Arpeggio AI
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
Fiddler AI is a premier enterprise-grade observability platform designed to bridge the gap between AI development and production-scale reliability. By 2026, Fiddler has solidified its position as the 'Trust Layer' for organizations deploying Large Language Models (LLMs) and traditional machine learning models. Its technical architecture centers on a centralized Model Commons, which allows data scientists and MLOps teams to track model performance, explainability, and fairness across fragmented environments. The platform excels in identifying 'silent killers' of AI performance, such as data drift and concept drift, using proprietary algorithmic approaches to attribution. For LLMs, Fiddler provides a robust evaluation framework that monitors for hallucinations, toxicity, and PII leakage in real-time. In the 2026 market, Fiddler differentiates itself through its deep integration with data warehouses like Snowflake and Databricks, offering a seamless workflow from training-time validation to production-time monitoring. Its focus on 'Responsible AI' makes it a critical tool for regulated industries—such as finance, healthcare, and insurance—ensuring compliance with evolving global AI acts and governance standards.
A centralized repository that stores model versions, schemas, and metadata to ensure consistency across the ML lifecycle.
Enterprise-grade observability and real-time guardrails for LLM-powered applications.
The open-source AI observability platform for LLM evaluation, tracing, and data exploration.
The lightweight toolkit for tracking, evaluating, and iterating on LLM applications in production.
The Intelligent AI Observability Platform for Enterprise Scale MLOps.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
An open-source library for auditing LLMs for robustness and safety before deployment.
Automated slices of data that identify exactly where a model is failing (e.g., specific regions or demographics).
Deep-learning specific explainability method that attributes model predictions to input features.
Real-time scoring of LLM responses for truthfulness, toxicity, and adherence to system prompts.
Tracks disparate impact and equal opportunity metrics across protected classes in real-time.
A synthetic data generation technique used to test model sensitivity without exposing real PII.
A bank's credit model starts denying loans to a specific demographic due to unforeseen drift.
Registry Updated:2/7/2026
Retrain and validate.
A customer support chatbot starts hallucinating incorrect refund policies.
An AI imaging tool shows performance degradation on new MRI machine data.