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Enterprise Layer Intelligence
Categories/AI Orchestration & MLOps/Fiddler AI
Fiddler AI

Fiddler AI

Founded 2019

Monitor, evaluate, and enforce guardrails across enterprise artificial intelligence agents and models

Pricing model

Free Tier, Paid

You can use Fiddler AI to monitor, evaluate, and protect enterprise artificial intelligence agents and machine learning models in production. It checks incoming prompts and outgoing responses in real time to catch jailbreaks, sensitive data leaks, and toxic content before they reach users or internal networks. The system tracks accuracy, latency, and token consumption across your entire fleet of agents. You can run automated evaluations locally without calling external language models, maintain full compliance audit trails, and debug problematic behaviors across testing and deployment stages.

What Fiddler AI does

Block unauthorized prompt injections before they reach language modelsRedact confidential customer information from user queriesCalculate total cost of ownership for model evaluation pipelinesGenerate auditable compliance logs for internal risk reviewsTrack token consumption and latency metrics for active agentsDeploy local evaluation models directly inside your private networkUnder 80-millisecond in-environment policy enforcement and guardrailsInline redaction of sensitive PII, PHI, and credentials before reaching modelsLocal evaluation models that eliminate external foundation model API feesAudit logs capturing user triggers and enforcement events for compliance standardsFleet-wide monitoring dashboard tracking agent performance, actions, and token costsJailbreak and prompt injection defense blocking attacks in production

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price
FreeFree
Developer (per trace)$0.00 / mo

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Fiddler AI, AI observability, LLM guardrails, agent monitoring, model governance, MLOps, Centor models, prompt injection defense, jailbreak prevention, PII redaction, token tracking, enterprise AI, continuous evaluation, audit logging, model evaluation