Overview

Maxim delivers an enterprise-grade distributed tracing solution specifically engineered for GenAI applications, enabling real-time log monitoring in production environments.

The platform is built upon three fundamental architectural principles:

1. Comprehensive Distributed Tracing

Maxim's architecture allows you to capture the complete request lifecycle, including pre-and post-LLM call data. This end-to-end visibility gives developers exhaustive insights into their application flow, enabling precise monitoring and debugging capabilities.

2. Zero-State SDK Architecture

Maxim's SDK implementation employs a pioneering stateless design, eliminating the complexity of state management across function boundaries, class hierarchies, and distributed microservices. This architectural choice significantly reduces integration overhead and potential points of failure while maintaining robust observability capabilities.

3. Compatibility with open source technologies

Maxim logging is inspired by (and highly compatible with) open telemetry. Every function call you make via the Maxim logging SDK generates an idempotent commit log for our server. Maxim logging system is designed for high concurrency and extremely flaky network systems. Irrespective of which order these commit logs reach Maxim servers, the final timeline of your trace will always be intact. This has been tested on over a billion logs indexed last year.

The platform's design ensures seamless integration into existing production environments while delivering enterprise-scale performance monitoring for GenAI applications.

Realtime monitoring & alerting

Maxim's distributed tracing system enables real-time tracking of GenAI application metrics. Users can set up instant alerts using Slack, PagerDuty, or OpsGenie. For example, alerts can be triggered when cost per trace exceeds set limits, token usage reaches defined thresholds, or based on specific user feedback patterns.

Saved views

Maxim allows users to create and store frequently used search patterns for log analysis. These saved views are shortcuts to common search combinations that users regularly need, making log analysis and debugging faster and more efficient.

Online evaluation

Maxim's continuous log evaluation system allows users to monitor application performance in real-time. The system lets users set up evaluations using custom filters and rules. For instance, you can generate automated reports from ongoing log analysis and set up instant alerts to notify teams via Slack, PagerDuty, or OpsGenie when specific metrics cross preset thresholds.

Data curation from logs

Maxim simplifies the process of transforming log data into useful datasets. Users can filter and segment incoming logs to create new datasets or update existing ones with a single click. This feature enables quick dataset generation, which is vital for ongoing, prompt improvements and evaluation needs.