{ your data }

Capture datasets at the application level, fine tune best in class models, and deploy with our optimized inference engine.

OUR INVESTORS
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OUR INVESTORS
Training

Fine tuning made as simple as possible, but no simpler.

Some training jobs are harder than others. Use our out-of-the-box training hyperparameters, input your own, or reach out to our support team of ML engineers for task-specific optimizations.

Enqueued
Startup
Execution
Status
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0.0s
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09:34:10 AM
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Deployment

Easily deploy models for inference.

Each deployment is a custom Relace inference engine, tuned to perform optimally on your specific task. Just request a dedicated deployment on any number of GPUs, or use one of our autoscaling default deployments.

Capture

A competitive edge starts with only a few extra lines of code

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from openai import OpenAI

client = OpenAI(
    base_url="https://openai.log.relace.run/v1",
    api_key="RELACE_API_KEY",
)

messages = [
    {
        "role": "system",
        "content": "..."
    },
    {
        "role": "user",
        "content": "..."
    }
]

completion = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    extra_body={"relace-metadata": metadata_json}
)
from relace import OpenAI client = OpenAI( api_key= "OPENAI_API_KEY", relace_api_key= "RELACE_API_KEY", ) messages = [ { "role": "system", "content": "..." }, {"role": "user", "content": "..." } ] completion = client.chat.completions.create( model="gpt-4o", messages= messages, )
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from anthropic import Anthropic

client = Anthropic(
    base_url="https://anthropic.log.relace.run",
    api_key="RELACE_API_KEY",
)

system = "..."
prompt = "..."

completion = client.chat.completions.create(
    model="claude-3-5-sonnet-latest",
    system=system,
    messages=prompt,
    max_tokens=8192,
    extra_body={"relace-metadata": metadata_json}
)
import OpenAI from 'relace'; const client = new OpenAI({ apiKey: "OPENAI_API_KEY", relaceApiKey: "RELACE_API_KEY" }); const messages = [ { role: "system", content: "..." }, { role: "user", content: "..." } ]; const completion = await client.chat.completions.create({ model: "gpt-4o", messages: messages });

Own your LLMs

Walk us through the task, begin collecting data, and we'll help you build your custom model.