Modern applications generate enormous amounts of data from servers, APIs, databases, cloud infrastructure, user interactions, and AI models. Simply collecting this information is not enough. IT and business teams need tools that can identify problems, understand performance, and turn telemetry into useful insights.
AI monitoring tools are helping teams detect anomalies, investigate incidents, and monitor increasingly complex AI-powered applications. At the same time, AI analytics tools can analyze large volumes of operational and business data to identify patterns and support faster decisions.
In 2026, monitoring is also expanding beyond traditional uptime and infrastructure metrics. Teams increasingly need visibility into AI model latency, token usage, costs, agent behavior, quality, and application performance.
What Are AI Monitoring Tools?
AI monitoring tools use artificial intelligence alongside traditional observability technologies to monitor applications, infrastructure, AI models, and business systems.
They can help identify:
- Performance issues
- Errors and failures
- Unusual behavior
- Latency increases
- Resource usage
- AI model problems
- Cost spikes
- User-experience issues
Traditional monitoring often tells teams that something is wrong. AI-powered monitoring can help explain what changed, identify likely causes, and prioritize issues.
What Are AI Analytics Tools?
AI analytics tools use AI and machine learning to analyze data and generate insights from large or complex datasets.
For example, an analytics platform might identify unusual traffic patterns, compare application performance, detect changes in customer behavior, or highlight a sudden increase in infrastructure costs.
AI analytics can therefore turn large amounts of telemetry into information that teams can use for decision-making.
What Is Performance Tracking AI?
Performance tracking AI refers to using AI to continuously evaluate system, application, model, or business performance and identify patterns that may require attention.
For AI applications, performance tracking can include metrics such as:
- Response latency
- Request volume
- Error rates
- Token usage
- Model costs
- Model quality
- Agent execution
- Infrastructure utilization
New Relic’s AI Monitoring, for example, provides visibility into AI application performance, cost, and quality, while its agent monitoring capabilities can trace individual agent and tool calls.
Best AI Monitoring and Analytics Tools in 2026
Dynatrace
Dynatrace provides full-stack observability with AI-powered analysis across applications, infrastructure, logs, metrics, traces, and AI workloads.
Its AI Observability capabilities can monitor LLMs, agents, orchestration frameworks, vector databases, and infrastructure. It also tracks metrics such as latency, errors, tokens, costs, and AI quality.
Best for: Enterprise observability, complex infrastructure, and AI-powered applications.
New Relic
New Relic provides application performance monitoring and dedicated AI monitoring capabilities. Teams can track AI responses, model performance, token usage, and AI application behavior.
In August 2026, New Relic also announced general availability for OpenTelemetry-based AI monitoring, allowing GenAI spans to appear within existing distributed traces.
Best for: Application monitoring, developers, and teams building AI applications.
Google Cloud Vertex AI
Google Cloud provides model monitoring and performance monitoring capabilities for AI workloads. Vertex AI’s built-in monitoring can surface usage, latency, and error-rate information and configure alerts for model problems.
Best for: Teams building and deploying machine-learning and generative AI applications on Google Cloud.
Key Features to Look For
Real-time monitoring
Choose tools that can collect and analyze telemetry with enough speed for your operational requirements.
Anomaly detection
AI can help identify unusual patterns that may be difficult to detect using fixed thresholds alone.
Root-cause analysis
Advanced platforms can correlate metrics, logs, traces, dependencies, and events to help determine why an issue occurred.
AI-specific monitoring
If you operate AI applications, look for monitoring of tokens, model latency, costs, tool calls, agent execution, and quality.
Custom dashboards
Dashboards should allow different teams to track the metrics that matter to them.
Alerts
Good monitoring systems should notify teams when important thresholds, service-level objectives, or unusual patterns require attention.
How to Use AI Monitoring Tools
1. Define what you want to measure
Identify the systems and performance indicators that matter most.
For an application, these might include uptime, latency, error rates, and response times.
For an AI application, you may also track token usage, model costs, quality, and agent execution.
2. Connect your data sources
Integrate application logs, metrics, traces, cloud infrastructure, databases, AI services, and other relevant telemetry.
OpenTelemetry can be particularly useful for creating standardized telemetry pipelines across supported systems. New Relic’s current AI monitoring supports GenAI spans through OpenTelemetry.
3. Establish performance baselines
Understand normal system behavior before relying heavily on anomaly detection.
4. Configure alerts
Create alerts for critical failures, unusual latency, rising error rates, unexpected costs, or other important conditions.
5. Analyze anomalies
Use AI-assisted analysis to investigate unusual patterns and correlate events across your technology stack.
6. Track improvements
Measure whether monitoring and optimization efforts actually improve reliability, performance, costs, and user experience.
Common Monitoring Mistakes
Monitoring too many metrics
Collecting everything can create noise. Focus on metrics connected to important technical and business outcomes.
Ignoring AI-specific costs
Traditional infrastructure monitoring may not reveal rapidly increasing token or model costs. AI applications need dedicated cost tracking.
Relying only on alerts
Alerts tell teams that something may be wrong, but dashboards, traces, logs, and historical data are necessary for deeper investigation.
Ignoring data privacy
AI monitoring can involve prompts, responses, user information, and application telemetry. Configure data collection carefully and apply appropriate privacy controls. New Relic, for example, states that prompt and completion content capture is configurable and provides filtering options for sensitive information.
Trusting AI conclusions blindly
AI-generated root-cause suggestions and anomaly explanations should be treated as decision support rather than unquestionable facts.
ADVANCED INSIGHTS
Move from monitoring to observability
Modern observability combines metrics, logs, traces, user behavior, topology, and business context. AI can correlate these signals to provide more useful explanations than isolated dashboards.
Dynatrace describes AI-powered observability as combining telemetry with causal, predictive, and generative AI to help teams detect and resolve issues across complex environments.
Monitor AI agents end-to-end
AI agents introduce new monitoring requirements because one user request can involve multiple model calls, tools, databases, and other agents.
New Relic’s AI Agent Monitoring can capture agent invocations, tool calls, and handoffs, while Dynatrace provides visibility into agent topology and tool usage.
Combine performance with business analytics
Technical performance should not be viewed separately from business outcomes.
For example:
Latency Increase → Customer Experience Declines → Conversion Drops → Revenue Impact
Connecting technical and business analytics helps organizations understand the actual impact of system performance.
Use predictive performance tracking
The next step beyond detecting problems is predicting them. AI can analyze historical patterns and current telemetry to identify conditions that may lead to failures, cost increases, or performance degradation.
Build an AI observability feedback loop
A mature monitoring architecture can follow this cycle:
Collect → Analyze → Detect → Investigate → Act → Measure
Human engineers should remain involved in high-impact decisions, especially when automated systems can change production infrastructure or AI behavior.
About automationtools.best
automationtools.best helps businesses, developers, IT teams, and AI professionals discover AI-powered monitoring, analytics, observability, automation, and performance tools. The platform makes it easier to find software for monitoring applications, analyzing operational data, tracking AI performance, detecting problems, and improving digital system reliability.
