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Friendli Dedicated Endpoints, Serverless Endpoints, and Container are OpenAI-compatible.
Existing applications can migrate with minimal effort, still using the official OpenAI SDKs.

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Specify the base URL and API Key

Initialize the OpenAI client using Friendli’s base URL and your Personal API Key.
  • Serverless Endpoints: https://api.friendli.ai/serverless/v1.
  • Dedicated Endpoints: https://api.friendli.ai/dedicated/v1.
  • Container: your own container’s URL (e.g., http://HOST:PORT/v1).
Get your Personal API Key in Friendli Suite > Personal Settings > API Keys. 
client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://api.friendli.ai/serverless/v1",
)

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Usage

Choose any model available on Friendli Serverless Endpoints, Dedicated Endpoints, or Container.

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Completions API

Generate text completions using a simple prompt-based approach. 
from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://api.friendli.ai/serverless/v1",
)

completion = client.completions.create(
    model="meta-llama-3.3-70b-instruct",
    prompt="Tell me a funny joke about programming.",
    max_tokens=100,
    temperature=0.7,
)
print(completion.choices[0].text)

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Chat Completions API

Generate chat completions using a conversational message-based approach. 
from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://api.friendli.ai/serverless/v1",
)

completion = client.chat.completions.create(
    model="meta-llama-3.3-70b-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a funny joke."},
    ],
    stream=False,
)
print(completion.choices[0].message.content)

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Streaming Mode

Receive responses in real-time, enabling better user experience for long responses. 
from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://api.friendli.ai/serverless/v1",
)

stream = client.chat.completions.create(
    model="meta-llama-3.3-70b-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a funny joke."},
    ],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)
Last modified on April 17, 2026