Fiddler AI vs NannyML

Fiddler AI provides observability and security for AI agents and predictive applications. It evaluates agents and models during development, then monitors them in production, with diagnostics spanning more than 100 built-in and custom metrics such as hallucination, toxicity, PII/PHI, drift, performance, and business KPIs. Real-time guardrails can detect risks and pause, reroute, or escalate agent actions. The platform records decisions, actions, evaluations, and policy outcomes to support audit evidence for requirements including GDPR, HIPAA, NAIC, and SR 11-7. It names LangGraph, Amazon Bedrock, and AWS Strands Agents as native framework support, and is built on OpenTelemetry standards. Deployment options include cloud SaaS, VPC, on-premises, and air-gapped environments. Fiddler says it is SOC 2 Type II certified, complies with HIPAA, and encrypts data in transit and at rest. Plans include Free at free, Developer at a price agreed with the maker billed per trace, and Enterprise with no listed price. The company was founded in 2018 and is headquartered in Palo Alto, California.

NannyML monitors deployed machine-learning models and estimates their performance when ground truth is delayed or unavailable. It detects concept drift as well as multivariate and univariate data drift, and connects alerts to changes in model performance. Data-quality checks and ranked drift alerts can help teams investigate features associated with performance issues. Users can relate model performance to monetary or other business outcomes. NannyML Cloud supports webhooks for retraining actions and an SDK for automating monitoring-data ingestion. Cloud is available through Azure and AWS marketplaces as SaaS or a managed application. With the managed-application option, the application and required infrastructure are provisioned within the customer's cloud subscription. For its Azure managed application, NannyML documents encrypted traffic and disks, but says automated backups were still being implemented and advises customers to safeguard model inputs and outputs elsewhere. The open-source library installs with pip or conda. Direct local dataset uploads through the Cloud UI must be smaller than 100 MB. Plans include a free self-managed Open Source option, Starter at $399 a month, and Scale at $999 a month.