
API Logs & AI Oservabilty
Full-stack AI Observability tool to get real-time insights into usage of AI API, track performance response, and tweak LLMs for optimization. AI API logs and ML Observability allows developers and enterprises to ensure the security, reliability, and correctness of advanced AI systems and machine learning models.

AI Observability
AI Observability & Tracing History
The ML monitoring tool keeps the record of all requests and responses to provide clear visualization of all interactions with dynamic AI observability dashboard. It helps tracking training data, viewing model performance, and AI infrastructure monitoring.
Full Request & Response History
ML Monitoring
Real-Time Debugging Tools
Monitor AI Systems
Instant Access to API Events

AI Observability
Robust Predictive Analytics
With Taam Cloud’s AI Observability solutions, developers can get a visualization of more than 40 key performance metrics, which enable users to audit logs, track performance, and enhance efficiency of AI applications to make data-driven decisions.
In-Depth API Usage Insights
LLM Performance Tracking
Cost & Consumption Metrics
Real-Time Anomaly Detection
Visual Datalog View

AI Observability Dashboard
API Request Filters
AI Observability allows developers to filter and pinpoint the output data for more efficient and targeted interpretations by API call. It helps in analyzing data quickly, debugging faster, and focusing on targeted AI interactions.
Advanced Search & Filtering
Sort by API, Model, or User
Time-Based Log Analysis
Custom Log Tagging

Custom Data Implementation
Custom Metadata
Users can group and categorize interactions with the addition of implementing custom metadata in each API request to ensure easy and smooth fine-tuning and debugging.
Assign Tags & Labels
Customizable API Call Metadata
Track User-Specific Interactions
Simplify Troubleshooting
Organize Logs for Efficiency

Root Cause Monitoring
Root Cause Analysis
Taam Cloud’s AI Observability allows quick and real-time monitoring and help identify potential issues in workflow and models with critical insights on performance.
End-to-End Request Tracing
Model Performance Monitoring
Live Debugging & Insights
Automated Alerts for Issues
Scalable & Enterprise-Ready Logging

Secure API Logs
Secure Framework
Test, monitor, optimize, and track performance of AI-powered applications and agents by accessing log data within a secure, reliable, and responsible AI framework.
Enterprise-Grade Security
Regulatory Compliance
Fraud Detection
Regular Security Audits

FAQs for AI Observability
Reach out to our support team and start building with Taam Cloud today!
What is an AI observability tool?
AI Observability tool provides detailed real-time insights into performance and usage of AI tools, AI services, and models. It helps users in continuous LLMs monitoring, detecting issues, analyzing root cause, and troubleshooting issues to ensure smooth and optimized AI performance. It also helps reduce AI infrastructure costs.
Why is AI Observability Important for LLM Infrastructure?
AI Observability is important for understanding the operations, performance, and health of AI Systems. It provides detailed insights into why an AI model is predicting certain responses to better understand, identify, and resolve errors and biases. AI observability helps users to enhance resources efficiency that cuts the overall AI costs.
How AI Observability will optimize AI cost?
By providing insights and helping identify performance inefficiencies, prevent unnecessary resources consumption, detect issues on time, AI Observability indirectly help in cost optimization, enhancing resource efficiency, reducing errors, and improving model performance.
How Do AI Gateways Enhance Observability in LLM Deployments?
AI Gateway, acting as a middleware layer and a control point that provides a single source for monitoring AI performance, detecting irregularities, and helping in root cause analysis, enhances observability in LLM deployment.
What Best Practices Should I Follow to Implement AI Observability?
Best practices:
1. Establish Clear Metrics and KPIs
2. Emphasize on security and compliance
3. Centralize Data
4. Mitigation of Data Drifts
5. Model Performance tracking
6. Using data analytics
How Taam Cloud Help in implementing AI observability?
Taam Cloud offers detailed AI API Logs dashboard for real-time monitoring, tracing, and analysis of AI systems and models performance. Taam Cloud ensures that AI applications remain reliable, efficient, and compliant with industry standards such as GDPR, SOC2, and HIPAA, through features like centralized log management, end-to-end request tracing, and real-time analytics, to help in detecting inefficiencies, understanding model behavior, and optimizing performance across the AI lifecycle.