7 Best On-Prem AIOps Tools in 2026 (Self-Hosted and Air-Gapped)

AIOps tools use machine learning and, increasingly, LLMs to do the work an on-call engineer would otherwise do by hand: detect anomalies, correlate alerts into incidents, cut noise, find a probable root cause, and sometimes trigger remediation. Most of the well-known ones are SaaS. If you run in a regulated industry, an air-gapped network, or anywhere data residency is a hard requirement, SaaS is a non-starter and you need to keep all of it inside your own boundary.

The catch is that "on-prem AIOps" has two halves, and vendors often only deliver one. Self-hosting the data plane (your logs, metrics, and traces) is common. Keeping the AI inference local is not. Several tools here let you store telemetry on-prem but still send prompts to a vendor-hosted model, which defeats the point if your reason for going on-prem was the data itself. So the most useful filter is: where does the telemetry live, and where does the AI actually run.

This guide compares seven on-prem and self-hostable AIOps tools across AIOps depth, deployment model, where inference runs, pricing, and operational burden. For the broader category, see our best AIOps tools roundup, or the BYOC observability tools comparison if data residency is your main driver.

What to look for in an on-prem AIOps tool

• True air-gap support: the data plane and the control plane both run inside your network, with no mandatory phone-home.
• Where AI inference runs: whether prompts and telemetry can hit a model in your own account (Bedrock, Vertex, Azure OpenAI, or a self-hosted model) instead of a vendor's cloud.
• Real AIOps depth: event correlation, noise reduction, root cause analysis, and remediation, not just dashboards with a chat box bolted on.
• Operational burden: how much infrastructure you have to run and tune yourself to keep it alive.
• Predictable pricing: a model you can estimate up front rather than one that drifts with ingest volume or opaque consumption units.

Want the full feature matrix? Jump to the comparison table.

Quick Picks

How we compared these tools

We looked at each tool through the lens of a team that cannot use SaaS:

• Deployment: can it run fully on-prem and air-gapped, not just "self-managed with a license server that phones home"?
• AI locality: can the AI features run against a model you control, or do they require a vendor-hosted LLM?
• AIOps capabilities: anomaly detection, alert correlation and noise reduction, root cause analysis, and automated or assisted remediation.
• Telemetry coverage: metrics, logs, traces, profiling, and events in one workflow.
• Pricing posture: predictable licensing versus consumption that scales with ingest.
• Operational burden: how much you have to stand up and tune to run it.

1. Metoro

Kubernetes-native observability with an AI SRE

Pricing: $20/node/month licensing for BYOC and on-prem. Free Hobby tier.
Setup time: under 5 minutes (one Helm install, no code changes).

Metoro is a Kubernetes-native observability platform with a built-in AI SRE. One Helm install deploys an eBPF collector that captures seven signals (logs, metrics, traces, profiling, Kubernetes events, resource state, and deployment context) with no SDKs, sidecars, or code changes. That complete, kernel-level context is what its AI agents use to detect issues, investigate alerts, verify deployments, and open fix PRs rather than just summarizing what you already see.

For on-prem buyers, the deployment story is the point. Metoro runs fully on-prem, air-gapped or connected, on your own hardware, with the full data plane behind your firewall. It also runs as BYOC inside your own VPC. Critically, AI inference can run against your own model provider (AWS Bedrock, GCP Vertex, Azure OpenAI, or a self-hosted model), so prompts and telemetry never leave your environment. That closes the gap most "on-prem" AIOps tools leave open, where the data is local but the AI still phones home.

The same telemetry powers AI root cause analysis, AI deployment verification, and automatic alert investigation. Teams already on OpenTelemetry can send OTLP traces, logs, and metrics instead of replacing existing instrumentation. Metoro is SOC 2 Type II and a CNCF Silver member.

Strengths:

• 5-minute eBPF setup that captures requests, queries, dependencies, and profiles across pods with no code changes.
• AI SRE workflows: root cause analysis, alert investigation, deployment verification, and review-ready fix PRs from runtime and code context.
• Data and inference both stay in your environment, with inference on your own model provider.
• Kubernetes-native model correlating logs, traces, metrics, profiles, resources, and events automatically.
• Predictable per-node licensing that does not drift with ingest volume.

Don't use if:

• You are not running Kubernetes, since Metoro is purpose-built for it.
• You need a fully open-source stack with no proprietary components.

Deployment options: On-prem (air-gapped supported), BYOC (your VPC, managed by Metoro), and managed SaaS.

2. IBM Cloud Pak for AIOps

Heavyweight enterprise AIOps for OpenShift

Pricing: quote-driven, licensed by VPC and Resource Units. An AWS Marketplace "Small Starter" contract lists $12,120/year for 100 Resource Units.
Setup time: weeks. Requires standing up Red Hat OpenShift first.

IBM Cloud Pak for AIOps (formerly Cloud Pak for Watson AIOps) is the heavyweight enterprise option. Its classic AIOps runs in-cluster: event correlation and deduplication, noise reduction, anomaly detection across logs and metrics, probable-cause analysis over a topology map, and runbook automation with ChatOps integrations. IBM has also added watsonx.ai generative AI for incident summarization, but that piece does not run locally: IBM's docs state the integration requires a watsonx.ai project on IBM Cloud (SaaS) and that on-prem watsonx.ai is not supported.

It runs only on Red Hat OpenShift, but that means the core platform can run fully on-prem, and IBM officially supports air-gapped installation by mirroring images into a private registry. The trade-offs for a strict air-gap: the Microsoft Teams ChatOps integration is not supported, and the cloud-connected watsonx.ai generative summarization is out, so you get the classic ML AIOps but not the LLM features. This is a genuine on-prem AIOps platform, but it is also a major commitment.

Strengths:

• Mature event correlation and noise reduction, with IBM citing large reductions in alert volume.
• Probable-cause analysis grounded in topology, plus runbook automation for remediation.
• Officially supported air-gapped install for regulated environments.
• Deep integration with the IBM ecosystem (Instana, Turbonomic, watsonx).

Don't use if:

• You are not already running OpenShift or want fast time to value, since it is complex to implement with a steep learning curve.
• You are cost-sensitive or a smaller team, since pricing is quote-driven and enterprise-scale.

Deployment options: Self-hosted on OpenShift (on-prem or cloud-hosted), air-gapped supported. A separate AIOps Insights SaaS exists on AWS.

3. Dynatrace Managed

Self-hosted Davis causal AI

Pricing: Managed pricing is quote-only. Only the SaaS rate card is published (full-stack from ~$0.01 per GiB-hour).
Setup time: agents deploy fast, but self-hosted cluster ops are heavy.

Dynatrace Managed is the self-hosted version of Dynatrace, running in your own datacenter. Its Davis AI engine does genuine causal RCA, performing fault-tree analysis across the Smartscape topology to surface a most-probable root cause, plus auto-adaptive anomaly detection. OneAgent auto-instruments hosts and services without code changes.

Air-gapped operation is possible with a Managed offline license, at the cost of fully manual updates and no extensions hub. The bigger caveat for on-prem buyers: Dynatrace's newest features are not on Managed. Grail and the apps built on it are SaaS-only and not on the Managed roadmap, and the generative AI, including Davis CoPilot, is not available for Dynatrace Managed customers at all. So Managed gives you Davis causal AI, but the LLM assistant is SaaS-only, and you accept an effectively feature-frozen build relative to SaaS.

Strengths:

• Davis causal RCA grounded in an auto-discovered topology, not just metric correlation.
• OneAgent auto-instrumentation across many runtimes with no code changes.
• Self-hosted in your own datacenter, with air-gapped operation supported.

Don't use if:

• You want the latest Dynatrace platform capabilities on-prem, since Grail and newer apps are SaaS-only.
• Budget predictability matters, since DDU-based pricing is famously hard to forecast and Managed pricing is quote-only.

Deployment options: Self-hosted (Dynatrace Managed), air-gapped via offline license, or SaaS.

4. Splunk Enterprise + ITSI

Service-level AIOps on data you already self-host

Pricing: ingest, workload, or entity-based, with an expensive reputation. No public per-GB rate.
Setup time: significant. Production Splunk is multi-tier, and ITSI is configuration-heavy.

For teams that already self-host Splunk Enterprise, IT Service Intelligence (ITSI) adds the AIOps layer. It does adaptive thresholding that retrains nightly, predictive analytics via the Machine Learning Toolkit, and event analytics that groups notable events into episodes for noise reduction. Note that the legacy ITSI anomaly detection feature was deprecated in 4.20 in favor of adaptive thresholding.

Splunk Enterprise and ITSI run fully on-prem and air-gapped. The important exception is the generative AI: the Splunk AI Assistant for SPL requires a cloud-connected, Splunk-hosted service and is not available in a true air-gapped setup. The local ML (thresholding, episodes, predictions via MLTK) still runs on-prem, but MLTK is a build-your-own framework that expects SPL and data-science skills. Splunk was acquired by Cisco in March 2024.

Strengths:

• Mature service-level AIOps (KPIs, service trees, episodes) on top of a self-hosted data platform.
• Local ML for thresholding, episode grouping, and prediction runs fully on-prem.
• Backed by one of the most powerful query and search engines in the category.

Don't use if:

• You need generative AI air-gapped, since the AI Assistant is cloud-connected only.
• You want turnkey AIOps, since MLTK is DIY and production Splunk is operationally heavy and costly.

Deployment options: Self-hosted on-prem (air-gapped for core), or Splunk Cloud SaaS.

5. Elastic Observability

Local ML anomaly detection and AIOps Labs

Pricing: self-managed Free/Basic, but ML and AIOps features require a Platinum or Enterprise subscription.
Setup time: moderate, but running Elastic at scale is an operational job.

If you already run the Elastic Stack, its AIOps features are a natural extension. Unsupervised ML anomaly detection runs inside the cluster, and the AIOps Labs features add log rate analysis (the standout, which pinpoints the exact field values driving a spike), log pattern analysis, and change point detection. The Elastic AI Assistant adds RAG-backed chat over your data.

For on-prem use the split is clean and favorable. The Stack runs fully self-managed and air-gapped, and all the statistical ML and AIOps features run locally with no external LLM. Only the AI Assistant needs a model, and Elastic supports pointing it at a self-hosted local LLM for air-gapped deployments. The trade-off is operational: running Elastic at scale means managing heap, shards, and upgrades yourself, with no native autoscaling.

Strengths:

• ML anomaly detection and AIOps Labs run entirely inside your cluster, no external LLM required.
• Genuinely useful log rate analysis for pinpointing the cause of a spike, not just flagging it.
• AI Assistant can run against a self-hosted local model for air-gapped use.

Don't use if:

• You want turnkey AIOps, since ML jobs need real tuning and the cluster needs in-house expertise to run.
• You want the AIOps features for free, since they are gated behind paid tiers.

Deployment options: Self-managed on-prem (air-gapped supported), Elastic Cloud, or serverless.

6. Coroot

Open-source eBPF observability with AI RCA

Pricing: Community edition free. Paid edition from $1 per monitored CPU core/month.
Setup time: minutes to deploy, but you operate the backends.

Coroot is an open-source (Apache 2.0) observability platform with eBPF-based collection of metrics, logs, traces, and continuous profiling, plus a service map, SLO alerting, deployment tracking, and cloud cost monitoring. It is fully self-hostable, which makes it an appealing open-source on-prem option. Its AI-powered root cause analysis and agentic investigation are the AIOps piece, though those sit in the paid Enterprise edition rather than the free Community build.

The thing to weigh is operational burden. Coroot stands on Prometheus and ClickHouse, which you run and scale yourself. High-cardinality eBPF telemetry can stress those backends at scale, so this suits teams comfortable operating that stack.

Strengths:

• Fully open-source and self-hostable, with zero-instrumentation eBPF collection.
• Full-stack telemetry plus service maps, SLOs, and cost monitoring in one tool.
• Clear paid upgrade path to AI RCA and agentic investigation.

Don't use if:

• You want AI RCA without paying, since it is an Enterprise feature.
• You want minimal ops, since you manage both Prometheus and ClickHouse, which get harder at scale.

Deployment options: Self-hosted Community (free) or Enterprise, plus a hosted Coroot Cloud.

7. HolmesGPT

Open-source agentic AI investigator

Pricing: HolmesGPT is free OSS. The Robusta platform around it starts at $50/user/month, with self-hosted on the Enterprise plan.
Setup time: minutes for the CLI, more to wire up data sources.

HolmesGPT, by Robusta, is an open-source (Apache 2.0) agentic AI investigator and a CNCF Sandbox project. Rather than being a full observability platform, it plugs into what you already run (Prometheus, Kubernetes, Grafana, cloud providers, databases) and investigates alerts to produce a root cause. It is the most lightweight way to add AI investigation to an existing on-prem stack.

For air-gapped use, the key detail is the LLM. HolmesGPT is bring-your-own-model via LiteLLM and supports Ollama for local models, so it can run without an external API key. The docs label Ollama support experimental and recommend validating quality against a hosted frontier model first, so the offline path works but with a quality caveat. The free OSS agent is CLI and API only; the web UI, chatbots, and triage automation live in the Robusta platform.

Strengths:

• Open-source, lightweight, and connects to existing observability rather than replacing it.
• Bring-your-own-model, including local Ollama models for air-gapped investigation.
• Strong fit for adding AI RCA on top of a Prometheus and Kubernetes stack.

Don't use if:

• You want a full observability platform, since HolmesGPT is an investigator, not a data store.
• You need production-grade air-gapped quality today, since local-model support is still experimental.

Deployment options: Self-hosted OSS agent, or the Robusta platform (self-hosted on Enterprise, or SaaS).

Comparison of on-prem AIOps tools

Pricing note: Pricing and packaging change often, especially for consumption units and AI features. These snapshots were checked against public vendor pages on June 18, 2026. Verify the current vendor page before buying.

Conclusion

Start from why you went on-prem. If it was data residency, the question that actually matters is where the AI runs, not just where the data sits. Splunk's and Dynatrace's generative assistants lean on vendor-hosted models, so a strict air-gap limits you to their local ML. Elastic and HolmesGPT can point at a self-hosted model, and the open-source options (Coroot, HolmesGPT) keep everything in your hands at the cost of running it yourself.

If you are on Kubernetes and want both the telemetry and the AI inference to stay inside your environment, Metoro is the most direct fit: eBPF observability and an AI SRE that detects, investigates, and drafts fixes, deployable on-prem or air-gapped, with inference on your own model provider. You can try it yourself.

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