Observability platforms track what happened across your AI pipeline for operational monitoring. InferTrust™ proves what happened at the moment of inference for
AI observability platforms like Datadog, Weights & Biases, MLflow, and Arize provide valuable operational visibility into model performance, data drift, prediction distributions, and system health. These tools answer the question: "How is our AI performing?"
InferTrust™ answers a different question: "Can you prove what the AI decided?" The distinction is between operational monitoring and evidentiary proof. Observability tells you what your system is doing. InferTrust™ proves what a specific inference produced at a specific moment, with cryptographic certainty that the record has not been altered.
Observability platforms are essential for detecting model degradation, data drift, and operational issues. They help engineering teams maintain model quality and system reliability. These are operational concerns that InferTrust™ does not address.
InferTrust™ addresses a separate set of concerns: regulatory compliance, litigation defense, and audit readiness. When a regulator asks you to prove which model version made a specific credit decision, your observability dashboard cannot provide cryptographic proof. When a plaintiff's attorney challenges the integrity of your AI decision records, your monitoring tools cannot demonstrate tamper-evidence.
Most organizations deploying AI in regulated environments will use observability tools for operations and InferTrust™ for compliance. The two serve different stakeholders (engineering vs. legal/compliance), answer different questions (how is it performing vs. can you prove what it decided), and provide different guarantees (operational visibility vs. cryptographic proof).