Agents do a lot. FriendliAI Inference stays ahead.
Open-weight models now power the next generation of AI agents. FriendliAI keeps every token, tool call, and agent loop moving fast and reliably in production.
Why teams choose FriendliAI
Built for production AI agents
Production agents don't stop after one response. Every step adds latency—and every failure can interrupt the entire workflow. FriendliAI is engineered to keep agent loops fast, reliable, and moving in production.
Keeps agent loops moving
Low-latency token streaming prevents delays from compounding across long-running workflows.
Reliable long-context inference
Maintains fast, stable inference across large codebases, long context windows, and extended agent workflows.
Robust tool calling under load
Ensures structured outputs and function calls remain consistent as production traffic scales—not just in controlled demos.
How teams scale with FriendliAI
Learn how leading companies achieve unmatched performance, scalability, and reliability with FriendliAI
FriendliAI was consistently 7 times faster and had a significantly lower error rate.
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Rock-solid reliability with ultra-low tail latency.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Fluctuating traffic is no longer a concern because autoscaling just works.
Friendli Engine is an irreplaceable solution for generative AI serving.
FriendliAI was consistently 7 times faster and had a significantly lower error rate.
Our custom model API went live in about a day with enterprise-grade monitoring built in.
Rock-solid reliability with ultra-low tail latency.
Scale to trillions of tokens with 50% fewer GPUs, thanks to FriendliAI.
Fluctuating traffic is no longer a concern because autoscaling just works.
Friendli Engine is an irreplaceable solution for generative AI serving.
Why we're different
We pioneered the industry standard. Then kept improving it.
Our founder and CEO, Byung-Gon Chun, led the Seoul National University research team behind continuous batching, introduced in the Orca paper (OSDI 2022). Today, continuous batching has become the industry standard across modern inference engines.
Performance doesn’t come from one optimization.
Our work continues beyond a single innovation. FriendliAI optimizes the entire inference stack—from scheduling and kernels to caching, decoding, and serving—to keep production workloads fast, efficient, and reliable at scale.
Continuous Batching (aka iteration batching)
Schedules requests to maximize throughput and GPU utilization under dynamic production workloads.
Custom Kernels
Purpose-built kernels optimized for frontier model architectures and techniques, including quantization, MoE, and LoRA.
Speculative Decoding
Drafts and verifies tokens in parallel to accept more per forward pass — tuned for agentic and coding workloads.
KV Cache Optimization
Reuses previously computed context instead of recalculating it, keeping inference fast and cost-efficient even as inputs grow longer.
Reliable serving
Combines automatic failover, cache-aware routing, and auto-scaling to keep inference performant under GPU failures and traffic spikes.
Run the model your agents need
Explore the frontier model that fits your workload
- Based on Kilo's internal GLM-5 evaluation across providers.
- Due to higher throughput.
