models

Finding the fastest, most reliable provider for open models

Kilo wanted to give developers real freedom over their models by making open models a first-class choice. To deliver on that, open models had to feel as fast and dependable as their proprietary counterparts, which meant finding the fastest and most stable provider among inference companies.

Speed and reliability compound in agentic workflows. A coding agent chains dozens of model calls across planning, code generation, tool use, and debugging; slow responses break developer flow, and a single failed call can derail an entire multi-step task. Over the past year, Kilo Code tested several different inference providers hosting a range of both open and proprietary models, searching for faster inference and lower error rates.

The fastest inference for open models

In Kilo's internal evaluations of GLM-5 usage, FriendliAI consistently delivered up to 7× faster inference than several other providers while significantly reducing error rates.

FriendliAI sits directly within Kilo's orchestrator, optimized for real-time coding workloads. Through continuous batching (iteration batching) and memory optimization, Kilo Code can feed massive multi-file codebases into large models without degrading performance, running out of memory, or spiking costs.

Built for multi-step agent reliability

Coding agents are only as dependable as their weakest inference call. FriendliAI's significantly lower error rates keep Kilo's multi-step agent workflows on track from repository analysis and code generation to tool use and long-context reasoning, so tasks complete instead of stalling mid-run.

Day-0 access to the latest open models

As new open-weight models are released, FriendliAI provides Day-0 availability, so Kilo Code developers can put the latest models to work in their coding environment as soon as they ship without waiting for infrastructure to catch up.

FriendliAI is now a core component of the Kilo stack, with each layer handling what it's built for: Kilo Code orchestrating across models, and FriendliAI delivering production-grade inference that runs up to 7× faster with significantly fewer errors. For Kilo's developers, open, optimized AI development is no longer a trade-off between performance and reliability.

FriendliAI helps AI companies and enterprise teams turn foundation models into reliable production systems with optimized inference, autoscaling, and the operational resilience that customer-facing services demand.

Serve high-performance inference with FriendliAI.