Fiddler AI

Fiddler AI provides enterprise teams with unified observability for ML and LLM systems to monitor performance, interpret decisions, and enforce trust.

Specialization:
Generative AI, LLM Monitoring, ML Model Monitoring, Explainable AI, Responsible AI Governance, Agentic Observability, Vector Embedding Monitoring

Company Type:
Startup

Total Financing Amount:
$63.8M

Founded Date:
2018

Description

Fiddler’s platform delivers continuous monitoring, explainability, fairness metrics, and behavioral traceability for machine learning and large language models in production. It integrates with telemetry tooling like OpenTelemetry and frameworks like Amazon SageMaker and LangGraph, enabling root‑cause analysis of agent or model outputs. Its dashboards and compliance reports serve data science, LLMOps, governance, and risk teams across finance, government, consumer lending, and digital marketplaces.

Customers

IASTideUS Navy PMS

What Problem Does Fiddler AI Solve?

When AI systems behave unpredictably, teams lack visibility into the underlying cause, making it hard to catch performance issues, bias, or model drift. This results in lost user trust, regulatory exposure, and delays in resolving incidents. Fiddler AI solves this by providing real-time observability, root-cause diagnostics, and fairness monitoring across ML and LLM pipelines, allowing enterprises to track, explain, and govern their models in production.

Pros

• Model Performance Visibility:
  Fiddler offers explainable AI dashboards that surface how models make predictions and why performance may drift, this transparency helps businesses detect bias and improve outcomes.
• Real-Time Monitoring:
  The platform enables continuous monitoring across training and production models. Alerts and diagnostics are built-in to catch issues before they affect operations.
• Enterprise Integration:
  Supports flexible integration across cloud environments and model types, reducing vendor lock-in.

Cons

• Narrow Focus Area:
  Primarily addresses explainability and monitoring, limiting utility for broader AI development needs.
• High Setup Complexity:
  Integrating and configuring the monitoring stack can require ML engineering support, out-of-the-box ease of use remains a challenge for lean data teams.
• Limited Out-of-the-Box Templates for Specific Use Cases:
  Teams often need to build custom monitors and explainability workflows from scratch, increasing time to value for smaller AI teams.

Investors

Isai, Samsung NEXT, Lightspeed Venture Partners, Cisco Investments, Insight Partners, Lux Capital

Last updated: April 20, 2026

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