Dedicated Endpoints
Build and run generative AI models on autopilot

Autopilot LLM endpoints for production

Easily create LLM inference endpoints that are performant, scalable, and cost-effective

Customer stories

SKT is South Korea's leading telecom operator known for its innovative mobile services, extensive 5G infrastructure and advancements in AI development.

CHALLENGE

Building and serving AI agents for SKT’s massive customer base required strict SLAs, high reliability, and the ability to efficiently handle heavy traffic.

SOLUTION

Friendli Dedicated Endpoints enabled exceptional reliability and traffic efficiency while reducing operational costs.

RESULT

Within
few hours of
onboarding

5×

LLM
throughput

3×

Cost
saving

Tunib's DearMate chatbot service offers various personas like friend, lover, counselor, and coach. Friendli Dedicated Endpoints' managed platform allows Tunib to focus on model training while automating GPU resource management and fault recovery.

Friendli Dedicated Endpoints simplifies generative AI model serving and optimizes our service development process.

FEATURES & BENEFITS

Superior cost-efficiency and performance with Friendli Engine

Build and serve custom models

Efficient and cost-effective serving with autoscaling

Dedicated GPU resource management

We are excited to announce that FriendliAI has been officially recognized as an Amazon Web Services (AWS) Partner.
Now you can find Friendli Dedicated Endpoints on AWS marketplace, making building and serving LLMs seamless and efficient.

Superior cost-efficiency
and performance

A performant LLM serving solution is the first step to operating your AI application in the cloud.

Compared to vLLM, we boast:

10x+ faster

token generation

5x+ faster

initial response time

Run Friendli Engine on the cloud to reduce LLM serving cost up to 90%.

Our engine achieves 6 times higher throughput. Serve more traffic on fewer GPUs with Friendli Engine.

Our engine generates tokens 10 times faster to guarantee unmatched efficiency and performance in your generative AI operations.

Custom model support

We offer comprehensive support for both open-source and custom LLMs, allowing organizations to deploy models tailored to their unique requirements and domain-specific challenges.With the flexibility to integrate proprietary datasets, businesses can unlock new opportunities for innovation and differentiation in their AI-driven applications.Create a new endpoint with your private Hugging Face Model Hub repository or upload your model directly to Dedicated Endpoints.

Dedicated GPU Resource Management

Friendli Dedicated Endpoints provides dedicated GPU instances ensuring consistent access to computing resources without contention or performance fluctuations.By eliminating resource sharing, organizations can rely on predictable performance levels for their LLM inference tasks, enhancing productivity and reliability.

Multi-LoRA serving on a single GPU

With our specialized optimization, you can serve multiple LoRA models on a single endpoint using just one GPU. Streamline your operations and maximize resource efficiency.Enjoy greater flexibility and performance as you customize your models with enhanced access and efficiency. Optimize your deployments while maintaining top-tier performance.

Train your model with Friendli Fine-tuning

Optimize your models using enterprise data to achieve business-specific goals. Friendli Fine-Tuning enhances performance, saving both time and resources.Seamlessly deploy your endpoints to serve inference requests, and maximize your business outcomes with tailored, optimized models.

Auto-scale your resources in the cloud

When deploying generative AI in the cloud, it’s important to scale as your business grows.Friendli Dedicated Endpoints employs intelligent auto-scaling mechanisms that dynamically adjust computing resources based on real-time demand and workload patterns.

Test your endpoints in the playground

Experiment with your model’s capabilities in the endpoint playground.Configure parameters like token length, temperature, top P, and frequency penalty.

PRICING