Tyk AI Studio incorporates an Analytics System designed to collect, aggregate, and provide insights into the usage, cost, and performance of the platform’s core components, particularly LLMs, Tools, and Chat interactions.

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Purpose

The Analytics System serves several key purposes:
  • Cost Tracking: Monitor spending associated with different LLM providers and models.
  • Usage Monitoring: Understand how users and applications are interacting with LLMs and Tools.
  • Performance Analysis: Track metrics like latency and token counts for LLM requests.
  • Auditing & Debugging: Provide detailed logs of interactions for troubleshooting and security analysis.
  • Reporting: Offer data points for dashboards and reports for administrators and potentially end-users.

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Data Collection

The primary point of data collection is the Proxy & API Gateway. As requests flow through the proxy:
  1. Request Details: Information about the incoming request is captured (e.g., user ID, application ID, requested LLM/route, timestamp).
  2. LLM Interaction: Details about the interaction with the backend LLM are recorded (e.g., model used, prompt tokens, completion tokens, latency).
  3. Cost Calculation: Using data from the Model Pricing System, the cost of the interaction is calculated based on token counts.
  4. Tool Usage: If the interaction involved Tools, relevant details might be logged (e.g., which tool was called, success/failure).
  5. Chat Context: For interactions originating from the Chat Interface, metadata about the chat session might be included.

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Architecture

  • Asynchronous Ingestion: To minimize impact on request latency, analytics data is typically collected by the Proxy and sent asynchronously to a dedicated analytics database or processing pipeline.
  • Data Storage: A suitable database (e.g., time-series database like InfluxDB, relational database like PostgreSQL, or a data warehouse) stores the aggregated analytics records.
  • API Endpoints: Tyk AI Studio exposes internal API endpoints that allow the Admin UI (and potentially other authorized services) to query the aggregated analytics data.

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Key Metrics Tracked (Examples)

  • Per LLM Request:
    • Timestamp
    • User ID / API Key ID
    • LLM Configuration ID / Route ID
    • Model Name
    • Prompt Tokens
    • Completion Tokens
    • Total Tokens
    • Calculated Cost
    • Latency (ms)
    • Success/Error Status
  • Per Tool Call (if applicable):
    • Timestamp
    • Tool ID
    • Success/Error Status
    • Latency (ms)
  • Aggregated Metrics:
    • Total cost per user/application/LLM over time.
    • Total requests per user/application/LLM over time.
    • Average latency per LLM.
    • Most frequently used models/tools.

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Monitoring & Dashboards (Admin)

Administrators typically access analytics data via dashboards within the Tyk AI Studio UI.
  • Overview: High-level summaries of cost, usage, and requests.
  • Filtering & Grouping: Ability to filter data by time range, user, application, LLM configuration, etc.
  • Visualizations: Charts and graphs showing trends in cost, token usage, request volume, and latency.
  • Detailed Logs: Access to raw or near-raw event logs for specific interactions (useful for debugging). Analytics Dashboard

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Integration with Other Systems

  • Budget Control: Analytics data (specifically cost) is likely used by the Budget Control system to track spending against defined limits.
  • Model Pricing: The pricing definitions are crucial for calculating the cost metric within the analytics system.
By providing detailed analytics, Tyk AI Studio enables organizations to effectively manage costs, understand usage patterns, and ensure the optimal performance of their AI interactions.