Fallom

Large Language Models (LLMs)AI Monitor Log Management

AI-native observability platform for LLMs & agents

Pricing:Freemium

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About

Fallom is an AI-native observability platform for LLM and agent workloads that lets you see every LLM call in production with end-to-end tracing, including prompts, outputs, tool calls, tokens, latency, and per-call cost. We provide session/user/customer-level context, timing waterfalls for multi-step agents, and enterprise-ready audit trails with logging, model versioning, and consent tracking to support compliance needs. With a single OpenTelemetry-native SDK, teams can instrument apps in minutes and monitor usage live, debug issues faster, and attribute spend across models, users, and teams.

Key Features

OpenTelemetry-native SDK

One OpenTelemetry-native SDK to instrument LLM and agent calls in minutes, enabling seamless integration with existing telemetry pipelines and minimal engineering effort.

End-to-end Tracing & Waterfalls

Full per-call tracing including prompts, outputs, tool calls, tokens, latency, and multi-step timing waterfalls for agents to visualize each step of complex flows.

Cost & Usage Attribution

Per-call cost tracking and aggregated spend reports that attribute usage and model costs to users, sessions, and teams to control spend and inform model selection.

Session & Contextual Insights

Session- and user-level context capture (customer data, conversation state) to understand behavior across requests and reproduce issues with full context.

Enterprise Audit Trails & Compliance

Logging, model versioning, consent tracking and immutable audit trails to support security, compliance, and governance requirements.

How to Use Fallom

1) Sign up for Fallom and obtain your project credentials. 2) Install and configure the OpenTelemetry-native SDK in your app to instrument LLM and agent calls (prompts, tool calls, tokens, model metadata). 3) Deploy to staging/production—Fallom will capture live traces, token usage, latency, and costs. 4) Use the Fallom dashboard to inspect traces, debug multi-step agents, set alerts, and generate cost/usage reports for teams and models.

Use Cases

•Production monitoring and debugging: Observe every LLM call and multi-step agent execution in production to trace failures, reproduce bugs with full prompt/output context, and reduce MTTR.

•Cost management and model optimization: Track per-call costs and attribute spend by model, user, or team to identify expensive patterns, compare model efficiency, and optimize usage.

•Compliance & auditability: Maintain immutable audit trails with model versioning, consent tracking, and logs to support regulatory compliance, internal audits, and data governance.