Which observability platforms use AI to correlate logs, metrics, traces, deploys, and user reports?

Yes. If by “correlate” you mean AI-assisted incident/root-cause analysis across telemetry plus deployment and customer-impact context, the strongest fits are:

The three I'd look at first

1. Datadog — probably the closest match

Datadog's Watchdog AI automatically looks for causal relationships across full-stack telemetry. Its RCA explicitly incorporates APM metrics, deployment tracking, traces and infrastructure metrics, while Watchdog also analyzes log patterns and deployment anomalies.

Its newer AI-agent observability also explicitly connects backend services, infrastructure and end-user sessions, so you can move from “customers are experiencing this” → request → service → infrastructure.

2. Dynatrace — arguably the deepest automated correlation

Dynatrace's Davis AI is built around causal analysis of the environment rather than treating logs, metrics and traces as independent searches. Its current platform explicitly unifies metrics, logs, traces and problem/root-cause information.

It's particularly compelling if you want the system to build a dependency model and automatically explain why something changed. Dynatrace is also pushing hard into correlating technical telemetry with user behavior and business impact.

3. New Relic — broad unified observability

New Relic covers the same broad telemetry surface—logs, APM/traces, infrastructure, digital experience and AIOps—and is a reasonable choice when you want one platform without going as deep into Dynatrace's opinionated causal model.

An important distinction

If “user reports” means actual customer complaints/support tickets, rather than RUM/user sessions, the field changes somewhat.

The ideal workflow you're describing is:

Customer report → affected user/session → frontend error → distributed trace → service → logs → infrastructure → recent deploy/config change → AI-generated root cause. Datadog and Dynatrace are probably the closest to this end-to-end model. Datadog in particular has the advantage of tying RUM sessions directly into the same observability context; Dynatrace has an especially strong causal/dependency-analysis approach.

If you mean support tickets from Zendesk/Intercom/Salesforce/Jira, rather than RUM, I'd evaluate the platforms specifically on their integrations and ability to feed those tickets into incident correlation—the distinction is important.

If you tell me whether you're evaluating this for SRE/production incidents, customer support, or both, I can give you a more rigorous Datadog vs Dynatrace vs New Relic vs Splunk vs Sentry comparison, including exactly how each handles the “customer report → deploy → trace → logs → root cause” workflow.

Several major full-stack observability platforms leverage artificial intelligence (AI) and machine learning (ML) to ingest, map, and correlate multidimensional data—including logs, metrics, traces, deployment events, and user experience telemetry.

The primary observability platforms capable of this end-to-end correlation include:

- **AI Engine:** Davis AI / Davis CoPilot
- **How it correlates:** Dynatrace relies on a deterministic real-time dependency topology (Smartscape) combined with Davis AI. It automatically ingests metrics, logs, traces, and point-in-time events like software deployments, configuration changes, and infrastructure shifts. When an anomaly occurs, Davis instantly correlates these streams to isolate the exact root cause (e.g., pointing directly to a faulty deployment or a specific code trace) rather than flooding operators with alert noise.[](https://google.com/goto?url=CAESoQEB6zswFZe--B0_bepTLQS5MVrwozC22R7i_j_wtV8s956q9ukedFTyNAuVfcSIwW3t_x1nzaPTXtcJkiWZqOs4IsUmnGPPgJEXV5yNlGpMuzmVkrXlcLXmsn6Vn9J_d7tkPTBktQ1BlxkukZdObc1EhoytgqYQfsTxddN2k_frg54ResT8hjls6PSotluqCbEh1uh_a0OIWXBjVlDbSoWrLA) [[1]](https://google.com/goto?url=CAESoQEB6zswFZe--B0_bepTLQS5MVrwozC22R7i_j_wtV8s956q9ukedFTyNAuVfcSIwW3t_x1nzaPTXtcJkiWZqOs4IsUmnGPPgJEXV5yNlGpMuzmVkrXlcLXmsn6Vn9J_d7tkPTBktQ1BlxkukZdObc1EhoytgqYQfsTxddN2k_frg54ResT8hjls6PSotluqCbEh1uh_a0OIWXBjVlDbSoWrLA)[[2]](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA)[[3]](https://google.com/goto?url=CAESngEB6zswFfYIC-ptTo_Wdz2eGuXh4Uk-6GE4RtEO95i3KHNbR1KWitqEgDHRsWJ9EH5fRGBqgHzeM1P9AQ4Xrb6nxDe_pdbIkbhfqRIyKR_Ni4xt2RcJRf709lk6npfb55TrMwhEGuKWPM6uIgHQPtqt--ueUMP1N3t3ZzuYNx4q6_vqK-YfHjv1wq0oxqkjt9AtmzpNToKKSN_vL_w7gg)

- **AI Engine:** Watchdog / Bits AI
- **How it correlates:** Datadog unifies APM traces, infrastructure metrics, security signals, and log management into a single platform. Watchdog uses AI/ML to automatically detect performance anomalies, infrastructure drifts, and error spikes across services without requiring manual threshold setup. By mapping deployments (via version tracking) and user-centric data (Real User Monitoring/RUM and session replays) alongside traces and logs, Datadog surfaces related telemetry context when an incident triggers.[](https://google.com/goto?url=CAESegHrOzAVO_B8IlDAz7VEyfofSzZSZTL42bSl5qs0n0dhkZmHy1Wl_JhSXgWgSoEvNy141KmCFtIsfUS1wtEV1cU7qiXjMNH2OMhVjq9QeYzg6aeC4ma_pucNRxErXHOYJ5Ss5uEoGRQ5DV3zNYCMRhxuO-cmz_rxbJjs) [[1]](https://google.com/goto?url=CAESegHrOzAVO_B8IlDAz7VEyfofSzZSZTL42bSl5qs0n0dhkZmHy1Wl_JhSXgWgSoEvNy141KmCFtIsfUS1wtEV1cU7qiXjMNH2OMhVjq9QeYzg6aeC4ma_pucNRxErXHOYJ5Ss5uEoGRQ5DV3zNYCMRhxuO-cmz_rxbJjs)[[2]](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA)[[3]](https://google.com/goto?url=CAESgwEB6zswFTFM8DylZbk48rFszmkBL8xHzfcIept8a-DE3zb0GU2coMZ96JLpwQpR8P0y3FIfg42NhTjG9mR_UTNOFFrzgM4mVVrlbJjlEyrCiz-OwbROoUIRZzSzDHo8rl-PteKVNw8l097fyUyAHYYY6S7TFaXdI3_lH7mziggZ_BhCwA)

- **AI Engine:** New Relic AI / Applied Intelligence
- **How it correlates:** New Relic provides unified telemetry data covering logs, metrics, traces, and deployment markers. Its applied intelligence layer automatically groups related alerts, detects changes in system behavior post-deployment, and uses anomaly detection to map symptoms across the entire stack to accelerate root-cause isolation.[](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA) [[1]](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA)

- **AI Engine:** Splunk IT Service Intelligence (ITSI) & Splunk AI Assist
- **How it correlates:** Particularly strong in enterprise, log-heavy environments, Splunk correlates infrastructure metrics, application traces, and change events (such as CI/CD deployment markers and infrastructure tickets). It uses machine learning models for adaptive thresholding, anomaly detection, and event analytics to tie user-impacting behavior down to underlying system logs and changes.[](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA) [[1]](https://google.com/goto?url=CAESaQHrOzAVmj4kM5tTw-d_qHZBr-ZlMNEGjrQe_4AKxKTimMsWjbFGt9CXv_XDP73ZlHDcKrfpOs6rgtw1I2nYG6EkFqW3J5WLLLjC26LpyhLbEoOVNfUg7LMiv2Nh9cp53vbs1cS0EL8DRA)

Would you like to explore how one of these platforms specifically handles OpenTelemetry integration for data collection, or compare their pricing/cost models for log and trace retention?

Yes. If by “use AI to correlate” you mean an AI layer that can reason across multiple telemetry types—not merely an anomaly detector on one signal—there are several serious options.

The closest matches to your exact description

1. Datadog is probably the closest literal match today. Its Bits Investigation agent can reason across metrics, APM traces, logs, dashboards, events, Change Tracking, source code, RUM, network, databases, and profiling when investigating an incident.

That makes it particularly interesting if your “user reports” means things like “customers started seeing checkout errors after the 14:32 deployment.” The AI can potentially connect the user-facing symptom to the backend telemetry and the change that preceded it.

2. Dynatrace is arguably the strongest competitor if you want causal/root-cause reasoning and automated topology rather than primarily a conversational copilot. Its AI is designed around correlating telemetry and dependencies across the stack; Dynatrace describes its platform as AI-powered observability and explicitly emphasizes automatic root-cause identification.

3. Splunk is particularly compelling if “user reports” includes IT/service-desk incidents, security signals, business events, and operational data in addition to conventional telemetry. Splunk says its platform correlates metrics, traces, logs and real-world events, with AI-powered alerting and troubleshooting.

An important distinction

There are really three levels of “AI observability” being marketed:

1. AI anomaly detection — “This metric looks unusual.”
2. AI correlation / investigation — “This error spike correlates with these traces, logs, deployment and affected users.”
3. AI SRE agent — “I investigated the evidence, identified the likely root cause, proposed/applied a fix, and documented the incident.”

Datadog and Dynatrace are particularly interesting at #3. Datadog's current Bits Investigation, for example, explicitly reasons across telemetry and can determine, triage, and remediate production incidents.

If your real requirement is specifically “feed the platform logs + metrics + traces + Git/deploy events + PagerDuty/Slack + customer support tickets/user complaints, and have AI produce one correlated incident/root-cause narrative,” the shortlist gets narrower. Datadog, Dynatrace, and Splunk would be my first three to evaluate, with New Relic as a strong fourth.

2. Dynatrace is arguably the strongest competitor if you want causal/root-cause reasoning and automated topology rather than primarily a conversational copilot. Its AI is designed around correlating telemetry and dependencies across the stack; Dynatrace describes its platform as AI-powered observability and explicitly emphasizes automatic root-cause identification.

Several major enterprise observability platforms utilize artificial intelligence, machine learning, and causal reasoning engines to ingest, parse, and cross-correlate multi-signal telemetry—including logs, metrics, traces, deployment events, and user-facing/synthetic data.

The primary platforms offering these unified AI correlation capabilities include:

• Dynatrace — Powered by its Davis® AI engine and the Grail™ hypermodal storage repository, Dynatrace automatically maps your entire application topology. Davis continuously analyzes and correlates logs, metrics, traces, and environment changes (such as deployment and configuration events) to deliver deterministic root-cause analysis rather than just probabilistic alerts.
• Datadog — Features Bits AI and Bits Investigation , an autonomous SRE agent. Bits natively correlates APM traces, infrastructure metrics, error logs, deployment markers, and synthetic user test failures to formulate root-cause hypotheses and guide engineers through triage.
• New Relic — Utilizes Intelligent Root Cause Analysis (iRCA) , which overlays operational symptoms onto a real-time dynamic topology graph. It uses causal modeling to tie application telemetry, logs, metrics, and deployment changes together while filtering out cascading noise to isolate the exact source of a failure.
• Grafana Labs — Features Grafana Asserts and integrated machine learning features across Grafana Cloud. It automatically maps out application and infrastructure topology, correlating metrics, logs, and traces alongside incident and deployment events to surface anomalies and reduce mean time to resolution (MTTR).

Would you like a deeper dive into how one of these specific platforms (like Dynatrace or Datadog) structures its AI data ingestion pipeline, or are you comparing them for a particular tech stack?

Yes. If by “AI to correlate” you mean more than anomaly detection—i.e. automatically connecting logs + metrics + traces + deployments/events + customer/user-reported symptoms into an incident or root-cause hypothesis—the strongest platforms to evaluate are:

1. Dynatrace — closest to what you're describing

Dynatrace is probably the closest conceptual match. Its Intelligence/Davis AI correlates telemetry into “problems,” including metrics, logs, traces and events, and performs automated root-cause analysis. Its current platform explicitly describes automatically correlating traces, events, metrics and logs across data silos.

It also has RUM and AI observability that can connect end-user behavior → application → backend traces → infrastructure, which is particularly useful when the initial signal is “customers are complaining” rather than an infrastructure alert.

Best if: you want the platform to reason about relationships and probable root cause, rather than merely put all telemetry in one search interface.

---

2. Datadog — probably the strongest practical alternative

Datadog has perhaps the broadest single-platform coverage: infrastructure, APM, logs, distributed traces, RUM, Session Replay, synthetics, deployment/CI events and incident management.

The particularly interesting part for your use case is the ability to move from a user-facing problem into backend telemetry. For example:

user complaint → RUM session → frontend error → API request → distributed trace → service → logs → deployment

That correlation-first approach is a major strength of Datadog.

Best if: you want one operational workspace spanning SRE, developers, frontend and incident response, with a huge integration ecosystem.

---

3. New Relic

New Relic has long had a particularly strong application/user-centric model, combining logs, metrics, traces, browser/mobile monitoring and application performance.

It's worth considering if the workflow you're envisioning starts with:

“Users are reporting that checkout is broken—what changed, and which backend component explains it?”

rather than:

“CPU is anomalous—what happened?”

Third-party comparisons currently put New Relic alongside Datadog and Dynatrace as the major full-stack observability platforms.

---

4. Splunk

Splunk is particularly interesting if user reports, logs and business events are important.

Its observability platform emphasizes correlating telemetry with real-world/business events, and Splunk explicitly supports correlation of metrics, traces, logs and events.

Best if: your organization already has a substantial Splunk/SIEM footprint and wants observability and security investigations to converge.

---

The important distinction

There are actually two different categories hiding inside your question:

A. Unified observability

“Put logs, metrics, traces and deploys together and let engineers investigate.”

Datadog, New Relic, Splunk, Grafana, Elastic, Honeycomb, etc. do this.

B. AI-driven causal correlation

“These 17 alerts, 3 deployments, 42 traces, 8 log patterns and a spike in checkout complaints are probably one incident. The likely cause is service X, introduced by deployment Y.”

That's a more ambitious capability. Dynatrace is the clearest fit, with Datadog and New Relic also worth evaluating.

Dynatrace's current positioning is unusually explicit about this: it says its AI automatically detects problems and correlates observability signals for root-cause analysis rather than requiring manual cross-tool analysis.

If “user reports” means actual support tickets

That's an even more interesting distinction. If you mean:

Datadog/Dynatrace telemetry + Jira/ServiceNow tickets + PagerDuty incidents + GitHub deployments + Slack/Teams reports + customer complaints

then you're really looking for an AI incident intelligence / SRE copilot layer, not just an observability platform.

I'd shortlist Dynatrace, Datadog, Splunk and New Relic, but evaluate specifically whether their AI can ingest and correlate unstructured human signals (tickets, Slack, support cases) with telemetry—not merely RUM data.

If that's the workflow you're after, I can also map out 10–15 platforms that specifically do “telemetry + deployment + incident + support-ticket correlation”, including newer AI-native entrants rather than just the traditional observability vendors.

Several enterprise-grade observability and product platforms use native artificial intelligence and machine learning engines to ingest, parse, and correlate traditional telemetry (logs, metrics, and traces ) alongside change events (deploys ) and customer-facing touchpoints (user feedback/reports/session data).

The primary platforms capable of multi-signal and user-context correlation include:

• Dynatrace : Utilizes its Davis AI engine on top of a unified data lakehouse (Grail ). It automatically discovers service topologies (Smartscape ), correlates metrics, logs, and traces, layers in deployment and configuration changes, and pulls in real user monitoring (RUM) and user feedback data to isolate root causes.
• Datadog : Employs its Bits AI SRE and investigation agents to cross-examine streams from APM traces, infrastructure metrics, log management, CI/CD deployment markers, and synthetic or real user monitoring data during active incidents.
• New Relic : Combines full-stack telemetry (logs, metrics, events, and traces) in a unified database managed by its New Relic AI/SRE Agent suite. It explicitly correlates system performance changes and token/cost metrics with user feedback and deployment boundaries.
• PostHog : Uniquely bridges product analytics, error tracking, and session replay with OpenTelemetry-native logs, metrics, and traces. Its PostHog AI layer connects front-end user sessions, user surveys, and feature flag/deployment states directly to back-end errors and trace waterfalls.

If you are evaluating these for a specific team, let me know:

• Is your stack primarily cloud-native/Kubernetes or heavily reliant on AI/LLM application layers?
• Do you need an agentic AI that automatically investigates alerts or a tool that unifies product analytics with infrastructure?

I can help you narrow down which platform fits your workflow best.

Yes. If by “use AI to correlate” you mean one platform can ingest logs + metrics + traces + deployment/change events + user-impact signals, then use AI/AIOps to connect them into an incident/root-cause narrative, the strongest options in 2026 are:

The two closest fits

1. Dynatrace — probably the closest to what you're describing.

Its current platform explicitly correlates logs, traces, metrics and events into problems and uses Dynatrace Intelligence for automated detection and root-cause analysis. Its platform also maintains topology/context around applications and infrastructure.

That makes it particularly good if the desired workflow is:

“Users are reporting checkout failures → correlate those reports with RUM → identify the affected service → correlate traces/logs/metrics → notice a deployment/change → determine probable root cause.”

2. Datadog — arguably the best breadth.

Datadog combines infrastructure, APM, logs, RUM/Session Replay, deployments/events and incident management. Its AI capabilities include Watchdog and Bits AI, which can investigate alerts and assemble related telemetry/events into an incident context.

This is particularly attractive if user reports/RUM are an important part of the correlation, because you can move from an affected user/session into the backend trace and then into the relevant service, logs and infrastructure.

An important distinction

There are actually two different things vendors call “AI correlation.”

Correlation/AIOps platforms such as Dynatrace are trying to answer:

What underlying change caused these seemingly unrelated symptoms?

They build topology and causal relationships among telemetry, changes, dependencies and services.

AI investigation assistants such as Datadog Bits AI or New Relic AI are more like:

Here are the signals associated with this incident; investigate them and tell me what you think is happening.

Both are useful, but if your goal is automatic incident stitching, I'd put Dynatrace and Datadog at the top of the shortlist.

If “user reports” means actual support tickets

That's an especially interesting requirement. Most observability products are very good at correlating RUM/user sessions, but fewer natively treat a human report such as:

“Customer ABC says checkout has been failing since 10:15.”

as another first-class signal.

For that use case, I'd look for a platform that can ingest Zendesk/ServiceNow/Jira/PagerDuty/incident-management events alongside telemetry, rather than simply buying an APM product with RUM.

If you tell me whether you're trying to replace an existing observability stack, build an internal “AI incident investigator,” or evaluate vendors for purchase, I can narrow this to the 3–4 platforms that best fit and compare their actual correlation/AI capabilities.

The leading enterprise observability platforms use native artificial intelligence and machine learning engines to ingest, map, and correlate logs, metrics, traces, deployments, and user/incident data.

The primary platforms capable of this end-to-end correlation include:

• Dynatrace uses its Davis AI engine built on the Grail data lakehouse. Dynatrace automatically maps a real-time topology of your environment (Smartscape) and uses causal AI—rather than statistical guesswork—to deterministically tie infrastructure metrics, application traces, logs, and user sessions (Digital Experience/RUM) back to specific code deployments or configuration changes.
• Datadog relies on Watchdog (for automated anomaly detection and root cause analysis) and Bits AI (conversational SRE and investigation agents). Datadog natively aggregates and correlates metrics, distributed traces, and log data alongside deployment tracking and real-user monitoring (RUM), allowing teams to query the entire stack or use AI agents to run parallel investigations.
• New Relic features New Relic AI and its Ground Truth operational intelligence layer. It merges infrastructure data, APM traces, logs, browser/mobile user experiences, and deployment markers into a unified telemetry structure to automate root cause analysis and execute SRE workflows via conversational or autonomous agents.
• OpenObserve is an open-source/enterprise cloud platform that provides native cross-signal correlation. Its O2 AI Assistant and AI SRE Agent analyze logs, metrics, and traces simultaneously to compare behavior across deployments, summarize anomalies, and map out automated root cause incident reports.

If you are comparing these for your team, let me know:

• Is your stack primarily Kubernetes/Cloud-Native or a hybrid enterprise environment?
• Do you prefer a SaaS solution or an open-source/self-hosted option?

I can help you break down the pricing trade-offs or setup complexity for your specific use case.

Several leading enterprise observability platforms integrate machine learning and generative AI engines to ingest, stitch, and correlate operational telemetry (logs, metrics, and traces) alongside contextual deployment events and end-user experience or feedback data.

The primary platforms offering these unified AI correlation capabilities include:

• Datadog uses its Watchdog engine for unsupervised anomaly detection across logs, metrics, and traces, combined with Bits AI agents that autonomously parse release validations, deployment changes, and Real User Monitoring (RUM) / user feedback sessions during incidents.
• Dynatrace features its causal AI engine, Davis AI , built directly on top of its Smartscape topology map and Grail data lakehouse. It automatically correlates metrics, logs, traces, and user sessions with deployment and configuration change events to deliver deterministic root cause analysis.
• Splunk via the Splunk Observability Cloud , utilizes its AI SRE / Troubleshooting Agent to ingest OpenTelemetry data and continuously correlate logs, metrics, events, and traces while mapping them against system alterations to automate triage and remediation suggestions.
• New Relic employs New Relic Applied Intelligence (NRAI) to stitch together full-stack telemetry (MELT: metrics, events, log data, and traces) with changes across the application stack, mapping technical anomalies directly to front-end user impact and digital customer journeys.
• Elastic Observability leverages purpose-built machine learning models and its AI Assistant for Observability (using RAG) to correlate logs, metrics, traces, and business events inline, reducing noise and providing contextual insight into application behavior.

If you're evaluating these for a specific environment, let me know:

• Are you primarily a Kubernetes/Cloud-native shop or a hybrid enterprise?
• Do you have a preference for OpenTelemetry-native or proprietary agents?

I can help you compare their pricing models and implementation overhead for your team.

Several modern observability platforms are adding AI capabilities to correlate logs, metrics, traces, deployments/changes, alerts, topology, and sometimes customer/user reports into a single incident narrative or root-cause analysis workflow. The strongest matches are:

A few newer AI-native platforms are explicitly built around incident investigation agents rather than dashboards:

• Sazabi — focuses on AI-driven incident investigation, using operational context to identify causes and provide actionable fixes.
• Ceburu — markets an AI-native incident view that correlates metrics, logs, traces, deploys, and root-cause analysis into a timeline.
• NirikshaAI — focuses on AI correlation of logs, traces, metrics, and automated root-cause detection.

For user reports/customer-impact signals, the leaders are usually platforms that combine observability with incident management or customer experience data:

• Dynatrace — strong at connecting technical problems to user impact through service and dependency modeling.
• Datadog — strong when combined with RUM, logs, traces, events, and incident workflows.
• New Relic — strong for tying application telemetry to end-user experience.

A typical AI-driven workflow these platforms aim for looks like:

1. User reports: “checkout is slow”
2. AI links complaint → affected service → latency metrics
3. It correlates the spike with traces and error logs
4. It checks recent deploys/config changes
5. It identifies likely root cause (“payment-service deploy increased DB latency”)
6. It proposes remediation or creates an incident summary

If your goal is specifically “AI SRE that investigates incidents automatically and tells engineers what changed and why”, the shortlist is usually:

• Dynatrace
• Datadog
• New Relic
• Elastic
• Observe
• Sazabi/Ceburu-style AI-native tools

If your goal is “build this yourself on OpenTelemetry + AI agents”, the architecture choices are different (telemetry lake + embeddings/context graph + incident agent).