Fallom

About Fallom

Fallom is an AI-native observability platform designed specifically for monitoring and optimizing large language model (LLM) and agent workloads. It provides organizations with unparalleled visibility into every LLM call in production, offering end-to-end tracing that encompasses prompts, outputs, tool calls, tokens, latency, and the cost associated with each interaction. This level of detail is crucial for developers, data scientists, and operational teams who require real-time insights to understand LLM performance and troubleshoot issues effectively. Fallom enhances compliance with enterprise-ready features such as session/user/customer-level context, timing waterfalls for multi-step agents, and comprehensive audit trails that include logging, model versioning, and consent tracking. By utilizing a single OpenTelemetry-native SDK, teams can set up monitoring in minutes, gaining the ability to live monitor usage, debug issues quickly, and allocate spending accurately across models, users, and teams.

Features of Fallom

Real-Time Observability

Fallom provides real-time monitoring of AI agent operations, allowing teams to track tool calls and analyze timing effectively. This feature empowers users to debug issues with confidence, ensuring that every interaction is transparent and manageable.

Cost Attribution

With detailed cost attribution, Fallom enables organizations to track spending per model, user, and team. This full cost transparency is essential for budgeting and chargeback processes, helping teams manage their AI-related expenditures efficiently and effectively.

Compliance Ready

Fallom ensures that organizations meet regulatory requirements with its full audit trails, which support compliance with standards such as the EU AI Act, SOC 2, and GDPR. Features like input/output logging and user consent tracking are built in to assist with regulatory reporting and compliance.

Session Tracking

The session tracking feature allows users to group traces by session, user, or customer, providing complete context for each interaction. This capability is crucial for understanding user behavior and for troubleshooting issues related to specific sessions or users.

Use Cases of Fallom

Debugging Complex Workflows

Fallom is ideal for debugging complex, multi-step workflows in AI applications. By visualizing timing waterfalls and tracing individual tool calls, teams can identify bottlenecks and optimize performance effectively.

Cost Management

Organizations can leverage Fallom to manage and analyze costs associated with their LLM operations. By tracking spending across various models and users, finance teams can make informed budgeting decisions and allocate resources more effectively.

Compliance Auditing

Fallom's comprehensive audit trails make it suitable for organizations operating in regulated industries. It allows for thorough documentation of LLM interactions, which is essential for audits and compliance checks.

Performance Evaluation

With built-in evaluation tools, Fallom enables teams to assess the performance of their LLM outputs continuously. This feature helps catch regressions before they impact production, ensuring that performance standards are consistently met.

Frequently Asked Questions

What is Fallom?

Fallom is an AI-native observability platform that provides comprehensive monitoring and visibility into LLM and agent workloads, facilitating effective debugging and compliance management.

How does Fallom support compliance needs?

Fallom features full audit trails, input/output logging, model versioning, and user consent tracking, ensuring that organizations can meet regulatory requirements such as GDPR and SOC 2.

Can I track costs across different models with Fallom?

Yes, Fallom provides detailed cost attribution, allowing organizations to track spending per model, user, and team, which is essential for budgeting and financial analysis.

How quickly can I set up Fallom?

Fallom can be set up in under five minutes using its OpenTelemetry-native SDK, enabling teams to start monitoring their LLM operations almost immediately.