FriendliAI - Generative AI inference infrastructure with maximized performance

What is FriendliAI

If you've ever tried to deploy a large language model in production, you know the pain: GPU costs spiral out of control, inference latency frustrates your users, and managing infrastructure takes precious engineering time away from building actual AI features. These challenges are exactly why FriendliAI exists.

FriendliAI positions itself as The Generative AI Infrastructure Company—a platform designed to make deploying and running generative AI models not just possible, but genuinely affordable and performant for enterprises. The company has achieved what many thought impossible: delivering 2x+ faster inference speed compared to standard solutions, while cutting GPU costs by 50-90%.

How? Through a combination of custom GPU kernels, intelligent caching, continuous batching, speculative decoding, and parallel inference. These aren't just marketing terms—they're real technical innovations that FriendliAI's team (including a professor from Seoul National University and engineers from leading tech companies) has built from the ground up.

What sets FriendliAI apart is its dual approach: they've optimized both the infrastructure level (multi-cloud scaling, automatic failover) and the model level (custom kernels, quantization, speculative decoding). This means you get performance gains regardless of whether you're running open-source models or your own fine-tuned ones.

The platform integrates natively with Hugging Face, giving you access to 521,695 models that can be deployed with a single click. Companies like LG AI Research, SKT, ScatterLab, and NextDay AI already trust FriendliAI for their most demanding AI workloads.

FriendliAI's Core Features

You need your AI applications to be fast, reliable, and cost-effective. Here's how FriendliAI delivers on each of these fronts—and why thousands of developers consider it the best inference platform for production workloads.

Blazing-Fast Inference Engine is FriendliAI's crown jewel. By combining custom GPU kernels specifically optimized for inference workloads, intelligent caching to avoid redundant computations, continuous batching to maximize GPU utilization, and speculative decoding to predict tokens before they're needed, FriendliAI achieves speeds 3x faster than vLLM on comparable hardware. For Qwen3 235B, this translates to significantly lower latency for your end users and more efficient use of your GPU budget.

Guaranteed Reliability means you can sleep at night. The platform runs on a multi-cloud, multi-region architecture with active redundancy. If one region experiences issues, traffic automatically fails over to another—without any intervention from your team. FriendliAI backs this with a 99.99% uptime SLA on enterprise plans, which is critical for customer-facing applications where every second of downtime costs you money and trust.

Effortless Auto-Scaling handles the unpredictable nature of AI traffic beautifully. Whether you're dealing with viral product launches or steady enterprise usage, FriendliAI dynamically allocates GPU resources across its network to match demand in real time. NextDay AI processes 3 trillion tokens per month on this system without any manual intervention—that's the scale auto-scaling enables.

Powerful Model Tools give you visibility and control. The real-time dashboard shows latency, throughput, error rates, and token usage as they happen. You can update models without any downtime, rolling out improvements to production instantly. No more scheduled maintenance windows or risky deployments.

Simple Optimized Deployment means you don't need a team of MLOps engineers to get started. Quantization, speculative decoding, and performance tuning happen automatically when you deploy. Upload your model, and FriendliAI handles the optimization.

Enterprise-Grade Support includes dedicated Slack channels with engineering teams, hands-on assistance for complex migrations, and custom deployment configurations including VPC isolation and on-premise options. SOC 2 compliance is standard.

Who's Using FriendliAI

You might be wondering whether FriendliAI is right for your use case. The answer depends on what kind of AI application you're building—and the good news is that FriendliAI serves a remarkably diverse range of customers, from startups to Fortune 500 companies.

Conversational Chatbots are where FriendliAI truly shines. High-traffic chatbot applications face a brutal reality: each user conversation consumes significant GPU compute, and costs add up fast. NextDay AI processes 3 trillion tokens per month while reducing GPU costs by over 50%. ScatterLab's Zeta application handles 800 million conversations monthly with similar cost savings. If you're building a chatbot that needs to serve thousands or millions of users, these numbers should catch your attention.

Telecom AI Services have unique requirements: massive scale, strict SLAs, and reliability that can't be compromised. SKT, one of Korea's largest telecommunications companies, deployed FriendliAI's Dedicated Endpoints and achieved 5x LLM throughput improvement and 3x cost reduction within hours. This isn't a months-long migration—it was rapid deployment that immediately delivered results.

Document Processing and Analysis teams need consistent performance for extracting information from PDFs, contracts, and structured text. Upstage uses FriendliAI's Dedicated Endpoints with auto-scaling and automatic failover to stably process documents with their Solar Pro 22B model. Whether it's a slow trickle of requests or a batch of thousands, the system scales effortlessly.

Translation Services face particularly volatile traffic patterns—users around the world submit documents at unpredictable intervals. Upstage's Solar Mini 10.7B handles translation, chat, and document parsing with stable performance thanks to automatic scaling that responds to demand in real time.

Custom Model Deployment is ideal for companies that train their own models. TUNiB, a Korean AI company focused on language models, uses FriendliAI to offload infrastructure management entirely. Their engineers focus on model development and training, while FriendliAI handles GPU provisioning, scaling, and fault recovery automatically.

Enterprise AI Deployments with strict security and compliance requirements benefit from Reserved GPU instances, dedicated infrastructure, and the 99.99% SLA. If you need predictable capacity, guaranteed performance, and enterprise security controls, the Dedicated Endpoints with enterprise support are designed for you.

Technical Features

Understanding the underlying technology helps you appreciate why FriendliAI delivers such significant performance improvements over alternatives like vLLM. Let's dive into the technical innovations that make this possible.

Custom GPU Kernels are perhaps FriendliAI's most significant technical differentiation. Instead of using generic CUDA kernels provided by GPU vendors, FriendliAI's team has developed inference-specific kernels that are deeply optimized for LLM workloads. These kernels minimize memory bandwidth bottlenecks and maximize compute efficiency—translating directly to faster inference and lower costs.

Intelligent Caching dramatically reduces redundant computation. When users ask similar questions or when your application has common prompt prefixes, FriendliAI caches the computed results and KV caches. This means your GPU spends less time on repetitive work and more time generating unique responses. The impact on both latency and cost is substantial.

Continuous Batching represents a fundamental improvement over static batching. Traditional batch processing waits for a full batch before processing, leading to idle GPU time during low-traffic periods. Continuous batching processes requests as they arrive, dynamically adding them to the batch and removing completed requests immediately. The result: higher GPU utilization and lower latency, especially under variable load.

Speculative Decoding is an elegant technique that accelerates token generation. Rather than generating one token at a time sequentially, the system predicts multiple likely next tokens and validates them in parallel. When predictions are correct (which happens frequently with well-trained models), you get massive speedups. FriendliAI also offers N-gram speculative decoding as an additional optimization for certain model architectures.

Online Quantization compresses model weights to use less memory and compute while maintaining output quality. FriendliAI applies quantization automatically during deployment, so you don't need to be an expert in model optimization. The result: you can run larger models on the same hardware, or run the same models at lower cost.

Multi-Cloud Infrastructure means FriendliAI isn't locked into a single cloud provider. You can deploy across AWS, Oracle Cloud, and other providers, with automatic load balancing and failover. This also gives you geographic distribution for lower latency to users worldwide.

Supported GPU Options include the full range of modern NVIDIA hardware: B200 (192GB), H200 (141GB), H100 (80GB), and A100 (80GB). Different models have different memory requirements, and having access to the full spectrum means you can optimize for cost, speed, or capacity as your needs evolve.

FriendliAI's Pricing Plans

One of the most common questions about FriendliAI is simply: how much will it cost? The answer depends on your usage patterns, and FriendliAI offers three distinct pricing models to match different needs.

Serverless Endpoints (Pay-as-You-Go)

This model is ideal for applications with variable or unpredictable traffic. You pay only for the tokens you process, with no upfront commitment or infrastructure management.

For even more granular control, select models support per-second billing:

• Llama-4-Scout: $0.002/second
• Qwen3-32B: $0.002/second

This is excellent for development, testing, or applications where traffic fluctuates significantly.

Dedicated Endpoints (On-Demand)

For production applications requiring predictable performance, dedicated GPU instances provide consistent resources without noise from other tenants.

Enterprise Reserved instances offer significant discounts for committed usage. Reserve GPU capacity for 1 month or longer and receive preferential pricing—ideal for enterprises with predictable baseline workloads.

Container (Contact Sales)

For organizations with custom infrastructure requirements or specialized deployment needs, FriendliAI offers container-based deployment. Contact their sales team for pricing tailored to your specific requirements.

Frequently Asked Questions