AI Observability – Complete lifecycle management for teams building and running LLM and ML models. Build, deploy, and manage trustworthy AI systems – manage costs without compromising performance.
ai observability dashboard
Complete lifecycle management for teams building and running LLM and ML models. Build, deploy, and manage trustworthy AI systems – manage costs without compromising performance.
Data scientists, data engineers, ML engineers, AI platform engineers, and Chief AI Officers need to deliver and run new LLM and ML models operating in production environments.
Contents
Manage the full LLM life cycle:
Manage the full ML model life cycle:
One-stop shop for LLM model evaluation and selection. Compare foundational and open-source models, guardrails for bias, hallucination, safety.
Deploy and govern LLMs in production. Load balance across foundational and open-source models, manage cost and ensure availability. Overcome rate limits while ensuring continuous trust and safety screenings. Includes open-source LLM model hosting.
Manage all your LLMs in one place. Track input and output token consumption, response times, performance, change events, and failed evaluations. LLM traces to identify issues from individual prompts. Deep-dive monitors and workbenches for trust and safety, cost, and performance.
Ensure model data quality. Detect and fix data and concept drift across ML models. Detect bias across all data fields. Ensure local and global explainability (using SHAP values). Root cause analysis and auto-remediation for bias, model drift, and data drift.
Flexible Deployment options
SaaS
On Premise
Co-Pilot
Query and drill into data
Perform model troubleshooting and root cause analysis
Fast Onboarding
Model Management - model setup, definition + its associated model data
Integrations - onboard Model Data from Open Telemetry, Elastic, Prometheus, Google BigQuery
Add workbench for each use case in minutes
Model Monitoring
Out of the box monitors for data & model drift, LLM Trust & Safety, LLM performance, model data quality, and more.
Automatic detection of model drift, model performance and model accuracy anomalies
Complete LLM observability and ML observability
IFTracer SDK for collecting streaming prompt data (traces and spans)
Notifications via email for health/performance for each monitor.
Workbench
Analyze anomalies and perform deep dive analysis
Trace Viewer - view LLM traces with anomalies
Prompt Viewer - view all LLM prompts anomalies
Charts with flexible filtering
Compare models, anomalies, cost
Timeline view to analyze when anomalies occur, deliver root cause analysis, and morе
Instant workbench creation for each use case
Dashboards
Tailored dashboards for LLM and ML models
Data quality, model drift, total model performance (ML)
Token consumption, malicious prompt identification (LLM), cost
Analyze model drift using PSI or distance metrics
LLM Insights Dashboard for model usage & consumption, model health & performance
LLM Labs
Compare foundational and open-source models
Host open-source models during evaluation
Hallucination, irrelevance evaluations
LLM Guardrails and evaluations
Batch prompt processing and A/B testing
Model fine tuning
LLM Gateway
Model resilience – automatic recovery from foundational model outages
Overcome rate limits
Intelligence routing between models based on response time, cost, token limits
LLM Guardrails – continuous safety checks for 15+ measures
Model hosting for production open-source LLMs
Model Context Protocol (MCP) Server
LLMs interact directly with the InsightFinder platform
AI tools tap directly into incidents, log anomalies, and metric anomalies through secure, natural language queries
InsightFinder AI’s anomaly detection, root cause analysis, and incident predictions integrate easily into the leading Observability platforms – bringing high-power AI-based analysis to your existing Observability and Monitoring environment.
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