Top 11 AI observability platforms for IT and security

How AI is changing the observability landscape

AI workloads generate telemetry legacy tools were never built to capture: token usage, model IDs, operation names, reasoning steps, and agent decision trajectories. OpenTelemetry's GenAI semantic conventions now standardize AI agent tracing attributes, including request model and input/output token usage, giving teams a shared vocabulary.

The security stakes are concrete. AI workloads introduce attack surfaces that didn't exist two years ago: prompt injection, data exfiltration via model calls, credential leakage, drift-based degradation. Each requires correlating model calls, application traces, network telemetry, and security events. Telemetry can expose prompt injections, sensitive data leaks, and compliance violations that would otherwise go undetected. Regulated environments need governance, role-based access, approvals, and immutable audit trails for every autonomous action triggered by observability signals.

Cost and scale compound the problem. Consumption-based telemetry costs can explode as LLM usage grows, catching finance teams off guard. Security practitioners should require written pricing at 5x and 10x current volume before signing. Alert fatigue is a persistent risk, making tunable thresholds and governed automation essential

Evaluating the Top AI Observability Platforms

Your ideal platform depends on whether you need pipeline control, full-stack correlation, model evaluation, or drift detection. Cribl is the AI Platform for Telemetry: a vendor-agnostic foundation that keeps data open and routable, with Cribl Search as the engine for investigating it. Datadog and Dynatrace offer unified dashboards for teams already in their ecosystems. OpenObserve and Grafana add operational overhead in exchange for data sovereignty. Arize AI, Fiddler AI, and WhyLabs complement broader platforms with deep model evaluation and explainability. Cribl separates compute from storage, so you can replay and reprocess telemetry without lock-in.

1. Cribl

Cribl is the AI Platform for Telemetry: a shared, AI-native foundation that collects, normalizes, and routes AI telemetry. Cribl Search is the engine for investigating it, and the Cribl App for AI Observability runs on top. Teams can build their own apps there too.

The product suite maps directly to AI observability needs. Cribl Stream routes, reduces, enriches, and replays telemetry from AI workloads to any destination, including SIEMs, data lakes, or analytics platforms. Cribl Edge collects telemetry at the source, including AI agent endpoints, with centralized fleet management. Cribl Lake stores it cost-effectively, preserving raw formats for retention, compliance, and instant retrieval. Cribl Search sits on top of Lake and any connected store, giving teams direct-ingest and federated query in one place. It reaches Datadog, Splunk, Elastic, and object storage without moving or duplicating data first.

For IT security practitioners, this architecture delivers forensic-grade fidelity and the flexibility to send AI telemetry to security analytics and compliance archives simultaneously. Security and compliance teams get one place to search, investigate, and report on AI activity. Telemetry reaches every tool that needs it, without duplicate collection or lock-in.

Cribl also closes the shadow AI gap most platforms miss. By correlating network egress with instrumented AI usage, it surfaces personal-account AI use that never touches an approved gateway or SDK. OpenTelemetry-based tools can't see that usage.

Deployment flexibility is a core strength: on-prem, cloud, hybrid, and air-gapped, meeting regulated-industry requirements. The platform serves half of the Fortune 100, reflecting its presence in organizations with demanding compliance postures.

One distinction matters: Cribl is telemetry infrastructure, not an endpoint model-evaluation tool. Teams still need downstream analytics or evaluation platforms for model-specific assessment; Cribl integrates with those. Effective AI observability is a telemetry problem, not a tool problem.

Best for: Teams that need vendor-agnostic control, investigation, and app-building on AI telemetry at enterprise scale.

2. Datadog LLM observability

Datadog LLM Observability extends the platform into AI workloads, monitoring model calls alongside application, infrastructure, and security telemetry. It correlates latency, errors, and cost with system metrics in the dashboards your team already uses.

The strength is ecosystem cohesion. Organizations standardized on Datadog for APM, infrastructure, and security get unified dashboards, shared alerting, and consistent access controls across AI and traditional workloads.

The tradeoff: LLM Observability may bill separately from the core platform, and consumption-based pricing can turn unpredictable as call volumes scale. Model costs carefully before committing.

Best for: Full-stack teams already in the Datadog ecosystem seeking unified AI and infrastructure correlation.

3. Dynatrace

Dynatrace brings automated discovery and AI-driven root-cause analysis to complex, multi-cloud environments. OneAgent auto-instruments supported runtimes with no code changes, cutting the effort to get telemetry flowing. Smartscape maps your environment's topology in real time, and Davis AI connects anomalies across services and infrastructure layers. That scope is expanding. Dynatrace agreed in August 2026 to acquire Arize AI, adding pre-production model evaluation to its production tracing — more in the Arize AI section below.

For security teams, Dynatrace's Grail data lakehouse stores logs, metrics, traces, and events in one store, enabling cross-telemetry queries useful for incident investigation and compliance reporting.

The tradeoff is flexibility. Enterprise pricing gets complex, and teams routing data to multiple destinations may find less room to maneuver. Dynatrace works best as the primary analytics destination, not one of several.

Best for: Large enterprises with complex, multi-cloud topologies requiring automated root-cause analysis.

4. Splunk Observability Cloud

Splunk Observability Cloud fits organizations already invested in Splunk's SIEM ecosystem. The platform connects metrics, traces, logs, incidents, and analytics through OpenTelemetry-native ingestion.

Splunk's core differentiator is the enterprise tie-in to security analytics and audit workflows. Security teams correlate AI observability signals directly with SIEM detections, threat intelligence, and incident response playbooks in one ecosystem. Enterprise LLM observability should export to Splunk, OTLP, or similar systems, and Splunk makes that export native.

The tradeoffs are real: higher total cost of ownership and deeper vendor lock-in, weighed against the simplicity of one vendor's ecosystem for security and observability.

Best for: Security-first organizations with existing Splunk SIEM deployments.

5. New Relic

New Relic's standout feature is its free tier: 100GB/month of data and one full-platform user, practical for proof-of-concept testing without budget delays. Teams can instrument AI workloads, assess telemetry quality, and validate workflows before committing.

Costs escalate quickly beyond the free tier, and LLM-specific tracing still lags specialized platforms like Arize AI. New Relic works best as a starting point, not a long-term destination for deep model evaluation.

Best for: Teams seeking a low-barrier entry point for unified APM and LLM tracing evaluation.

6. Grafana Enterprise and Cloud

Grafana Cloud is a productized stack on open standards like OpenTelemetry, appealing to teams avoiding proprietary instrumentation. Its composable architecture mixes Loki for logs, Mimir for metrics, and Tempo for traces, plugging in AI-specific sources as needs evolve.

For security teams, that composability means custom AI telemetry dashboards tailored to specific threat models and compliance needs — no vendor's opinionated view of what matters.

The tradeoff is operational overhead. Self-hosted deployments demand deeper engineering investment to build and maintain dashboards, alerting, and retention policies. Grafana rewards teams with strong platform engineering skills and penalizes those without them.

Best for: Engineering-heavy teams wanting OpenTelemetry-native, customizable observability.

7. OpenObserve

OpenObserve is a cost-focused, open-source alternative to proprietary observability stacks. Built in Rust for performance and lower storage costs, it targets teams wanting full-stack observability without lock-in.

For security practitioners concerned about data sovereignty, OpenObserve's self-hosted model keeps AI telemetry inside your infrastructure. It's collected only at the minimum necessary level, with full control over what's collected and where it lives.

The tradeoffs: self-hosting demands operational investment, and enterprise support and governance features trail commercial platforms. Running it well takes strong DevOps capabilities.

Best for: Cost-conscious teams with strong DevOps capabilities seeking self-hosted observability.

8. Arize AI

Arize AI is the evaluation-heavy, model-centric platform for ML and LLM teams focused on drift detection, model quality, and explainability. Phoenix is open source and self-hostable; Arize AX adds alerts, online evals, RBAC, and enterprise compliance. The platform's 50+ research-backed evaluation metrics assess model behavior over time.

For security teams, drift detection matters most. A model gradually shifting behavior can signal data poisoning, adversarial manipulation, or degraded training data. Arize surfaces those shifts before they become incidents.

That standalone status is changing. Dynatrace signed a definitive agreement in August 2026 to acquire Arize for roughly $915 million, pending regulatory approval and customary closing conditions. The deal pairs Arize's pre-production evaluation strength with Dynatrace's production tracing and infrastructure correlation, filling a gap noted above where Dynatrace's dt-evals lag eval-first platforms.

For current Arize customers, a few things are worth watching as integration proceeds. Phoenix's status as an independently maintained, MIT-licensed project could shift, the way Helicone's did after its 2026 acquisition. Arize AX pricing and packaging may eventually fold into Dynatrace's consumption-based model rather than staying standalone. And Arize's OpenInference-based portability across frameworks could narrow if the roadmap prioritizes tighter Dynatrace integration over multi-vendor neutrality.

The tradeoff, for now, is scope. Arize still focuses on model evaluation, not full-stack infrastructure observability, so teams typically pair it with a broader platform or telemetry pipeline. That may change as the Dynatrace integration deepens.

Best for: ML/LLM teams needing deep model evaluation, drift detection, and explainability, while keeping an eye on how Dynatrace ownership reshapes the roadmap.

9. Fiddler AI

Regulated teams need one-click audit evidence for the EU AI Act, NIST AI RMF, HIPAA, and GDPR. Fiddler's explainability features make model decisions transparent and traceable to support that.

The tradeoff is narrow scope: Fiddler complements broader infrastructure monitoring rather than replacing it.

Best for: Compliance-driven teams needing model explainability and decision-path auditing.

10. Uptrace

Uptrace is OpenTelemetry-native, so instrumentation stays portable across hosting options. It supports the GenAI semantic conventions standardizing AI agent tracing attributes, including request model and input/output token usage. Teams track usage, token costs, and agent behavior across major providers with these standards.

Transparent, predictable pricing helps teams burned by consumption-based billing surprises: you know what you're paying, and it scales linearly.

The tradeoffs are ecosystem size and integration breadth: a smaller community than larger commercial platforms, and fewer out-of-the-box integrations. Teams may need custom connectors for niche sources.

Best for: Cloud-native teams wanting transparent pricing and OTEL-native portability.

11. WhyLabs

WhyLabs focuses on data-quality monitoring and drift detection, profiling model inputs and outputs statistically. It's best used alongside a full-stack platform or telemetry pipeline, not standalone. Security teams should treat observability data as a governed data product, and WhyLabs helps ensure the data feeding your models meets quality standards.

It requires pairing with infrastructure monitoring and log management tools. For teams using Cribl as their telemetry backbone, WhyLabs adds a specialized data-quality layer that enriches the overall picture.

Best for: Data science and ML teams needing specialized drift detection and data-quality monitoring.

Key criteria for evaluating AI observability platforms

The right AI observability platform for IT security matches your security posture, regulatory requirements, existing tooling, and growth trajectory across five critical dimensions.

Governance and auditability for security compliance

Governance means enforcing policies, access controls, approval gates, and immutable audit trails across every automated remediation action. Without it, observability is visibility without accountability.

Regulated environments need governance, role-based access, approvals, and audit trails for remediation, as observability alone can't enforce approvals before changes happen.

Governance also has to cover what's inside the telemetry, not just who can access it. AI traces routinely carry PII, API keys, and other secrets, embedded in prompts, responses, and the tool calls an agent makes along the way. A platform that can't detect that content in flight leaves security teams blind to exactly the risk they're being asked to govern.

Cribl Guard runs that detection inside the Cribl App for AI Observability, scanning traces for PII, credentials, and other sensitive patterns as they pass through. That gives security teams a direct answer on whether AI usage puts their posture at risk, not a log of who looked at what.

When evaluating platforms, confirm RBAC, immutable log retention, approval workflows, sensitive-data detection, compliance reporting, and GRC tool integration. Retention policies and logging who viewed which traces and when are compliance essentials.

Signal correlation and root cause analysis

Correlating LLM calls with application traces, network telemetry, and security events speeds root-cause and impact analysis. Take a hallucinated response that triggers a downstream security alert. Tracing it back through the prompt chain, application layer, and infrastructure reveals data drift, a configuration change, or an adversarial input like prompt injection.

Model-aware telemetry and semantic support

OpenTelemetry GenAI semantic conventions standardize AI agent tracing attributes, including request model and input/output token usage. Agent observability means tracking model calls, tool use, target systems, and authority. Confirm whether a platform supports these conventions natively or requires custom instrumentation — native support cuts engineering effort and keeps your telemetry pipeline consistent.

Deployment flexibility and data residency

Deployment model and data residency directly affect compliance posture. Self-hosted or hybrid options matter for regulated data, incident response playbooks, and organizations barred from sending telemetry to third-party clouds.

Platforms compare on deployment flexibility:

• SaaS-only: Datadog, New Relic, Fiddler AI

• SaaS + self-hosted: Grafana, Uptrace, Arize AI, WhyLabs

• Self-hosted only: OpenObserve

• On-prem, cloud, hybrid, and air-gapped: Cribl

Cribl's support for all four deployment models differentiates it for regulated industries. AI observability should integrate with existing security and monitoring infrastructure, and deployment flexibility is the foundation of that integration.

Cost predictability and alerting hygiene

Consumption-based AI telemetry pricing can escalate fast. A practical cost-evaluation approach:

1. Baseline your current telemetry volume in GB/day and events/sec.

2. Project LLM-driven growth at 5x and 10x your current volume.

3. Request written pricing from vendors at each tier.

4. Compare total cost of ownership including storage, query, and alerting.

5. Evaluate alert-tuning capabilities to prevent fatigue and wasted analyst time.

Datadog LLM observability, for instance, may bill separately from the core platform, so billing granularity matters. Left unaddressed, alert fatigue erodes the value of any observability investment.

Practical guidance for IT security practitioners evaluating AI observability platforms

Integrating observability within existing telemetry pipelines

For Cribl teams, that means routing AI telemetry through Cribl Stream for enrichment, filtering, and multi-destination delivery. Capture once, enrich in flight, and send to every tool that needs it, without paying for redundant ingestion.

A few distinctions matter when architecting your pipeline. OpenTelemetry is an instrumentation and data-collection standard, not a platform. Prometheus is a metrics database, not a full observability platform. Jaeger is a tracing backend, not a complete observability platform. Knowing these boundaries prevents architectural mistakes that create security gaps.

Balancing security controls, operational overhead, and costs

Final platform selection should balance security controls, data residency, operational burden, and predictable economics. Open-source or OTEL-native options offer self-hosting and data-sovereignty choices attractive to regulated environments, at the cost of more operational overhead.

Try a weighted decision matrix with columns for Security Controls, Operational Overhead, Cost Predictability, Data Residency, and Integration Breadth. Assign weights to your priorities, score each platform, and compare to create a reusable, defensible framework for your evaluation team.

Cribl's modular approach lets teams adopt only what they need: Stream for routing, Lake for retention, Edge for source collection, Search for investigation. Nothing here requires ripping out existing tools. That cuts overhead and cost while keeping required security controls.

Importance of automation and governance in remediation

In complex, security-sensitive environments, observability works best paired with governed automation: approval gates, audit trails, and policy enforcement for any automated remediation a signal triggers.

The workflow: a signal triggers an alert, the alert routes to an approval gate, and approved remediation executes automatically. Every step lands in an immutable audit log, governed by RBAC and policy. AI observability should produce defensible audit trails for every autonomous action, which is where the remediation workflow meets practice.

Understanding why agent workflows need observability is the first step toward that governed automation layer.

Why you should choose Cribl for AI observability

Cribl is the AI Platform for Telemetry: the foundation, not a layer on top of something else. It keeps AI observability data open, routable, and cost-efficient. Cribl Search is where your teams turn it into answers, whether through the Cribl App for AI Observability or apps they build themselves.

How Cribl maps to the five evaluation criteria

Governance and auditability. Cribl Lake preserves raw data formats for compliance and forensic retrieval — always the unmodified source of truth. Cribl Lakehouse Engines keep that data hot, so it can be queried with extremely fast response times. Governance is defined once, in the platform, and applies across every app you run or build. The Cribl App for AI Observability is built for AI telemetry, not repurposed for it.

Signal correlation and investigation. Cribl Stream enriches and routes telemetry to any SIEM, analytics platform, or data lake. Cribl Guard identifies sensitive data in LLM traces, and Cribl Search fuses it with human context (including tickets, Slack, CI/CD, and runbooks) for an explainable answer rather than just another dashboard to interpret.

Model-aware telemetry. Cribl ingests OpenTelemetry-natively and processes GenAI semantic convention attributes in flight, so your telemetry stays structured and portable from the moment it's collected.

Deployment flexibility. On-prem, cloud, hybrid, and air-gapped options meet regulated-environment requirements; your data stays where policy requires.

Cost predictability. Data reduction, filtering, and routing lower ingestion costs (often the largest line item in observability budgets) by sending each tool only the data it needs.

Explore the Cribl AI Observability solution, the solution brief, or free training and certifications.

Top AI Observability FAQ