9 Monitoring Tools That Deliver AI-Native Anomaly Detection

The observability market has moved beyond manual threshold-setting. Modern platforms use statistical algorithms, machine learning, and causal AI to detect anomalies automatically. Some work immediately after deployment. Others train on your data for better accuracy. Each approach has technical trade-offs worth understanding.

This guide compares how nine monitoring solutions handle automated anomaly detection and root cause analysis.

The Three Approaches to Anomaly Detection

You have a few ways to detect unusual behavior in your systems, and the right fit depends on how your workloads behave, how your services talk to each other, and the kind of signals you want from your monitoring stack.

Statistical pattern detection
This compares your current metric values with a rolling window of historical data. You get anomaly detection immediately because it doesn’t need a training period. If your traffic patterns shift during deployments, batch jobs, or regional failovers, you can tune the lookback window or sensitivity to keep alerts stable.

Machine learning baselines
These models train on longer periods of your data to understand seasonality, traffic cycles, and normal workload fluctuations. Once trained, they adapt as your usage changes—useful if your systems show strong weekly or monthly patterns. You’ll want this if accuracy matters more than instant setup, since the model improves as it sees more of your data.

Causal AI with topology awareness
This approach uses your live service graph—RPC calls, queue paths, DB connections, and external dependencies—to understand how signals propagate. Instead of calling everything that spikes an “anomaly,” it evaluates where the chain actually starts. If you operate a distributed system with fan-out traffic, shared infra layers, or noisy neighbors, this gives you cleaner root-cause indicators without manual correlation.

The choice depends on what you need right now: quick detection, higher accuracy over time, or a deeper understanding of how failures move through your stack.

9 Best Monitoring Solutions for AI-Native Anomaly Detection

Last9: High-Cardinality Telemetry for AI-Native Anomaly Detection

Configuration Required: Minimal
AI Features Tier: Available across all plans, including free
Pricing Model: Event-based (free: 100M events, 7-day retention)

Last9 is built around a simple principle: you should never lose the detail you need for debugging

We designed the platform to keep high-cardinality detail intact, even at a large scale. Our architecture supports more than 60M active time series per minute without dropping dimensions. This gives engineers and AI agents full-resolution telemetry instead of smoothed or averaged data.

Immediate Anomaly Detection

Alert Studio includes four statistical detectors that work the moment you enable them:

• High Spike
• Low Spike
• Level Change
• Trend Deviation

These algorithms compare current signals against short rolling windows or longer historical ranges. Since they’re statistical and not ML-based, there’s no training delay—you get meaningful alerts immediately.

How High-Cardinality Helps

Many debugging workflows depend on granular identifiers: user_id, service_version, device_type, region, and other high-cardinality labels.
If a checkout flow breaks for a specific cohort, a global average metric won’t show you where to start.

Our storage engine keeps those detailed dimensions available for both human debugging and AI-assisted analysis. This becomes especially valuable when LLMs run correlations or investigate regressions using real telemetry instead of simplified aggregates.

MCP Integration for LLM Debugging

Last9 MCP gives LLMs controlled access to production telemetry. You can ask questions directly from your IDE (Cursor, VS Code) or chat tools:

• “What changed in the payment-service after the last deploy?”
• “Show anomalies for checkout in the past 10 minutes.”
• “Which endpoints slowed down before the failure?”

The LLM retrieves traces, correlated metrics, logs, and related code context. You’re free to use any model—GPT-4, Claude, or others—because we expose the data instead of locking you into a proprietary assistant.

LLM Monitoring

AI-heavy applications generate their own traffic patterns and failure behaviors. With Last9, you can monitor:

• token usage and rate limits
• model latency and timeout patterns
• agent-run steps and outcomes
• prompt failures or regressions
• dependency paths across LLM and non-LLM services

All LLM telemetry flows into the same dashboards, detectors, and alert rules you already use, giving you one place to observe both traditional and AI systems.

Specialized Agents

You can automate targeted operational tasks using agents built on top of Last9 telemetry. These agents can:

• detect recurring exceptions
• flag regressions early
• file pull requests with suggested fixes
• validate improvements against live traffic

They help you encode repeatable workflows and reduce repetitive manual investigation.

Conversational O11y

You can query your systems using plain language—no PromQL, LogQL, or DSLs required. Conversational O11y works across:

• IDEs
• Slack
• Google Chat
• Microsoft Teams

You ask a question, and Last9 returns clear, precise, telemetry-backed answers.

Trace-to-Metrics

Our ingestion pipeline can convert traces into useful metrics automatically:

• latency percentiles
• error counts
• throughput
• custom service KPIs

This lets you build dashboards and alerts directly from trace data with minimal setup. The Last9 SDK and OTel instrumentation work out of the box.

Transparent Pricing

Last9’s event-based approach keeps costs predictable, so you can instrument with the detail your services need.

Best For

You’ll find Last9 a strong fit if you need:

• high-cardinality data preserved without sampling
• immediate, pattern-based anomaly detection without baseline training
• pricing that stays predictable in dynamic Kubernetes environments
• telemetry that AI agents and LLMs can query directly
• Conversational interfaces for investigating production issues

This approach works well when you want fast, reliable detection and a data layer built for both engineers and AI-driven workflows.

What truly differentiates Last9, is how deeply invested their support and customer success teams are—during IPL season, when our traffic scaled dramatically on a daily basis, Last9 tuned their infrastructure dynamically to meet our specific needs, something we’ve never experienced with other vendors.
- Neeraj Prem VermaEngineering Manager - DevOps, Games24x7

Dynatrace Davis AI: Causal Analysis with Topology Awareness

Configuration Required: Minimal (OneAgent auto-discovers topology)
AI Features Tier: Included across all plans
Pricing Model: Host Units + Davis Data Units (consumption-based)

With Davis AI, you get causal analysis built to show how an issue moves through your environment. Instead of surfacing isolated anomalies, Davis walks the dependency graph backward and highlights where a problem begins. The approach is deterministic, so you get consistent and reproducible results every time—useful if your systems need clear explanations instead of probabilistic guesses.

How Smartscape Enables Causal AI

When you deploy OneAgent, it maps your applications automatically. You get a real-time Smartscape graph that captures:

• services
• processes
• hosts
• upstream and downstream paths

This topology powers Davis AI.
Whenever performance shifts, Davis evaluates the fault tree and helps you see whether the signal originates in a specific service or is the result of cascading impact. Related anomalies get grouped into a single problem card with a structured summary of what happened.

If you work in a regulated or SLA-heavy environment, this level of traceability can save time during audits or incident reviews.

Pricing Model

Dynatrace uses Host Units and Davis Data Units to calculate usage. Costs grow with data volume and environment coverage. If your traffic is highly dynamic, you’ll want to keep an eye on consumption to avoid surprises. Because OneAgent integrates deeply across your stack, migrations generally require planning on your end.

Best For

You’ll find Davis AI a good fit if you need:

• deterministic, explainable root-cause analysis
• clear dependency-based reasoning
• strong audit trails for compliance
• repeatable fault trees that hold up under review

This approach works well when you value consistency and auditability as much as timely detection.

Datadog Watchdog: Machine Learning After Baseline Training

Configuration Required: Low (automatic after baseline)
AI Features Tier: Pro Plus tier ($18+/host/month)
Pricing Model: Per-host tiered

Watchdog uses a machine learning model that learns from your historical traffic. Once the baseline is established, it analyzes patterns across APM, logs, and infrastructure signals to surface unusual behavior. Datadog provides three algorithms—Basic, Agile, and Robust—so the system can adapt to steady workloads, bursty workloads, or mixed environments. Low-traffic endpoints (below 0.5 RPS) are automatically filtered out to reduce noise.

How Baseline Training Works

If you’re adopting Watchdog, you’ll go through a training period where the model observes your environment.
During these first 2–6 weeks, it captures:

• daily and weekly traffic cycles
• normal latency ranges
• throughput variations
• expected error behaviors

As the baseline matures, anomaly detection becomes more reliable, especially for services with noticeable seasonality. When the model stabilizes, Watchdog correlates signals across services and surfaces them as related events, which helps reduce the number of alerts you receive.

If you’re already using Datadog, this integration is smooth because Watchdog plugs into the same dashboards, monitors, and APM data you work with today.

Best For

Watchdog is a good fit if you:

• already operate within Datadog’s ecosystem
• can allocate time for the initial baseline period
• run services where long-term behavioral patterns matter
• are on the Pro Plus tier or plan to move to it

If your environment leans on ML-trained detection and you’re comfortable with per-host pricing, Watchdog delivers consistent value once the baseline has settled.

Elastic Observability: Preconfigured ML Jobs

Configuration Required: Moderate (choose and enable jobs)
AI Features Tier: Platinum or Enterprise ($125+/month)
Pricing Model: GB ingested + retention

Elastic ships with more than a hundred preconfigured machine learning jobs you can enable across your services. These include detectors for latency shifts, throughput anomalies, and error-rate changes. When you use Elastic APM, anomaly scores appear directly on service maps, so you can see which parts of your system need attention.

Job Selection and Training

To get started, you pick the ML jobs that align with your architecture.
Once enabled, each job trains on your historical data to understand:

• expected latency ranges
• normal throughput levels
• traffic cycles
• typical error behavior

This gives you flexibility—you decide exactly which patterns matter for your environment—but it also means you’ll want a good understanding of how your services behave so you can map the right jobs to the right components.

ML capabilities require Platinum or higher, whether you’re running Elastic Cloud or managing the cluster yourself.

Best For

Elastic’s approach works well if you:

• are already invested in Elastic Platinum or Enterprise
• want ML-based anomaly detection without switching to another platform
• prefer fine-grained control over which detectors run on each service
• operate workloads where job-specific training provides more signal than global baselines

If Elastic is already part of your stack, the built-in ML jobs give you anomaly detection without introducing new tooling or monitoring layers.

New Relic Applied Intelligence: Included Across Tiers

Configuration Required: Low (immediate basics; advanced features need setup)
AI Features Tier: Standard and Pro tiers
Pricing Model: Per-user + data ingestion

Applied Intelligence gives you anomaly detection, correlation, and enrichment as part of the core product—no separate AI add-on. Basic anomaly detection works as soon as your data streams in. As the system observes more of your environment, it starts linking deviations to past behavior, deployments, and related services.
If you want richer insights—such as grouping anomalies into incidents or connecting them to changes—you configure the workflows the platform offers.

Predictable Usage-Based Pricing

New Relic’s pricing is structured around users and data ingestion, which can make budgeting more consistent if your infrastructure scales frequently. You forecast your monthly spend based on:

• Number of engineers using the platform
• How much telemetry do you ingest?

For teams that prefer predictable billing over per-host pricing, this model is easier to manage.

Best For

Applied Intelligence works well if you:

• want AI-assisted detection included across subscription tiers
• prefer usage-based pricing that’s easier to forecast
• need correlation and enrichment features without adopting a new monitoring tool
• operate at a scale where per-host pricing becomes harder to control

It’s a practical option for mid-sized teams who want built-in AI capabilities without reworking their existing observability setup.

Splunk Observability Cloud: Correlation with GenAI Assistant

Configuration Required: Moderate (service maps auto-populate; AI Assistant requires prompting)
AI Features Tier: APM tiers
Pricing Model: Per-host ($60–75/host/month)

Splunk Observability Cloud gives you correlation-driven analysis supported by a GenAI Assistant. Service maps populate automatically, and Tag Spotlight lets you break down traces by attributes, making RED metrics—Rate, Errors, Duration—easy to compare across tag values. Color-coded service graphs help you see where an error originates and how it propagates through dependent services.

Agentic AI Architecture

Splunk’s GenAI Assistant uses an agentic pattern. When you ask a question, it plans a sequence of queries, fetches telemetry, and then synthesizes an explanation.
This helps when you’re in the middle of an investigation and want the Assistant to collect context quickly. It’s designed more for guided debugging than proactive anomaly detection.

The underlying model isn’t tied to a single signal type—it works across APM, logs, metrics, and traces to build a correlated narrative around an issue.

Pricing Considerations

Splunk’s per-host pricing can grow quickly in container-heavy environments. For example, a Kubernetes cluster with 100 pods across 20 nodes approaches $1,200–1,500/month before you account for data retention.

If your infrastructure is stable and doesn’t autoscale aggressively, this model stays easier to manage. In dynamic clusters, you’ll want to keep an eye on host counts.

Best For

Splunk Observability Cloud is a good fit if you:

• rely on correlation-heavy incident analysis
• want a GenAI assistant that helps assemble findings during investigations
• Run infrastructure where host counts remain predictable
• prefer guided debugging rather than training-based anomaly detection

It’s especially useful when you want AI help stitching together signals from multiple sources while keeping your existing workflow intact.

Honeycomb: Exploratory Debugging Over Automation

Configuration Required: Minimal (BubbleUp works immediately)
AI Features Tier: All tiers
Pricing Model: Per-event (free: 20M events/month)

Honeycomb focuses on giving you powerful tools for exploratory debugging rather than building autonomous automation layers. BubbleUp compares failing requests against a baseline of healthy traffic across high-cardinality dimensions. This makes it easy for you to spot patterns—outlier versions, unusual endpoints, rare flags—that traditional dashboards often smooth away.

Query Assistant and BubbleUp

The Query Assistant helps you shape queries by translating your intent into Honeycomb’s query model.
Once you’ve isolated a problem area, BubbleUp highlights which attributes differ between successful and failing requests—latency shifts, error spikes, slow dependencies, or unusual tag values.

The platform doesn’t try to replace your reasoning. Instead, it gives you interactive tools that let you break down complex issues quickly, especially in services with deep or irregular traffic patterns.

Pricing Approach

Honeycomb’s per-event pricing supports high-cardinality debugging.
An event with three attributes is priced the same as an event with three hundred, so you can include as much context as you need without adjusting tags for cost reasons. Burst Protection allows up to 2× daily traffic spikes without extra charges, which helps when you’re running experiments or seeing short-term load surges.

Best For

Honeycomb is a strong fit if you:

• prefer hands-on, exploratory debugging
• want tools that guide your reasoning rather than automate decisions
• rely heavily on high-cardinality data
• need flexible pricing that doesn’t penalize detailed event structures

If your workflow leans on asking iterative questions and exploring outliers, Honeycomb’s approach gives you the depth and interactivity you need.

Grafana Cloud: Free ML Features, Cloud-Only

Configuration Required: Low (Sift requires a Kubernetes context)
AI Features Tier: Free for all Grafana Cloud accounts
Pricing Model: Usage-based with a free tier

Grafana Cloud gives you a set of free ML-powered features, including outlier detection, forecasting, and Sift—their diagnostic assistant. Sift analyzes metrics, logs, and traces during incidents, but it currently works best when you provide a Kubernetes cluster and namespace context. The platform leans heavily on Kubernetes metadata for accurate results.

Accessible Entry Point

If you’re exploring AI-assisted observability without committing budget upfront, Grafana Cloud is one of the easiest places to start. Grafana Assistant billing doesn’t begin until January 2026, and the broader ML suite remains free on all cloud accounts.

A key limitation: the ML features are cloud-only. Self-hosted Grafana deployments don’t include Sift, forecasting, or ML detection.

Best For

Grafana Cloud works well if you:

• Run Kubernetes and can provide cluster metadata
• want a low-friction way to experiment with AI-powered detection
• Rely on usage-based pricing with a generous free tier
• prefer adopting ML features without migrating your existing dashboard patterns

It’s a good entry point for budget-conscious teams looking to try AI features before making long-term decisions.

Lightstep (ServiceNow Cloud Observability): Change-Aware Correlation

Configuration Required: Moderate (Telemetry Satellites + service mapping)
AI Features Tier: Included in core observability plans
Pricing Model: Usage-based (ServiceNow units + ingest)

Lightstep focuses on change-aware observability. Instead of scanning for anomalies in isolation, it correlates deviations with deployments, configuration updates, feature flags, and dependency shifts. The platform uses automated change detection across your services so you can quickly see whether a performance issue lines up with a recent code or infra change.

How Change-Aware Correlation Works

When you stream telemetry through Lightstep, the platform tracks:

• service-to-service relationships
• deploy markers
• feature flag evaluations
• schema changes
• infrastructure events

Its correlation engine compares sudden shifts against this change timeline, helping you see whether a spike in latency aligns with a specific deploy or configuration update. You get immediate value without a training period; deeper correlation improves as Lightstep sees more of your environment.

Lightstep also integrates with ServiceNow workflows, letting you tie incidents, CMDB entries, and operational metadata into investigations.

Pricing Model

Lightstep uses a usage-based structure tied to telemetry ingestion and ServiceNow units. Teams with highly variable traffic patterns typically pay attention to ingest volume, but the correlation-first model means you don’t need ML warm-up periods or per-host licensing.

Best For

Lightstep fits well if you:

• Want strong deployment-aware analysis
• Relies heavily on feature flags or progressive delivery
• want immediate correlation without waiting for baselines
• operate in environments where changes are frequent and tightly controlled
• prefer the integration ecosystem of ServiceNow

It’s a good option when you want fast answers driven by a change in context rather than long ML training cycles.

Market Shifts and How They Shape Your Vendor Choices

Two major changes are influencing how you evaluate observability platforms today:

• ServiceNow is discontinuing Lightstep (end-of-support: March 2026).
  Lightstep introduced modern distributed tracing and Change Intelligence, linking metric shifts directly to trace data. ServiceNow is now prioritizing platform-native ITOM AIOps, so Lightstep customers will need to plan a transition.
• Palo Alto Networks is acquiring Chronosphere for $3.35B.
  Chronosphere’s AI-Guided Troubleshooting and Control Plane will fold into the Cortex security ecosystem. If you’re a current customer, expect roadmap changes driven by security integrations and platform consolidation.

These moves signal a maturing market. As you compare vendors, it’s worth thinking not just about features but also long-term stability, roadmap direction, and whether the platform aligns with where you want your observability strategy to go.

Key Checks to Make During Vendor Evaluation

Choosing an observability platform isn’t just about comparing feature lists. The questions you ask at this stage determine how well the platform will fit your scale, urgency, and long-term plans.

What’s the real baseline period?
Some platforms start detecting issues immediately (Last9, Dynatrace). Others need days or weeks of training before results stabilize (Datadog, Elastic). Match this to how quickly you need value.

Are AI features included in your tier?
AI capabilities vary widely by plan. Datadog requires Pro Plus. Elastic requires Platinum. Last9, New Relic, Honeycomb, and Grafana include AI across tiers, which makes evaluation and rollout easier.

How does the platform handle high-cardinality dimensions?
Many systems reduce detail through aggregation or sampling.
Last9 keeps 60M+ active time series per minute without dropping high-cardinality labels. If you rely on attributes like user_id, region, or service_versionAsk explicitly about retention and cost impact.

What’s the migration path?
Check whether the vendor supports:

• OpenTelemetry ingestion
• data export APIs
• schema compatibility
• migration tooling

This tells you how flexible the platform is today—and how portable your data will be tomorrow.

Can you test AI features before committing?
Hands-on evaluation helps you confirm accuracy and workflow fit.
Grafana Cloud, Last9, and Honeycomb provide free tiers with functional AI features so you can test without commercial commitments.

How Last9 Solves the Gaps AI Workflows Expose

If you’re building or running AI-driven systems, you need an observability platform that behaves like a high-fidelity data layer—something both engineers and AI agents can query without losing context. This is exactly where Last9 takes a different path from other vendors.

Last9 Named a Gartner® Cool Vendor in AI for SRE and Observability | Last9

Gartner recognizes Last9 in their latest Cool Vendor report for unified telemetry and agentic SDK—moving teams from reactive monitoring to proactive ops.

Last9Nishant Modak

MCP access for any LLM or agent

Instead of shipping a proprietary assistant, Last9 exposes your telemetry through the Model Context Protocol (MCP). You can ask questions from your IDE (Cursor, VS Code) or chat tools using any LLM you prefer:

• GPT-4
• Claude
• local models
• agent frameworks

We provide structured, high-fidelity telemetry; you bring the intelligence layer.
This lets AI agents analyze production signals directly, generate explanations, surface anomalies, or even propose fixes.

The architectural bet we’re making

As AI agents become part of daily debugging, the differentiator won’t be “who has the better assistant.” It’ll be who provides the cleanest, most complete data layer for those assistants to reason over.

Last9 is built for that world—preserving detail, enabling direct LLM access, and detecting patterns without waiting weeks for a baseline.

Try it side-by-side with your current setup

The free tier—100M events and 7-day retention—is designed for live evaluation. Run Last9 next to your existing tool, send the same telemetry to both, and compare:

• Which catches anomalies faster?
• Which keeps the dimensions you actually need?
• Which integrates better into AI workflows?

You’ll see the difference immediately.

Try Last9 today, and if you'd like, a detailed walkthrough book sometime with us!

FAQs

What's the difference between statistical algorithms and machine learning for anomaly detection?

Statistical algorithms (like Last9's Alert Studio) compare current data against historical windows using mathematical patterns. They work immediately but require you to select which algorithm fits your data pattern.

Machine learning (like Datadog Watchdog) trains models on your historical data to learn normal behavior. More accurate once trained, but requires initial baseline periods.

Both approaches are valid. Choose based on whether you need immediate results or can invest time for improved accuracy.

Why does high-cardinality data matter for AI-assisted debugging?

High-cardinality dimensions include user IDs, session tokens, and container IDs—attributes with many unique values. When debugging "why did checkout fail for user 12345?", you need that specific user_id preserved.

Traditional monitoring aggregates these away to control storage costs. You see average latency across all users—not helpful for finding specific failures.

AI agents querying via MCP need the same granular dimensions humans need. Platforms preserving high-cardinality data (like Last9's 60M+ time series/minute) enable more precise AI-assisted debugging.

How do MCP servers change observability?

MCP (Model Context Protocol) lets LLMs query observability data directly. Instead of vendors building proprietary AI, they provide APIs that external LLMs can call.

This means you ask Claude or GPT-4 questions about production from your IDE. The LLM fetches relevant telemetry and suggests fixes. You choose the AI model. The observability platform provides data access.

Last9, Honeycomb, and Chronosphere offer production MCP servers. This architecture is growing as teams integrate AI agents into development workflows.

Should I prioritize AI features when choosing monitoring?

AI features are valuable but not the primary decision factor. Choose based on:

• Pricing model fit (per-host vs. usage-based)
• Data retention requirements
• Integration ecosystem
• Vendor stability

AI capabilities are incremental improvements. Every platform in this analysis works well with traditional alerting. Teams getting value from Grafana's free ML or Last9's pattern detection see it as a bonus, not a requirement.

And if you're building AI agent workflows, then MCP integration and high-cardinality preservation become primary requirements.

What does "AI-native" actually mean?

The term varies by vendor. Some mean:

• Built-in ML models trained on your data (Datadog Watchdog)
• Causal AI analyzing topology (Dynatrace Davis)
• Statistical algorithms working immediately (Last9)
• MCP integration for external LLM access (Last9, Honeycomb)

Look past marketing to understand the technical implementation. "AI-native" might mean proprietary ML, statistical pattern matching, or architecture optimized for AI agent consumption. Each has different technical and cost implications.