Fiddler AI
MLOps platforms
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
Take the quiz to check if Fiddler AI and its alternatives fit your requirements.
Pay-as-you-go
Free TrialFree version
Small
Medium
Large
- Banking and insurance
- Healthcare and life sciences
- Retail and wholesale
What is Fiddler AI
Fiddler AI is an MLOps platform focused on monitoring, explainability, and governance for machine learning models in production. It is used by data science, ML engineering, and risk/compliance teams to track model performance, detect drift and anomalies, and generate explanations for predictions. The product emphasizes model observability and responsible AI workflows rather than end-to-end data preparation or model training.
Strong model monitoring focus
The platform centers on production model observability, including performance tracking and data/model drift detection. It supports ongoing monitoring workflows that help teams identify issues after deployment. This focus is useful for organizations that already train and deploy models elsewhere but need dedicated monitoring and governance capabilities.
Explainability and diagnostics tools
Fiddler AI provides model explainability features intended to help users understand drivers of predictions and investigate model behavior. These capabilities support debugging, stakeholder communication, and review processes for higher-risk use cases. The emphasis on interpretability differentiates it from broader platforms where explainability may be less central.
Governance-oriented workflows
The product is positioned for responsible AI needs such as model risk management and audit support. It helps teams document and review model behavior over time, which can be important in regulated environments. This governance orientation complements organizations that use separate tools for data science development and deployment.
Not an end-to-end stack
Fiddler AI primarily addresses monitoring, explainability, and governance rather than full lifecycle development. Organizations may still need separate tools for data preparation, feature engineering, training, and experiment tracking. This can increase integration work compared with broader platforms that bundle more of the ML lifecycle.
Integration effort varies by stack
Deploying monitoring and explainability typically requires connecting to existing model serving, data pipelines, and logging systems. The amount of engineering effort depends on the organization’s infrastructure and model types. Teams should validate supported deployment patterns and data access requirements during evaluation.
Best fit for production maturity
The value is highest when models are already deployed and there is a need for ongoing oversight and governance. Early-stage teams that are still experimenting may find fewer immediate benefits compared with tools optimized for iterative development. Budget and operational ownership (ML engineering vs. risk/compliance) can also affect adoption.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Free | $0.00 (permanently free) | Real-time guardrails to detect hallucinations, toxicity, PII/PHI, prompt injection and jailbreak attempts; latency <100ms; powered by Fiddler Trust Models. (CTA: "Run free guardrails") |
| Developer | $0.002 per trace | Everything in Free, plus unified AI observability (tests & experiments) for agentic and predictive systems; custom evaluators / bring-your-own-judge; visualization-driven insights; role-based access control and SSO; SaaS deployment. |
| Enterprise | Custom (contact sales) | Everything in Developer, plus enterprise-grade guardrails, infrastructure scalability, flexible deployment options (SaaS, VPC, on-premise), white-glove support, named Customer Success Manager and customized onboarding. |
Seller details
Fiddler Labs, Inc.
Palo Alto, California, United States
2018
Private
https://www.fiddler.ai/
https://x.com/fiddlerlabs
https://www.linkedin.com/company/fiddler-labs
