Model Logging & Analytics System

1 Overview

The LLM402 platform features an enterprise-grade analytics and logging system designed to manage dynamic AI model ecosystems, track multi-dimensional API usage, and ensure operational integrity across decentralized, token-based environments.

Its architecture unifies model lifecycle management, real-time quota tracking, and performance observability into a scalable, modular infrastructure—ideal for Web3-native AI platforms where trustless auditing and transparency are essential.

2 Model Registry & Matching Engine

Model Metadata Repository

A centralized registry maintains structured metadata for each model:

  • Model aliases and version variants (e.g., gpt-4-turbo, claude-2.1)

  • Supported endpoints and status flags

  • Vendor and capability associations

Supports soft deletion to retain audit history while allowing safe name reuse.

Rule-Based Model Matching

Beyond strict name matching, the system enables flexible patterns:

  • Prefix: e.g., "gpt-4*" matches gpt-4-turbo, gpt-4-vision

  • Suffix / Contains: Ideal for regionalized or vendor-specific variants

This reduces administrative load when providers release new versions.

Channel Binding & Aggregation

Models are linked to upstream API channels via a many-to-many binding graph:

  • Enforces access control per user group

  • Aggregates capabilities for request routing

  • Enables multi-provider fallback for the same model name

3 Dynamic Pricing Configuration

Supports two pricing modes:

  • Fixed-price per request

  • Token-based pricing (with prompt/completion ratio settings)

Includes conflict detection to prevent hybrid misconfigurations and supports:

  • Group-based pricing tiers

  • Cached-token discounts

  • Model-specific multipliers (e.g., GPT-4 > GPT-3.5)

4 Logging Architecture

Real-Time Logging Pipeline

Each API call generates a rich log entry capturing:

  • Token usage (prompt/completion)

  • User & group identity

  • Model, channel, request ID

  • Response time, streaming mode, error status

  • Client IP (optional, privacy-configurable)

Logs are categorized into:

  • Usage logs

  • Error logs

  • Top-ups & refunds

  • Admin actions

  • System events

Multi-Database Design

Heavy traffic logs are stored in a dedicated log database, separate from operational metadata, enabling:

  • Independent retention policies

  • Efficient analytics querying

  • Scalable log storage

5 Quota & Metric Aggregation

Hourly Bucketing (QuotaData)

A background service aggregates logs into hourly statistical blocks:

  • Tracks tokens consumed, quota debited, request volume

  • Indexed by user, model, hour

  • Stored in a high-throughput quota_data table

Used for:

  • Billing dashboards

  • Rate enforcement

  • Predictive scaling

In-Memory Cache Layer

A mutex-safe in-memory store buffers live statistics and periodically flushes to disk. Key features:

  • Composite key index: user_id + model + hour

  • Safe for concurrent updates

  • Tunable flush interval via DATA_EXPORT_INTERVAL

Live RPM/TPM Calculations

Real-time stats such as Requests per Minute and Tokens per Minute are calculated from logs in the last 60 seconds for observability dashboards.

6 Operational Health Services

Channel Health Monitoring

  • Periodic liveness checks for all upstream providers

  • Failed endpoints are auto-disabled with reason codes

  • Tracks latency trends for performance tuning

Balance Polling

  • For credit-based APIs (e.g., OpenAI), workers poll upstream balances

  • Automatically disables channels when funds are low

  • Enables proactive funding alerts

Batch Updates & Write Optimization

  • Quota updates are accumulated in memory and batch-flushed via atomic SQL operations

  • Minimizes contention during high-throughput periods

7 Performance & Observability

  • pprof-enabled runtime profiling (heap, CPU, goroutines)

  • Connection pool monitoring for DB stability

  • Timestamped log cleanup to maintain data hygiene

  • Admin audit trails for all privileged operations

8 Analytics & Dashboarding

Multi-Dimensional Querying

Supports filtered aggregation by:

  • User, Token, Channel, Model

  • Time range, User Group, Quota Type

Used for:

  • Usage trend analysis

  • Anomaly detection

  • Tiered billing & quota enforcement

Dashboard Integration

  • Hourly granularity pre-aggregated data feeds visual interfaces

  • Enables user segmentation, model popularity stats, and cost visualizations

9 Summary

The Model Logging & Analytics System empowers LLM402 with:

  • Elastic scalability for real-time API usage

  • Deep insights into consumption patterns

  • Transparent billing & quota tracking for Web3-native users

  • Autonomous monitoring and fault recovery via background services

This subsystem is foundational for operating trustless, high-availability AI APIs across decentralized environments.

PreviousCore Relay SystemNextAI Provider Integration

Last updated