Fallom

Fallom is a cutting-edge, AI-native observability platform designed specifically for managing large language model (LLM) and agent workloads. It empowers organizations to monitor and analyze every LLM call made in production environments through comprehensive end-to-end tracing. Key aspects captured include prompts, outputs, tool calls, tokens utilized, latency, and the cost associated with each call. Fallom is tailored for enterprises needing robust compliance measures, providing session, user, and customer-level context as well as detailed timing waterfalls for multi-step agents. Its enterprise-ready features include audit trails, logging, model versioning, and user consent tracking, ensuring adherence to regulatory standards. With a streamlined OpenTelemetry-native SDK, teams can easily instrument applications in just minutes, enabling real-time monitoring, rapid debugging, and precise cost attribution across various models, users, and teams.

Real-Time Observability

Fallom offers real-time observability for AI agents, allowing users to track every tool call in detail. This feature enables comprehensive analysis of timing, latency, and costs associated with LLM calls, fostering a deeper understanding of operational performance.

Cost Attribution

With Fallom, organizations can achieve full transparency over their AI-related expenditures. The platform allows users to track costs per model, user, and team, facilitating accurate budgeting and chargeback processes essential for financial accountability.

Compliance Ready

Fallom is engineered to meet stringent regulatory requirements, offering full audit trails that support compliance with frameworks such as the EU AI Act, SOC 2, and GDPR. Features include immutable logs, input/output tracking, model versioning, and consent management.

Session Tracking

This feature groups traces by session, user, or customer, providing complete context for every LLM interaction. It allows organizations to analyze user behavior and interactions, enhancing insights into application performance and user engagement.

Debugging Multi-Step Agents

Fallom is indispensable for teams developing multi-step agents, as it provides timing waterfalls that help identify latency issues within workflows. This allows engineers to optimize performance and improve user experience through better debugging.

Regulatory Compliance Management

Organizations operating in regulated industries can leverage Fallom to maintain compliance effortlessly. With comprehensive audit trails and user consent tracking, businesses can demonstrate adherence to necessary legal standards while managing LLM operations.

Cost Management in AI Deployments

With the cost attribution feature, companies can meticulously track their AI spending on a granular level. This empowers teams to identify cost-saving opportunities and allocate resources more effectively across various models and user groups.

Performance Monitoring and Evaluation

Fallom offers real-time dashboards for monitoring LLM usage, enabling teams to spot anomalies and performance issues before they escalate. This proactive approach helps maintain the reliability of AI systems and enhances overall operational efficiency.

What is Fallom?

Fallom is an AI-native observability platform that provides comprehensive monitoring and analysis of LLM and agent workloads, enabling organizations to optimize performance and ensure compliance.

How does Fallom ensure compliance?

Fallom includes features like audit trails, input/output logging, model versioning, and user consent tracking, all designed to help organizations meet regulatory standards such as GDPR and SOC 2.

Can Fallom be integrated easily?

Yes, Fallom utilizes a single OpenTelemetry-native SDK that allows teams to instrument their applications quickly and monitor usage with minimal overhead, typically in under five minutes.

What kind of analytics does Fallom provide?

Fallom delivers detailed analytics on LLM calls, including timing, cost attribution, performance metrics, and session tracking, which collectively allow organizations to gain deeper insights into their AI deployments.