LLMOps Observability
An All-in-One Platform for Your LLM Observability and Security Needs
The Fiddler AI Observability and Security platform is built to help enterprises launch accurate, safe, and trustworthy LLM applications. With Fiddler, you can safeguard, monitor, and analyze generative AI (GenAI) and LLM applications in production.
Metrics-driven LLMOps in Production Environments
Proactively Detect LLM Risks
Analyze Issues in Prompts and Responses
Pinpoint High-Density Clusters
Track Key Metrics with AI Observability Dashboards
The MOOD Stack: Empowering AI Observability for LLM Applications
The MOOD stack is the new stack for LLMOps to standardize and accelerate LLM application development, deployment, and management. The stack comprises Modeling, AI Observability, Orchestration, and Data layers.
AI Observability is the most critical layer of the MOOD stack, enabling governance, interpretability, and the monitoring of operational performance and risks of LLMs. This layer provides the visibility and confidence for stakeholders across the enterprise to ensure production LLMs are performant, accurate, safe, and trustworthy.
Industry Use Cases for LLMOps
Fiddler supports industry leaders to scale and build trust into their LLM deployments.
Featured Resources
Frequently Asked Questions
What is LLMOps?
LLMOps (Large Language Model Operations) is the practice of training, deploying, and monitoring large language models (LLMs) in real-world applications. It spans everything from prompt tracking and model evaluation to risk detection, compliance, and observability — ensuring LLMs are accurate, safe, and trustworthy in production.
What is the difference between generative AI and LLM?
Large language models (LLMs) use deep learning algorithms to analyze massive amounts of language data and generate natural, coherent, and contextually appropriate text. Unlike predictive models, LLMs are trained using vast amounts of structured and unstructured data and parameters to generate desired outputs. LLMs are increasingly used in a variety of applications, including virtual assistants, content generation, code building, and more.
Generative AI is the category of artificial intelligence algorithms and models, including LLMs and foundation models, that can generate new content based on a set of structured and unstructured input data or parameters, including images, music, text, code, and more. Generative AI models typically use deep learning techniques to learn patterns and relationships in the input data in order to create new outputs to meet the desired criteria.
What problems does LLMOps address?
LLMOps tackles the unique risks of deploying large language models in production — such as hallucinations, prompt injections, jailbreaks, unsafe outputs, and unpredictable behavior. It also helps track operational costs, monitor key performance metrics, and ensure regulatory compliance. By bringing observability, guardrails, and root cause analysis into one workflow, LLMOps enables teams to build safer, more reliable generative AI systems at scale.
What are Guardrails?
Guardrails are mechanisms designed to ensure the safe, reliable, and ethical operation of GenAI systems by preventing harmful, unsafe, and unintended outputs and maintaining trust in the GenAI system. It moderates LLM conversations and detects issues like hallucinations, toxic outputs, privacy violations (e.g., PII leakage), or prompt injection attacks in LLM applications, to minimize downstream impact.
How do guardrails work in LLMOps?
LLM guardrails are programmable rules that limit or block toxic, unsafe, or hallucinated prompts and responses before they reach the large language model or end user. Fiddler Guardrails provide low-latency moderation to prevent hallucinations, harmful content, or jailbreak attempts before they can cause damage to the organization's models, applications, or brand.
What types of metrics can be tracked for LLMOps?
A robust LLMOps observability platform offers dashboards to monitor key metrics such as:
- Jailbreak activity
- Response faithfulness (hallucination detection)
- Response toxicity (hateful, illegal, harassing, etc.)
- Costs linked to disliked or low-quality outputs
- PII exposure risks
- Model latency and throughput
- Domain specific metrics
Fiddler supports tracking 50+ out-of-the-box LLM metrics, as well as the creation of custom metrics for domain specific observability.
