Fallom

Fallom is an innovative AI-native observability platform developed specifically for large language model (LLM) and agent workloads. It empowers organizations by providing unprecedented visibility into LLM operations, allowing users to track every LLM call in production. This visibility is achieved through comprehensive end-to-end tracing, which captures essential data points, including prompts, outputs, tool calls, tokens, latency, and per-call costs. The platform is designed for businesses that leverage AI agents, enabling them to effectively monitor and optimize their LLM usage. Fallom's deep insights into user and session-level contexts help teams understand performance metrics and usage patterns. Additionally, it meets enterprise compliance needs with features such as robust logging, model versioning, and consent tracking. With a single OpenTelemetry-native SDK, teams can instrument their applications in just minutes, facilitating live monitoring, rapid debugging, and effective cost attribution across various models, users, and teams.

Comprehensive LLM Call Tracing

Fallom offers real-time observability for AI agents by enabling teams to track and analyze every LLM call. This feature allows users to debug confidently and understand the timing and costs associated with each call, enhancing overall operational efficiency.

Cost Attribution and Transparency

With Fallom, organizations can effectively track their spending across different models, users, and teams. This feature delivers full cost transparency, making budgeting and chargeback processes seamless and accurate.

Enterprise-Grade Compliance

Fallom is equipped with compliance-ready capabilities that provide complete audit trails to support regulatory requirements. Features include input/output logging, model versioning, and user consent tracking, ensuring that organizations meet standards such as GDPR and the EU AI Act.

Real-time Monitoring and Session Tracking

The platform enables live monitoring of LLM usage, allowing teams to spot anomalies before they escalate into serious incidents. Additionally, session tracking groups traces by user or customer, providing complete context for performance analysis.

Optimizing AI Workflows

Organizations can utilize Fallom to optimize their AI workflows by analyzing LLM call data, identifying bottlenecks, and improving response times. This leads to enhanced efficiency in operations involving AI agents.

Ensuring Compliance in AI Deployments

Fallom's robust compliance features make it ideal for organizations operating in regulated industries. Businesses can maintain compliance with data protection regulations while ensuring that their AI systems are transparent and accountable.

Cost Management in AI Operations

Companies can leverage Fallom to gain insights into their LLM usage costs. By tracking expenses on a per-model and per-user basis, organizations can make informed budgeting decisions and manage their AI investments effectively.

Debugging and Performance Enhancement

Fallom's real-time monitoring capabilities allow teams to debug issues quickly and enhance the performance of their AI agents. By identifying latency problems and performance regressions, organizations can ensure a smoother user experience.

What industries can benefit from using Fallom?

Fallom is tailored for organizations that rely on AI agents across various industries, including finance, healthcare, retail, and technology, enabling them to optimize their AI operations and ensure compliance.

How quickly can I integrate Fallom into my existing systems?

With Fallom's OpenTelemetry-native SDK, teams can set up and instrument their applications in under five minutes, allowing for rapid integration and immediate start of live monitoring.

What compliance standards does Fallom support?

Fallom is designed to meet various compliance standards, including GDPR, the EU AI Act, and SOC 2, providing organizations with the necessary tools to maintain regulatory compliance in their AI operations.

Can Fallom help with debugging AI models?

Yes, Fallom provides features that allow teams to debug their AI models efficiently. With real-time monitoring and session tracking, users can quickly identify latency issues and performance regressions, leading to improved model performance.