Their AI investigates their data. Your outage doesn't stay inside it.
Datadog Bits AI, Dynatrace Davis, Splunk and ServiceNow AI features investigate the data their own platform ingests, in their own cloud, and hand an engineer a summary. Onepane investigates across every tool in the estate, including the database, the mainframe and the change ticket, inside your VPC, and delivers the evidence-linked RCA document under SLA.
What are observability-vendor AI features good at?
- 01Data gravity: your telemetry is already in their platform, so there is nothing to connect and no new vendor to review.
- 02Contract convenience: the AI features can be added to an existing renewal without a new procurement cycle.
- 03Genuinely good on their own data: for an incident wholly inside one vendor's instrumented estate, their investigation features are fast and getting better.
We do not compete on demo polish, and we would not win if we did. We compete on scope of estate, deployment model and the deliverable.
Why doesn't single-vendor AI reach root cause in a hybrid estate?
Single-vendor blindness
Their AI investigates their data. Ask what it does when the cause is in the Oracle database, the mainframe, the change ticket or the acquired business unit still on a different monitoring stack. Most enterprise Sev1s cross at least one of those boundaries.
SaaS-only, so the data must leave
The AI runs where the vendor runs, and for most enterprises that is the vendor's cloud. Where telemetry is already there this is moot; where it is not, the mainframe, the on-prem database, you cannot bring the investigation to the data.
Incentives point the wrong way
An observability vendor's revenue grows with hosts monitored and gigabytes ingested. They cannot sell you an outcome that reduces your consumption. Their incentives and yours diverge at exactly the moment the product starts working.
The two questions to ask your observability vendor: what does your AI do when the cause is in the Oracle database, the mainframe or a change ticket? And will you run it inside our VPC?
How does observability-vendor AI compare with a managed root-cause service?
| Dimension | Observability-vendor AI (Bits AI, Davis, Splunk, ServiceNow) | Onepane, managed RCA |
|---|---|---|
| Scope of investigation | The vendor's own telemetry: hosts, services and logs instrumented with that vendor. | Everything the causal chain touches: Datadog and Splunk and CloudWatch, the Oracle database, the mainframe, the change ticket, the cloud control plane. |
| Where it runs | The vendor's SaaS cloud. Your data is already there; the AI runs beside it. | Your VPC. Read-only connectors into the tools you own; nothing shipped out. |
| Change attribution | Change events surfaced where the vendor ingests them; correlation with deploy markers. | Deployment, config, IaC and cloud-provider change linked through topology to the symptom, with diff and approver. |
| Service ownership | Service catalog you populate and maintain inside the vendor's product. | Service-and-ownership map built from what is running; snapshot at incident time; you keep it. |
| Deliverable | An investigation summary, suggested cause and hypotheses for an engineer, inside the vendor's UI or chat. | Root Cause Report, Executive Summary, Customer-Facing RCA, Evidence Pack, Problem Record, inside an SLA, with engineers signing off. |
| When the cause is outside the vendor's data | The investigation stops at the boundary of what the vendor can see. | The investigation follows the chain wherever it goes. |
| Commercial incentive | Revenue scales with hosts and gigabytes ingested; the AI is an add-on to renewal. | Priced per service under coverage and accepted RCA; no incentive tied to your ingestion volume. |
| Accountability | A software feature; the outcome is yours. | A managed service with an SLA and credits; the outcome is ours. |
Vendor capabilities as described in their public documentation, August 2026; verify current features and deployment options with the vendor. See also the deployment-model comparison.
Keep the observability. Add the root cause.
Your observability is the input to our investigation. We connect to Datadog, Dynatrace, Splunk, New Relic, CloudWatch and Prometheus read-only, alongside the databases, cloud control planes, CI/CD, IaC and ITSM that the observability vendors do not ingest, and we do it inside your VPC.
The output is not another dashboard. It is the Root Cause Report with an evidence index, the Customer-Facing RCA, the Evidence Pack and the Problem Record in your ITSM, inside an SLA, signed off by our engineers.
And because we price per service under coverage and per accepted RCA, nothing in our model rewards you ingesting more.
Onepane vs observability-vendor AI, the questions.
Does Datadog do root cause analysis automatically?
Datadog offers AI-assisted investigation features that surface likely causes, related changes and anomalies across data Datadog ingests, and can produce an investigation summary for an engineer. It does not produce an evidence-linked RCA document across systems outside Datadog, and it runs in Datadog's cloud. As of Datadog's public documentation; verify current capabilities with the vendor.
What are the limitations of Datadog Bits AI for RCA in a hybrid estate?
Three structural ones: it investigates Datadog data, so anything on Splunk, the Oracle database, the mainframe or a change ticket outside Datadog is invisible to it; it runs SaaS, so it cannot be brought inside your VPC to data that never leaves; and its deliverable is an investigation summary for an engineer rather than a document with an SLA. It is a strong feature for Datadog-instrumented incidents.
Does Dynatrace Davis find root cause?
Dynatrace Davis is a causal AI engine that identifies probable root cause within Dynatrace-monitored topology, and it is well regarded for that. The same boundary applies: it reasons over what Dynatrace observes, and it delivers to an engineer inside Dynatrace. Verify with the vendor for current deployment options.
Should we replace Datadog or Dynatrace with Onepane?
No. Keep your observability; it is the input. Onepane runs on top of Datadog, Dynatrace, Splunk, CloudWatch and the rest, read-only, inside your VPC, and follows the causal chain across all of them. We compete on scope of estate, deployment model and the deliverable, not on replacing anything.
Why not just use the AI features already in our observability contract?
If every Sev1 you have starts and ends inside one vendor's instrumented estate and nobody outside engineering is waiting on the RCA document, you probably should. If your incidents cross tools, involve change tickets or databases the vendor cannot see, or must be documented for a customer or a review board, the vendor's AI helps the engineer but does not produce the deliverable. Run the replay to find out which you are.
See what the vendor's AI missed.On your own last 90 days.
Send us your Sev1 tickets. We show which incidents crossed a tool boundary, what we would have found, and the document you would have received. Two weeks, no cost.