Fallom

Fallom is an AI-native observability platform engineered specifically for monitoring and optimizing Large Language Model (LLM) and AI agent workloads in production environments. It provides engineering, product, and compliance teams with comprehensive, real-time visibility into every AI interaction, moving organizations from blind deployment to data-driven management of their AI applications. The platform's core value proposition is delivering end-to-end tracing for LLM calls, capturing granular details such as prompts, outputs, tool calls, token usage, latency, and per-call costs.

Built on the open standard OpenTelemetry (OTEL), Fallom offers a single, lightweight SDK that allows teams to instrument their applications in minutes, eliminating vendor lock-in. It is designed for enterprises that require scale, reliability, and compliance, featuring session-level context for user journeys, timing waterfalls for complex multi-step agents, and robust audit trails. By centralizing observability, Fallom empowers teams to debug issues faster, monitor usage live, attribute spend accurately across models and teams, and ensure their AI systems are performant, cost-effective, and compliant with regulations like the EU AI Act, SOC 2, and GDPR.

End-to-End LLM Tracing

Fallom provides comprehensive tracing for every LLM call, capturing granular details in real-time. This includes the full prompt, model output, any function or tool calls made, token counts, latency metrics, and the calculated cost per call. This deep visibility is essential for debugging complex agentic workflows, understanding performance bottlenecks, and gaining a precise view of operational costs.

Cost Attribution and Transparency

The platform offers detailed cost tracking and attribution, breaking down spend by model, team, user, or customer. This provides full financial transparency for budgeting, forecasting, and internal chargeback processes. Teams can monitor live usage, set alerts for budget overruns, and make informed decisions about model selection based on both performance and cost-efficiency.

Compliance-Ready Audit Trails

Fallom is built for regulated industries, providing immutable, complete audit trails of every AI interaction. This includes full input/output logging, model versioning, and user consent tracking. These features are designed to help organizations meet stringent regulatory requirements such as the EU AI Act, SOC 2, and GDPR, ensuring accountability and traceability in AI operations.

Session Tracking and User Context

Group individual traces into complete user sessions to understand the full customer journey. This feature provides context for interactions, allowing teams to analyze how users engage with AI features, troubleshoot specific customer issues, and calculate the total cost-to-serve per user or account, enabling better product and support insights.

Production Debugging and Performance Optimization

Engineering teams use Fallom to rapidly diagnose failures and latency issues in live AI applications. By examining timing waterfalls and tool call sequences, developers can pinpoint exactly where in a multi-step agent workflow a problem occurred, whether it's a slow LLM call, a failing tool, or a logic error, drastically reducing mean time to resolution (MTTR).

Financial Governance and Cost Control

Finance and engineering leadership utilize Fallom's cost attribution features to monitor and control AI expenditure. By tracking spend per model, team, or product feature, organizations can identify cost drivers, optimize expensive workflows, implement chargebacks, and ensure AI initiatives remain within budget, transforming AI costs from a black box into a manageable line item.

Regulatory Compliance and Auditing

Compliance and legal teams leverage Fallom to demonstrate adherence to AI regulations. The platform's immutable audit trails, consent tracking, and detailed logging provide the necessary evidence for audits required by standards like the EU AI Act or SOC 2. Privacy mode features also allow sensitive data to be redacted while maintaining operational telemetry.

Model Evaluation and A/B Testing

Product and ML teams employ Fallom to run evaluations, test new prompts, and safely roll out new model versions. The platform facilitates A/B testing by splitting traffic between models or prompt versions, allowing teams to compare performance, cost, and quality metrics like accuracy or hallucination rates before committing to a full production deployment.

How does Fallom integrate with my existing AI applications?

Fallom integrates via a single, lightweight OpenTelemetry (OTEL)-native SDK. You can instrument your applications in under five minutes by adding the SDK, which automatically captures traces from LLM calls, tool usage, and custom spans. Being OTEL-based, it avoids vendor lock-in and works with a wide range of LLM providers and frameworks.

Does Fallom store sensitive user data from prompts and responses?

Fallom offers a configurable Privacy Mode to address this concern. You can choose to disable full content capture for sensitive data, redact specific fields, or log only metadata (like token counts and latency) while protecting confidential information. This allows you to maintain full observability for debugging while adhering to data privacy policies.

Can Fallom track costs for different teams or projects?

Yes, detailed cost attribution is a core feature. Fallom automatically breaks down costs by the LLM model used. You can further enrich traces with custom attributes (like team ID, project name, or user ID) to slice and dice spending across any dimension, enabling precise showback/chargeback and helping teams understand their AI resource consumption.

Is Fallom suitable for large-scale enterprise deployments?

Absolutely. Fallom is engineered for enterprise-scale, reliability, and security. It handles high-volume tracing data, offers robust access controls, and provides features essential for large organizations, including comprehensive audit trails, SOC 2/GDPR-ready compliance tools, and the ability to monitor complex, multi-agent AI systems across entire product suites.