AI Observability – InsightFinder’s AI Observability platform provides predictive, explainable insights across ML models, LLMs, and distributed infrastructure; going far beyond traditional monitoring tools.
multi-agent workflow tracing
Complete lifecycle management for teams building and running LLM and ML models. InsightFinder provides an end-to-end AI Observability Platform that helps teams build, deploy, govern, and maintain trustworthy AI systems in production; while controlling cost and improving reliability.
Data scientists, data engineers, ML engineers, AI platform engineers, and Chief AI Officers use InsightFinder to deliver high-performing, stable, and predictable LLM and ML applications at scale.
Contents
Manage the full LLM life cycle with reliability, governance, and cost control:
Operationalize and maintain ML models with accuracy and transparency:
Gain end-to-end visibility into complex multi-agent AI workflows with distributed tracing built for agentic systems. Trace every step across agents, tools, and handoffs in a single view, while surfacing performance anomalies, token consumption, and failed evaluations in context. See the most common reasons evals fail to pinpoint reliability issues faster and improve agent behavior with less guesswork.
Compare prompt performance across multiple underlying LLMs to find the best fit for your use case. Version prompt packs, run side-by-side tests, and evaluate outcomes across failures, execution time, token cost, and success rates in one place. Choose the right model for each prompt set, optimize quality and cost, and improve results with confidence.
Turn failed prompts into a continuous improvement loop for your AI systems. Automatically generate training datasets from real-world failures and use them to run reinforcement learning jobs that adapt foundational models into custom models tuned for your domain. Improve model behavior based on production evidence, not guesswork, while accelerating the path to more accurate, reliable outputs.
One-stop shop for LLM model evaluation and selection. Compare foundational and open-source models, guardrails for bias, hallucination, safety.
Deploy and govern AI models 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. Use LLM traces to identify issues from individual prompts. Deep-dive monitors and workbenches for trust and safety, cost, and performance.
Monitor datasets for anomalies and consistency issues. Quickly find missing data, field type mismatches, data outliers, or any other custom condition across any of your datasets. Identify the source of issues and see trends over time.
Monitor data drift, concept drift, and feature-level bias across ML models. 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 model, LLM, and system data
Troubleshoot models and perform full root cause analysis
Fast Onboarding
Model Management: simple 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
Unified observability across LLMs and ML models
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 for inspecting LLM traces with anomaly signals
Prompt Viewer for identifying anomalous or high-risk prompts
Charts with flexible filtering for fast diagnosis
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 LLM models
Host open-source models during evaluation
Evaluate hallucination, safety, relevance, and irrelevance
Apply 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-powered analysis to your existing Observability and Monitoring environment.
Take InsightFinder AI for a no-obligation test drive. We’ll provide you with a detailed report on your outages to uncover what could have been prevented.
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