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Improve your AI application outcomes

Build and monitor reliable AI applications for consistent results:
  • Monitor AI model performance in production
  • Detect and resolve issues proactively
  • Improve response quality through data-driven insights
  • Reduce costs with optimized resource usage

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Understanding LLM observability challenges

Why does traditional monitoring fail with LLM applications?
  • Cannot track prompt and completion correlations - Traditional tools lack the ability to correlate input prompts with their corresponding outputs
  • Cannot monitor critical LLM metrics - Missing essential metrics like token usage, model parameters, and response quality
  • Struggles with mixed data types - Ineffective at processing both structured and unstructured data simultaneously
  • Cannot trace reasoning processes - Unable to debug black-box LLM failures or understand internal reasoning
  • Fail to track complex workflows - Cannot handle RAG systems, tool calling, and multi-step reasoning chains
  • Limited human feedback support - Provide minimal capabilities for collecting and analyzing human feedback and preference models
  • Lack subjective metric tracking - Missing support for user ratings, A/B testing, and quality assessments

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Maxim’s solution

Maxim platform architecture overview Maxim platform leverages three core architectural principles:

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1. Comprehensive distributed tracing

Track the complete request lifecycle, including LLM requests and responses. Debug precisely with end-to-end application flow visibility.

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2. Zero-state SDK architecture

Maintain robust observability across functions, classes, and microservices without state management complexity.

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3. Open source compatibility

Maxim logging is inspired by (and highly compatible with) open telemetry:
  • Generate idempotent commit logs for every function call
  • Support high concurrency and network instability
  • Maintain accurate trace timelines regardless of log arrival order
  • Production-proven reliability with over one billion indexed logs

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Key Features

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Real-time monitoring and alerting

Track GenAI metrics through distributed tracing and receive instant alerts via:
  • Slack
  • PagerDuty
  • OpsGenie
Monitor critical thresholds for:
  • Cost per trace
  • Token usage
  • User feedback patterns

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Saved views

Find common search patterns instantly:
  • Store common search patterns
  • Create debugging shortcuts
  • Speed up issue resolution

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Online evaluation

Monitor application performance with:
  • Custom filters and rules
  • Automated reports
  • Threshold-based alerts

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Data curation

Transform logs into valuable datasets:
  • Create datasets with one click
  • Filter incoming logs
  • Build targeted training data
  • Update datasets for prompt improvements
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