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AI Infrastructure & Serving

FriendliAI

FriendliAI is a cloud inference service for serving generative AI models through hosted Model APIs and Dedicated Endpoints on dedicated GPUs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

FriendliAI — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Model APIs when a curated open-weight model meets the application’s needs; use Dedicated Endpoints when the deployment requires a chosen or custom model on dedicated GPUs. The structured-output feature applies when downstream software needs responses conforming to a JSON schema.

Who it is for

  • Developers integrating hosted open-weight models into applications.
  • Teams deploying a selected or custom model on dedicated GPU infrastructure.
  • Builders who need schema-constrained JSON responses from a model API.

Documented uses

  • Send inference requests to a hosted open-weight model through a Model API.
  • Deploy a chosen or custom model on a Dedicated Endpoint with dedicated GPUs.
  • Request JSON-schema-constrained responses through the chat completions API.
Before you choose

Requirements & limits

The official documentation lists Model APIs and Dedicated Endpoints. It also describes Model APIs as serverless and pay-per-token; the evidence does not specify prices.

  • Model APIs offer a curated set of open-weight models; Dedicated Endpoints are the documented route for running any model, including a custom one.
  • Dedicated Endpoints use dedicated GPUs, while Model APIs are described as serverless.
  • JSON schema enforcement restricts invalid continuations but does not by itself ensure the model chooses the intended valid content; prompts and examples matter.
  • FriendliAI reports up to 21% higher throughput for response_format in its own Endpoints comparison, with most of the gain attributed to speculative decoding.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Guide structured responses with a JSON example and schema

Request a JSON-schema-constrained response and prompt the model toward the intended fields and values.

Before you start

  • A FriendliAI chat completions API request that supports the response_format field.
  • A JSON schema describing the expected response.
  • A prompt that explains the requested information and includes a concrete example JSON value.

Steps

  1. Add response_format with type set to json_schema and provide the schema describing the desired output.

  2. In the prompt, explain what information to generate and how it maps to the schema.

  3. Include an example JSON value in the prompt that demonstrates the intended structure and content.

  4. Submit the request and inspect whether the response follows the intended structure.

Expected outcome

The inference engine constrains generation to schema-valid continuations, while the example guides the model toward the intended structure and content.

Guide limitations

  • Schema enforcement prevents schema-invalid continuations but does not ensure that the model chooses the intended valid tokens; valid whitespace may still be generated.
  • The article's example schema and output are illustrative; it does not establish support for every schema keyword or guarantee results for other models or prompts.
  • The throughput results described apply to FriendliAI Endpoints and the tested setup; they are not a general performance guarantee.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
FriendliAISend inference requests to a hosted open-weight model through a Model API.Web, CloudThe official documentation lists Model APIs and Dedicated Endpoints. It also describes Model APIs as serverless and pay-per-token; the evidence does not specify prices.
InferoxyServe requests to Dockerized computer-vision models through supported bridges.Docker, Linux, Self-hosted, Server
VllmopsDefine model server settings such as model identifier, GPU selection, port, environment, and vLLM arguments.CLI, Server, Self-hostedThe documented installation requires Python 3.10 or later and supports Linux and macOS. The repository specifies the Apache-2.0 license.
Evidence and transparency

Sources & review

friendli.ai ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
24 October 2026
Original website title
FriendliAI | The Frontier AI Inference Cloud · Official homepage title · retained in its source language
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