LLMon 🍋

Detect AI Safety risks in your LLM application:
hallucinations, incorrect responses, toxicity and more.

Get early access

Why is LLM monitoring important?

Protect your LLM apps against AI Safety risks

Hallucinations

Factual errors made by LLMs lead to serious distrust in the model, and can have severe financial implications.

Toxicity issues

LLMs can generate discriminatory or ethically biased content, posing reputational risks & harming society.

Robustness issues

Answers can vary depending on LLM providers. LLMon allows you to log model versions and compare output quality.

Costs

Track tokens per request to optimize cost and latency

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import openai
import os

original_openai_api_key=os.environ.get("OPENAI_API_KEY")
openai.api_base = "https://llmon.giskard.ai/openai/v1"
openai.api_key = "gsk-<GENERATE_YOUR_TOKEN>" + "|" +
   original_openai_api_key

response = openai.Completion.create(
   model="text-davinci-003",
   prompt="Tell me a joke",
   temperature=0.7,
)

print(response)

Quick & easy to deploy

Get access to insights on your
LLM app performance in 2 lines of code.
‍
Developers can easily integrate our LLM monitoring solution, SaaS or on-premise.

Evaluate LLM quality in real-time

Stay ahead of anomalies & continuously monitor your LLM apps. Get insights to optimize the performance & safety of your LLMs.

Get early access

Why choose LLMon?

Built by LLM experts

We have a dedicated team of Machine Learning & Security researchers, in charge of red-teaming LLMs.

Simple deployment

Deploy on-premise to manage the infrastructure yourself, or opt for our SaaS to get started quickly.

Collaborative roadmap

We build in the open and can collaborate in design partnerships to customize LLM monitoring to your needs.

Backed by AI leaders

We're supported by a very particular set of investors, including Hugging Face's CTO and the EU Commission.

Compliance with GDPR

Our SaaS infrastructure is based in the EU, and our data collection & processing practices are GDPR compliant.

AI standard readiness

We are working members on the upcoming AI standards written by AFNOR, CEN-CENELEC, and ISO, at global level.

Listed by Gartner

AI Trust, Risk and Security

Enable AI Observability in your LLMOps stack

Data type

Tabular

LLMs, NLP

coming soon: Computer vision

Model type

Classification

Regression

Copy to clipboard

import openai
import os

original_openai_api_key=os.environ.get("OPENAI_API_KEY")
openai.api_base = "https://llmmon.giskard.ai/openai/v1"
openai.api_key = "gsk-<GENERATE_YOUR_TOKEN>" + "|" +
   original_openai_api_key

response = openai.Completion.create(
   model="text-davinci-003",
   prompt="Tell me a joke",
   temperature=0.7,
)

print(response)

Get access to insights in your
LLM performance in a single line of code.
‍
Developers can easily
integrate our monitoring solution.

LLMOps encompasses the entire lifecycle of Large Language Models, from training and versioning to orchestration, deployment, and ongoing monitoring.
‍
An LLM app is a software application powered by a Large Language Model. It performs tasks like text generation and chat support, offering a versatile solution for automating text-based processes.
‍
LLMon provides a robust defense against potential pitfalls like hallucinations, ethical biases, and inaccuracies.
As an observability tool, it tracks performance, and output quality. Enable observability for your LLM by logging, drilling down, and controlling the usage of your LLM application in your company.

Built to integrate with any LLM

coming soon

Start free today

Try it out now & get access to free usage credits

Monitor your model's performance
Make your LLMs robust, reliable & ethical
Optimize quality and compute for your LLM apps

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Join the community

This is an inclusive place where anyone interested in ML Quality is welcome! Leverage best practices from the community, contribute new tests, build the future of AI safety standards.