Improve Your
LLM Applications
in Production

Optimize LLM applications by tuning prompts and models

Optimize LLM applications by tuning prompts and models

AI-Powered LLMOps for Developers

AI-Powered LLMOps for Developers

From development to production across data management, evals & fine-tuning.

Prompt Engineering Copilot

Use our Prompt Engineering Copilot to get to more accurate prompts faster via AI-powered tuning of prompts and models

Prompt Engineering Copilot

Use our Prompt Engineering Copilot to get to more accurate prompts faster via AI-powered tuning of prompts and models

Prompt Engineering Copilot

Use our Prompt Engineering Copilot to get to more accurate prompts faster via AI-powered tuning of prompts and models

Prompt Engineering Copilot

Use our Prompt Engineering Copilot to get to more accurate prompts faster via AI-powered tuning of prompts and models

Evaluate

Integrate Log10's llmeval tool to iterate even faster during development & continuously monitor the accuracy of your LLM apps in production

Evaluate

Integrate Log10's llmeval tool to iterate even faster during development & continuously monitor the accuracy of your LLM apps in production

Evaluate

Integrate Log10's llmeval tool to iterate even faster during development & continuously monitor the accuracy of your LLM apps in production

Evaluate

Integrate Log10's llmeval tool to iterate even faster during development & continuously monitor the accuracy of your LLM apps in production

AutoFeedback

Scale human review of LLM outputs with the power of Log10's AutoFeedback solution. Read the technical details

AutoFeedback

Scale human review of LLM outputs with the power of Log10's AutoFeedback solution. Read the technical details

AutoFeedback

Scale human review of LLM outputs with the power of Log10's AutoFeedback solution. Read the technical details

AutoFeedback

Scale human review of LLM outputs with the power of Log10's AutoFeedback solution. Read the technical details

Debug, Compare and Improve Prompts & Models

Debug, Compare and Improve Prompts & Models

Logs

Stats Get latency, cost & stats for each request

Feedback Collect feedback for model fine-tuning

Organize with full text search, tags and filters

Create playgrounds from logs improve accuracy with new prompts and models

Metrics

Operational Summary metrics on costs, usage and SLA

Accuracy Track accuracy of completions (coming soon)

Playgrounds

Compare Compare in one view prompts from OpenAI and Anthropic

Debug Integrated with logging and tracing for fast debugging

Collaboration Build for multi user collaboration from the start

OpenAI & Anthropic Configure and connect to model vendors in one place including to your fine-tuned models

AutoPrompt Get to the perfect prompt faster with AI-powered prompt tuning

Evaluations

llmeval GitHub CI/CD app and cli to systematically test prompts with metric, tool, and model-based evaluations

AutoFeedback Scale human feedback with custom evaluation models

Integrate Log10 with a single line of code

Integrate Log10 with a single line of code

Easy Programmatic Integration

Easy Programmatic Integration

OpenAI

Llama-3 (Self-hosted)

Anthropic

Log10

Langchain

Just call log10(openai) and use the OpenAI client library as before
import openai

from log10.load import log10
log10(openai)


client = openai.OpenAI()

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "system",
            "content": "You are the most knowledgable Star Wars guru on the planet",
        },
        {
            "role": "user",
            "content": "Write the time period of all the Star Wars movies and spinoffs?",
        },
    ],
)

print(completion.choices[0].message)

OpenAI

Llama-3 (Self-hosted)

Anthropic

Log10

Langchain

Just call log10(openai) and use the OpenAI client library as before
import openai

from log10.load import log10
log10(openai)


client = openai.OpenAI()

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "system",
            "content": "You are the most knowledgable Star Wars guru on the planet",
        },
        {
            "role": "user",
            "content": "Write the time period of all the Star Wars movies and spinoffs?",
        },
    ],
)

print(completion.choices[0].message)

OpenAI

Llama-3 (Self-hosted)

Anthropic

Log10

Langchain

Just call log10(openai) and use the OpenAI client library as before
import openai

from log10.load import log10
log10(openai)


client = openai.OpenAI()

completion = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "system",
            "content": "You are the most knowledgable Star Wars guru on the planet",
        },
        {
            "role": "user",
            "content": "Write the time period of all the Star Wars movies and spinoffs?",
        },
    ],
)

print(completion.choices[0].message)

Ready to Optimize LLM Accuracy at Scale?

Ready to Optimize LLM Accuracy at Scale?

Ready to Optimize LLM Accuracy at Scale?

Ready to Optimize LLM Accuracy at Scale?