Fallom

About Fallom

Fallom is the collaborative observability platform built for teams developing and operating AI applications. In the complex world of LLMs and AI agents, where a single user query can trigger a cascade of model calls, tool executions, and conditional logic, traditional monitoring falls short. Fallom provides the shared lens your engineering, product, and business teams need to see, understand, and optimize these workloads together. It delivers real-time, end-to-end tracing for every LLM interaction in production, capturing the full context—from the initial prompt and the model's output to every tool call, token usage, latency metric, and associated cost. This shared visibility transforms how teams collaborate on AI operations, enabling you to swiftly debug intricate agent failures, attribute spend accurately across projects, and ensure compliance with evolving regulations, all from a unified dashboard. By leveraging a single OpenTelemetry-native SDK, Fallom integrates seamlessly into your stack in minutes, fostering a cooperative environment where everyone has the contextual data needed to build reliable, efficient, and cost-effective AI experiences.

Features of Fallom

End-to-End LLM Tracing

Gain a complete, collaborative view of every AI interaction with unified traces that stitch together the entire workflow. Each trace provides your team with the full context: the exact prompts sent, the model's generated outputs, detailed token counts, precise latency measurements, and the calculated per-call cost. This shared source of truth allows developers, DevOps, and product managers to collectively understand system behavior and user experience, making troubleshooting a cooperative effort rather than a siloed hunt for clues.

Enterprise-Grade Audit & Compliance

Work together to meet stringent regulatory requirements with confidence. Fallom provides comprehensive audit trails that log every LLM interaction, supporting compliance frameworks like the EU AI Act, SOC 2, and GDPR. Teams can collaboratively manage features like model versioning for reproducibility and user consent tracking, ensuring that legal, security, and engineering departments are aligned and have the immutable logs needed for audits and governance reviews.

Granular Cost Attribution & Analytics

Foster financial transparency and accountability across your organization. Fallom breaks down AI spending by model, user, team, and even individual customer, providing clear charts and reports. This enables engineering teams to optimize for cost-efficiency, finance teams to manage budgets and implement chargebacks, and business leaders to understand customer-level profitability, all working from the same accurate dataset.

Real-Time Dashboard & Tool Call Visibility

Monitor your AI operations as a cohesive unit with a live dashboard that displays active traces, system health, and key metrics. Dive into the specifics of multi-step agent workflows with timing waterfalls that visualize each step's duration. Most importantly, see every function call your agents make—including the arguments passed and results returned—enabling your team to collaboratively verify logic, debug tool integration errors, and optimize complex chains.

Use Cases of Fallom

Debugging Complex AI Agent Failures

When a customer-facing agent fails to complete a task like booking travel or processing a support ticket, isolated logs are insufficient. Engineering teams use Fallom's session-level tracing and timing waterfalls to collaboratively reconstruct the exact sequence of LLM calls and tool executions that led to the failure. This shared visibility allows them to quickly identify whether the issue was a poor prompt, a tool error, a model hallucination, or a latency timeout, dramatically reducing mean time to resolution (MTTR).

Managing AI Costs and Implementing Chargebacks

As AI usage scales, costs can spiral unpredictably. Finance and platform engineering teams leverage Fallom's granular cost attribution to track spend per department, product feature, or internal team. This collaboration enables precise budgeting, identifies cost-inefficient model usage, and provides the data foundation for implementing fair chargeback or showback models, ensuring sustainable and accountable AI investment across the company.

Ensuring Compliance and Preparing for Audits

For companies in regulated industries, demonstrating control over AI systems is critical. Compliance, legal, and engineering teams work together using Fallom to maintain immutable audit trails of all LLM interactions. They utilize features like model versioning, input/output logging, and consent tracking to collectively build and evidence a compliant AI operations framework, streamlining the preparation for internal and external audits.

Optimizing Performance and User Experience

Product and engineering teams collaborate using Fallom's real-time latency metrics and performance analytics to ensure a snappy user experience. They can A/B test different models or prompt versions, compare their latency and cost profiles, and monitor for performance regressions. This joint effort ensures that new deployments maintain or improve response times, directly contributing to higher user satisfaction and engagement.

Frequently Asked Questions

How quickly can my team start using Fallom?

Your team can be up and running with basic tracing in under 5 minutes. Fallom uses a single, OpenTelemetry-native SDK that integrates seamlessly with popular frameworks. Simply install the SDK, add a few lines of configuration code, and your traces will immediately begin flowing to the collaborative Fallom dashboard for the whole team to see.

Does Fallom support all LLM providers and frameworks?

Yes, Fallom is designed for team flexibility and avoids vendor lock-in. Our OpenTelemetry-native approach means we support all major LLM providers (like OpenAI, Anthropic, Google Gemini, etc.) and agent frameworks. This allows your team to use the best tools for the job and switch providers if needed, all while maintaining consistent, unified observability in Fallom.

How does Fallom handle sensitive or private user data?

Teams can collaborate on privacy confidently with Fallom's configurable privacy controls. You can enable Privacy Mode to disable full content capture for sensitive workflows, logging only metadata like token counts and latency while redacting prompts and completions. This allows engineering and security teams to work together, balancing observability needs with data protection and compliance requirements.

Can we use Fallom for testing and evaluation before production?

Absolutely. Fallom supports collaborative evaluation workflows, allowing teams to run automated checks on LLM outputs for metrics like accuracy, relevance, and hallucination rates. You and your teammates can test new prompts or models in staging, compare their performance against baselines, and catch regressions together before deploying changes to production users, fostering a culture of quality and cooperation.