Scale on FriendliAI,
Get Up to $50,000 Inference Credit

Scale from proof of concept to production on FriendliAI with infrastructure built for high-performance inference. Get the speed, reliability, and efficiency you need to grow—plus up to $50,000 in inference credits to get started.

When your traffic grows, your inference stack needs to keep up

Many teams can get to prototype quickly, but production scale introduces new constraints: latency spikes, throughput ceilings, infrastructure complexity, and rising cost. FriendliAI helps teams scale open-model inference with the performance, reliability, and efficiency needed for real production demand—without forcing major application changes.

Up to $10,000 in free GPU inference credits

Sub-second latency, even at scale

Traffic-aware autoscaling

Over 400,000 Hugging Face and custom model support

No setup, no maintenance

Quick onboarding, technical support included

Lower cost as you scale
Inference economics matter more at production volume. FriendliAI is designed to improve cost efficiency as usage grows, helping teams serve more tokens, support more traffic, and reduce infrastructure waste without compromising performance.
Performance that holds up under load
FScaling is not just about handling more requests—it is about maintaining responsiveness as concurrency rises. FriendliAI is optimized for high throughput and low latency so teams can deliver better user experiences under real production traffic.
Built for production agentic workloads
Agentic applications need more than model access—they need reliable structured outputs, predictable function calling, and stable performance in production. FriendliAI supports modern open models such as Qwen, DeepSeek, and GLM so teams can scale agentic applications with confidence.
Start scaling without a rebuild
FriendliAI is compatible with Anthropic and OpenAI APIs, so teams can move quickly from evaluation to production rollout without reworking their application stack. That means less migration overhead—and a faster path to higher-performance inference at scale.
FriendliAI vs vLLM
We benchmarked Qwen3 235B on FriendliAI’s platform and compared it against a platform built on vLLM.
The results show 3x higher throughput and better efficiency benefits that matter most when you have high inference volume and tight latency requirements
Same Capability, Lower Cost
Teams using OpenAI or Anthropic are already running inference at scale — which means costs add up quickly.
Faster throughput, lower latency
FriendliAI outperforms OpenAI and vLLM-based systems in both throughput and latency.
Ready for agentic apps
FriendliAI provides stable, reliable function-calling APIs for models like Qwen, DeepSeek, and GLM, ensuring predictable structured outputs, allowing teams to build and run agentic applications seamlessly.
Switch with minimal effort
Migration is simple and fast. FriendliAI is OpenAI-compatible, so most teams can switch with as little as three lines of code.
FriendliAI vs vLLM
We benchmarked Qwen3 235B on FriendliAI’s platform and compared it against a platform built on vLLM.
These results illustrate that FriendliAI delivers superior throughput and efficiency on large-scale MoE models like Qwen3 235B. In particular, long-output scenarios benefit significantly.
Same Capability, Lower Cost
Teams using OpenAI or Anthropic are already running inference at scale — which means costs add up quickly.
Faster throughput, lower latency
FriendliAI outperforms OpenAI and vLLM-based systems in both throughput and latency.
Ready for agentic apps
FriendliAI provides stable, reliable function-calling APIs for models like Qwen, DeepSeek, and GLM, ensuring predictable structured outputs, allowing teams to build and run agentic applications seamlessly.
Switch with minimal effort
Migration is simple and fast. FriendliAI is OpenAI-compatible, so most teams can switch with as little as three lines of code.
FriendliAI vs vLLM
We benchmarked Qwen3 235B on FriendliAI’s platform and compared it against a platform built on vLLM.
These results illustrate that FriendliAI delivers superior throughput and efficiency on large-scale MoE models like Qwen3 235B. In particular, long-output scenarios benefit significantly.

Built for production inference.
Ready for real traffic.

Already running open models?

Scale on FriendliAI for 99.99% reliability, stronger performance under real traffic, and infrastructure designed to support production growth.

Are closed models too costly at scale?

Models like GLM-5, Kimi, DeepSeek, NVIDIA Nemotron, and Qwen often provide comparable quality, for a fraction of the cost.

What you Get
Up to $50K inference credit based on your
current inference spend
99.99% reliability, 3x throughput,
and 50–90% cost savings.
Access to 500,000+ Hugging Face
and custom models
Migrate in minutes
What You Provide
Your contact information
Company / employer
A recent invoice or bill from your current inference provider
No migration required before approval.

3 Quick Steps

First

Submit the form with your details and current provider bill

Second

We review and approve your credit amount

Third

Start running inference on FriendliAI using your credits

"Friendli Inference has enabled us to scale our operations cost-efficiently, allowing us to process over trillions of tokens each month with exceptional efficiency while cutting our GPUs by 50%. The performance and cost savings consistently exceed our expectations. After exploring open-source options, I cannot overstate the value and peace of mind FriendliAI brings to the table. It has become essential to driving our growth."

FriendliAI Customer
NextDay AI

"EXAONE models run incredibly fast on FriendliAI’s inference platform, and users are highly satisfied with the performance. With FriendliAI’s support, customers have been able to shorten the time required to test and evaluate EXAONE by several weeks. This has enabled them to integrate EXAONE into their services more quickly, accelerating adoption and driving real business impact."

Clayton Park
AI Business Team Lead, LG AI Research