MLOps & experiment tracking · Fiddler AI, Inc.

Fiddler AI

Hosted AI observability and guardrails platform for monitoring deployed ML models and LLM/agent applications.

Visit Fiddler AI Alternatives

Fiddler AI is a production model-monitoring and observability platform, not an experiment tracker: it watches deployed predictive models and LLM/agent applications for drift, performance degradation, and quality issues, and adds real-time guardrails that screen for hallucinations, toxicity, PII/PHI exposure, prompt injection, and jailbreak attempts before responses reach users. It also provides explainability tooling to help teams understand individual model predictions, which sits alongside its core monitoring function. This places it in the same bucket as Evidently AI, Arize AI, and WhyLabs rather than with MLflow or W&B. Fiddler is delivered primarily as hosted SaaS, with VPC and on-premises deployment options for enterprise customers; a free tier covers basic real-time guardrails, a Developer tier bills per trace, and Enterprise adds custom infrastructure, support, and onboarding.

At a glance

Vendor Fiddler AI, Inc.
Pricing model Usage-based
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams needing both monitoring and real-time safety guardrails for LLM/agent applications in production.

Pricing

A free tier covers basic real-time guardrails; the Developer tier is billed per trace; Enterprise adds flexible deployment and support and is custom-quoted.

Plan Price Notes
Free $0 Real-time guardrails for hallucinations, toxicity, PII/PHI, prompt injection, and jailbreaks; under 80ms latency
Developer $0.002 per trace Adds unified AI observability for agentic and predictive systems, custom evaluators, RBAC and SSO, SaaS deployment
Enterprise Custom Adds enterprise-grade guardrails, flexible deployment (SaaS, VPC, or on-prem), dedicated Customer Success Manager

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Production drift and performance monitoring
  • Real-time LLM guardrails (hallucination, toxicity, PII/PHI, prompt injection)
  • Model explainability tooling
  • Agentic and predictive-system observability
  • Custom evaluators / bring-your-own-judge
  • Role-based access control and SSO
  • VPC and on-premises deployment options

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Evidently AI Open-source and cloud model-monitoring tool that checks deployed ML/LLM systems for drift, data quality, and performance issues. Arize AI Hosted AI observability platform that monitors deployed ML models and LLM applications for drift, quality, and performance issues. WhyLabs Formerly a hosted AI-observability platform for monitoring model and data drift; the company has since shut down and open-sourced its stack.

Fiddler AI in the index now

Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Hallucination When a language model generates confident, fluent output that is factually wrong or unsupported by its source material. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.