Buying AIOps Platform

Decision criteria.

The question

AIOps purchase decisions are the discipline of separating real alert-volume pain from vendor pitch. Default to the existing-vendor add-on first; switch to a dedicated platform only with a documented case.

• What AIOps promises. Alert reduction, automated triage, and root-cause analysis per platform. The space spans Moogsoft, BigPanda, Splunk ITSI, Datadog Watchdog, and Nova AI Ops.
• Default to existing vendor. Observability-vendor AIOps add-on first per org. Switching observability backbones for AIOps alone rarely pays back the migration cost.
• Switch threshold. Roughly ten thousand alerts per day per org. Below that, the add-on usually clears the pain; above it, dedicated platforms start to earn their keep.
• Named pain. Documented current-state pain per org before evaluation begins. Catches solution-shopping without a problem; the vendor pitch is more compelling than the actual gap.

What to evaluate

Evaluation is its own discipline. Clustering quality, root-cause surfacing, and integration breadth together cover what the demo cannot show.

• Alert clustering. Related-signal grouping quality per platform. Run on thirty days of your historical alerts; measure manual labour saved against the actual triage workload.
• Root-cause hypothesis. Plausible-cause surfacing per platform. Beware demos with hand-tuned data; insist on running against your real telemetry.
• Integration. Existing alert-source connectivity per platform. Migration is the killer cost; missing connectors become weeks of custom integration work.
• Customisation surface. Rule and model customisation depth per platform. Supports the specific workload rather than locking the team into the vendor's defaults.

How to trial

The trial is where vendor pitches meet reality. Shadow live alerts, test real-incident scenarios, and talk to references at the team's actual scale rather than the marquee logo.

• Thirty-day shadow trial. Live-alert pipe with no action per platform. Measure precision and recall against your post-incident retros over the trial window.
• Real-incident test. Staging multi-signal outage per platform. Run a controlled failure and see if the platform clusters correctly without the vendor's coaching.
• Reference customers. Three same-scale references per platform. Vendor demos cherry-pick; references at the team's actual scale reveal the real ops burden.
• Documented success criteria. Named pass/fail bar per trial. Catches sunk-cost extension when the trial drags past the original deadline without a clear answer.

Hidden costs

The hidden costs are real. Ingestion fees, configuration time, and vendor lock-in each show up after the contract signs and rarely appear in the original ROI model.

• Data ingestion fees. Per-event billing per platform. Alert storms can blow budgets in hours rather than months; cap or alert on ingestion volume.
• Configuration time. Four to eight week SRE tuning cost per platform. Real and often understated; budget the time honestly rather than as nights-and-weekends.
• Vendor lock-in. Non-portable rules and models per platform. Custom rules and learned models do not port between vendors; the migration cost compounds with the platform's depth.
• Named ongoing owner. SRE owner per platform. Supports operational reviews and prevents the AIOps program from drifting into "everyone's job and therefore no one's".

When to buy

The decision is volume-driven. Be specific about the alert-rate threshold; below it, the dedicated platform is over-engineering, above it, the add-on is under-powered.

• Under one thousand alerts per day. Skip dedicated AIOps per org. PagerDuty event rules and basic deduplication clear the pain at this scale.
• One thousand to ten thousand per day. Existing-vendor add-on per org. Evaluate first; the add-on is usually enough and avoids the migration cost.
• Above ten thousand per day. Dedicated AIOps platform per org. Pays for itself in alert reduction within six months when the volume is genuinely there.
• Volume-trend chart. Alert-volume trajectory per quarter. Catches threshold-crossing early so the buying cycle starts before the on-call rotation breaks.