How to choose AIOps tools, and when to look past them

AIOps tools apply machine learning to IT operations data, cutting alert noise and surfacing the signals worth acting on. That part is useful, and it's also where the clarity ends. The category covers very different products, and vendors describe them in nearly the same language, so it's hard to tell one tool from another.

The split that matters most runs between tools that detect and correlate, and tools that investigate. The first kind groups related alerts and points at a probable cause, which is what AIOps was built to do. The second kind, figuring out what broke and why, is a separate, newer category, and it's where most of an incident's time goes.

AIOps platforms vs. AIOps tools

“AIOps tools” is a catch-all term that spans two fairly different things:

• A point tool does one job: an event correlation engine that collapses alert storms, a standalone anomaly detection service, or a log management product with machine learning added.
• An AIOps platform bundles those jobs into a single system that handles data ingestion, correlates events, runs predictive analytics, and supports automated incident response from a single place.

The trade-off is the usual one. A point tool is quicker to adopt and sharper at its single job. Stacking several of them, though, recreates the fragmentation AIOps was meant to fix, with each tool seeing only its own slice and an alert-routing tool like PagerDuty stitched on top. A platform bundles these cross-slice correlations, at the cost of more setup and a greater commitment.

The three categories of AIOps platforms

Gartner, in their original “Market Guide for AIOps Platforms” report, gave the market the vocabulary most buyers still use. AIOps platforms split into two approaches, domain-agnostic and domain-centric, and teams usually add a do-it-yourself third option. The distinction comes down to how much of your stack a tool can reason over, and how much integration work you take on to get there.

Domain-centric tools add AIOps capabilities to a platform that already owns one slice of your stack. Datadog, Dynatrace, New Relic, Splunk ITSI, and LogicMonitor all fold machine learning into the metrics and traces their own observability, application performance monitoring (APM), and infrastructure monitoring tooling already collect. They're easy to switch on if you already run the platform, and they're strong when the problem you're chasing lives inside that one domain. They see less outside it.

Domain-agnostic tools sit above your monitoring stack and ingest pre-processed alerts and events from many tools at once. BigPanda and Moogsoft built their platforms around this kind of cross-tool correlation, pulling IT infrastructure, application, and network signals into a single incident and serving DevOps, SRE, and security teams from one place. The cost is more integration and tuning to keep clean data flowing in.

The do-it-yourself path means assembling your own big data pipeline from open-source components or general machine learning models wired into your tools. It buys you maximum control and carries the highest ongoing cost, since your team owns the data engineering, the models, and the maintenance for as long as you run it.

Category	Best when	Example tools
Domain-centric	You mostly need AIOps inside one platform you already run	Datadog Watchdog, Dynatrace, New Relic, Splunk ITSI, LogicMonitor
Domain-agnostic	You need to correlate across many monitoring tools at once	BigPanda, Moogsoft
Incident response with AIOps features	Your pain is alert routing and on-call more than analysis	PagerDuty
Do-it-yourself	You have the engineers and want full control	Internal builds on Prometheus, the ELK stack, or LLM agents

The lines blur in practice. Many observability vendors now ingest third-party data, and several correlation platforms ship their own collectors. Treat the categories as a way to frame your shortlist rather than firm walls, and weigh each tool by how much of your environment it can actually see.

Most teams don't pick once and stop. A common path starts with the domain-centric AIOps features in a platform they already pay for, then adds a domain-agnostic layer once the number of monitoring tools makes cross-tool correlation the real bottleneck. Knowing which stage you're at saves you from buying for a scale problem you don't have yet.

Why Gartner is moving on

Gartner has walked away from that framing. In 2025, it retired the AIOps Platforms market, renamed it Event Intelligence Solutions, and folded application performance monitoring into a broader Observability Platforms category.

Gartner's reasoning is blunt. By Gartner’s own account, vendors had attached “AIOps” to products across many IT operations markets without agreeing on what it covered. That haziness, on top of the AI hype, left infrastructure and operations leaders unsure what they were buying and let down by what they got.

What's left is a capability set that has split into two. The detection and correlation features got absorbed into observability platforms, which now ship them as built-in functions. The harder part, investigating an incident to find what broke, has become its own category built on agentic AI. "AIOps tool" increasingly names the commoditized half of that split.

For a buyer, the upshot is that shopping for “an AIOps tool” as a standalone product makes a little less sense each year. The noise reduction and correlation come bundled into the observability platform you probably already run, and the investigation work belongs to the newer, agent-driven category.

What to check when every tool claims the same thing

Vendor pages converge on the same promises: less noise, faster root cause analysis, smarter automation. The real differences live a layer down, and they matter most to the on-call, DevOps, and SRE folks who'll actually live with the tool. A team buried in alert fatigue has a different problem than one that triages fast but then loses an hour finding the cause, and a few questions tend to separate the tools that help.

• How much of your stack can it see? Integration depth matters more than the length of the integrations page. A tool that reads code changes, deploys from your CI/CD pipelines, and IT infrastructure config will reason about root cause analysis far better than one limited to metrics and logs. Many platforms only reason over their own data, which is fine until an incident crosses domains.
• Does it correlate, or does it investigate? Most AIOps tools stop at a grouped, ranked alert with a probable cause attached. Surfacing a correlation and running the actual investigation are different jobs, and the second is usually where the hour goes. Be clear which one you're buying.
• How does it handle a failure it has never seen? Pattern-based tools do well with recurring incidents and poorly with novel ones, since rule-based automation only covers scenarios someone mapped in advance. Test it on a real, messy incident from your own environment instead of a canned demo.
• What does it cost to keep working? Budget for tuning, machine learning models that drift, and the engineering time to maintain integrations and feed an ITSM, on top of the license. Correlation rules that worked last quarter start grouping the wrong things as the environment changes.
• How does it treat production access? Anything that analyzes your systems needs broad read access, so review data handling, access controls, and audit logging as part of the selection process.

Where AIOps tools stop, and what picks up after

AIOps tools run statistical analyses on streams of metrics, logs, and events. They flag a metric that's out of band or fold 400-related alerts into a single incident. Detection and correlation are the job, and the strong tools do it well.

What they don't do is investigate. Once the correlated alert lands, a person still forms a hypothesis, queries the systems, reads the code, traces the dependency, and decides what actually broke. None of that is a shortcoming in Moogsoft, BigPanda, Datadog Watchdog, or Splunk ITSI. Correlation has a ceiling by design, and for years, it was the best anyone could do, until agentic reasoning arrived.

Closing that gap takes a different kind of tool, one built on agentic AI rather than statistical correlation. These systems place AI agents directly on the investigation, forming and testing hypotheses across code, infrastructure, telemetry, and past incidents until they reach a likely cause with supporting evidence. This is a separate category from AIOps, usually filed under AI for production systems, even though buyers often weigh the two in the same budget conversation.

The difference shows up where teams have adopted it. On DoorDash's ad platform, where real-time auctions clear in under 100 milliseconds and every minute of downtime costs revenue, Resolve AI cut time to root cause by up to 87 percent. Salesforce reported a roughly 60 percent drop in MTTR (mean time to resolution) and about 70 percent faster alert triage, closing some investigations in as little as 10 minutes.

Matching the tool to how your incidents actually unfold

The right choice comes down to where you spend your time. If you mainly need to cut alert volume, an AIOps tool, or the AIOps features in the observability platform you already run, will correlate and dedupe the noise, and that helps a team buried in pages.

Resolve AI goes further on the same problem. It handles the pages first, triaging and investigating each alert on its own, so on-call shrinks because each page arrives with the investigation already done and a likely cause attached. It connects across the code, infrastructure, and observability tools your team already runs, reaching probable root cause in minutes for teams like Coinbase and Zscaler.

So the two aren't a strict either-or. An AIOps tool reduces the alert pile; Resolve handles the pages and carries each incident to its cause. Whether on-call is buried or investigations are eating the week, it's worth watching Resolve work through one of your real incidents. See it in action.