Standard telemetry
Collect standardized telemetry from agent interactions across the lifecycle so teams can inspect what happened and where it happened.
Enterprise AI observability and security for production agentic systems, with evaluation, monitoring, guardrails, and governance.
Fiddler AI is an enterprise AI observability and security platform built as a control plane for AI agents. It combines telemetry, evaluation, monitoring, guardrails, and governance so teams can understand what agents are doing and enforce policy at runtime.
The product is aimed at organizations running agentic and predictive AI in production, including coding agents and multi-agent workflows. Its materials emphasize visibility across the agent hierarchy, root-cause analysis with execution context, and inline enforcement through the gateway an enterprise already runs.
Collect standardized telemetry from agent interactions across the lifecycle so teams can inspect what happened and where it happened.
Run evaluations during development to test agents before deployment, including experiments and quality checks.
Monitor production behavior with alerts, dashboards, and hierarchical visibility from application down to span.
Apply inline guardrails at the request and response path to block harmful inputs and outputs before they move through the system.
Keep complete evidence for review with an auditable trail across the agent fleet and support for governance workflows.
Analyze usage, spend, latency, throughput, and adoption by developer, model, and team to connect performance with operating cost.
Teams building customer-facing agents can evaluate behavior before launch, then monitor production for hallucinations, toxicity, PII/PHI, and jailbreak attempts.
Organizations with coding agents can use gateway-based inline enforcement to block sensitive data from reaching the model or leaving in responses.
Healthcare, financial services, and other regulated teams can retain audit trails and governance evidence for review and oversight.
Multi-agent systems can be monitored across the application, session, agent, trace, and span levels to reveal dependencies and failure points.
Platform teams can compare token usage, spend, latency, throughput, and adoption across developers, models, and teams to understand operational cost.
Fiddler positions its Control Plane as a system for standardized telemetry, reliable evaluation, continuous monitoring, enforceable policy, and auditable governance across the AI agent lifecycle. The source describes it as adding enforcement and governance, not just monitoring.
The source says it is meant for enterprises running AI agents in production, including coding agents and multi-agent workflows. It focuses on visibility, context, control, and runtime guardrails for agentic systems.
The product combines development-time evaluations, production monitoring, inline guardrails at the request and response path, and audit trails. It also exposes agent-level telemetry such as token usage, spend, latency, throughput, and adoption.
The pricing page shows a Free plan for real-time guardrails, a Developer plan priced at $0.002 per trace, and an Enterprise plan with contact sales pricing. The product pages also describe SaaS, VPC, and on-premise deployment options.
The source names Native OTel support and mentions compatibility with frameworks such as LangGraph, Amazon Bedrock, AWS Strands Agents, and Google ADK. It also says the Control Plane can integrate with the LLM gateway an organization already operates.
Traffic data is for reference only.
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