6 Best Hybrid Cloud Monitoring Tools in 2026 (Compared)

Hybrid cloud monitoring means watching workloads that are split across your own data center and one or more public clouds, from a single place. The hard part is rarely the dashboards. It is that one half of your estate may be subject to data-residency rules while the other half lives in AWS, GCP, or Azure, and most tools were designed for one of those worlds, not both.

So the evaluation looks different from a pure-SaaS comparison. You care about whether the same agent works the same way on a bare-metal VM and a cloud Kubernetes node, whether the backend can run inside your boundary when it has to, and whether the AI features survive when the data cannot leave. Here are six tools worth shortlisting, compared on collection, deployment flexibility, where the AI runs, and pricing.

What to look for in a hybrid cloud monitoring tool

• One pane across environments. Can you correlate on-prem hosts with cloud workloads in the same service map, traces, and queries, or are you stitching separate consoles together in your head?
• Deployment flexibility. Hybrid shops usually have at least some data that cannot leave the building. The tool should run as SaaS and self-hosted, ideally with feature parity, including air-gapped. If on-prem is a second-class citizen with fewer features, it fails the hybrid test.
• Collection that works identically everywhere. One agent and one data model that deploy the same on a data-center VM and a cloud node. Open standards like OpenTelemetry help you avoid a different proprietary agent per environment.
• Where the AI runs. Many platforms route their best AI features through a vendor cloud. If part of your estate is regulated, check whether root cause analysis still works when the data stays put.
• A cost model that does not explode. Per-GB ingest and per-host bills behave very differently once you add high-volume on-prem fleets. Model pricing against your noisiest environment, not the demo.

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

Quick Picks

How We Compared These Tools

Every tool here can monitor workloads in both a data center and public cloud. We compared them on what actually matters when your estate is split:

• Collection model: agent, eBPF, or OpenTelemetry, and whether it behaves the same on-prem and in cloud.
• Deployment options: SaaS, BYOC, self-hosted, and whether air-gapped is genuinely supported.
• Where the AI runs: in the vendor cloud only, or also inside your boundary.
• Pricing posture: predictable per-node, or scaling with ingested hosts, GBs, and users.

Pricing and feature details were verified on the publication date and can change.

1. Metoro

End-to-end Kubernetes observability with an AI SRE, deployable in your own cloud or on-prem

Pricing: $20/node/month for SaaS and BYOC, $30/node/month for on-prem.
Setup time: SaaS around 5 minutes (one Helm install); BYOC and on-prem around 20 minutes.

Metoro is a Kubernetes-native observability platform that gives you end-to-end coverage out of the box. One install brings up metrics, logs, distributed traces, continuous profiling, Kubernetes events, service maps, dashboards, and default alerts, on a cluster in your data center or any cloud. You do not assemble a stack or instrument services one at a time. The golden-signal dashboards, request-level traces, and the service map populate themselves within minutes of the Helm install, which keeps onboarding short even across a split estate. It is also OpenTelemetry-compatible, so you can send OTLP metrics and traces alongside the built-in telemetry. Under the hood, eBPF handles the zero-code instrumentation across your services, third-party containers, and runtime dependencies, so there are no SDKs, code changes, or restarts.

For a hybrid estate, the deployment model is what seals it. Metoro BYOC runs the entire data plane, ingest, storage, and query, inside your own AWS, GCP, or Azure account, while Metoro manages upgrades and on-call from a separate control plane that never sees your telemetry. It also runs fully self-hosted and air-gapped on-prem, or as managed SaaS. So you can keep the regulated half of your estate inside your boundary and still run a managed experience on the rest, with the same dashboards, traces, and alerts everywhere.

The same data powers AI root cause analysis, deployment verification, and alert investigation. It detects issues from live traffic, investigates across code and infrastructure, and can open review-ready fix PRs. Unlike most platforms here, the AI features can run against models you control, so the AI keeps working even when the telemetry cannot leave the network. Pricing is $20 per node per month for SaaS and BYOC and $30 per node for on-prem, with 28-day default retention, which stays predictable as volume grows.

Tool complexity: Low

Differentiator(s):

• End-to-end coverage out of the box: metrics, logs, traces, profiling, dashboards, and alerts from one install, no per-service instrumentation.
• Fast onboarding: one Helm install brings up tracing, dashboards, and default alerts in minutes, the same way on-prem and in cloud.
• BYOC runs the whole data plane in your own AWS, GCP, or Azure account; the control plane never sees your telemetry.
• Fully air-gapped option, including the AI, which can run on your own models.
• eBPF auto-instrumentation populates traces and the service map with no code changes.
• Predictable per-node pricing that does not drift with ingested volume.

Don't use if:

• You are not running Kubernetes (Metoro is purpose-built for K8s).
• You need to monitor a large fleet of non-containerized VMs as the primary use case.

Deployment options: SaaS, BYOC (your VPC, managed by Metoro), self-hosted, and fully air-gapped on-prem. Runs on EKS, GKE, AKS, OpenShift, and Rancher.

2. Datadog

The broad SaaS platform, if you can keep data in the cloud

Pricing: Infrastructure from $15/host/month (annual); Logs $0.10/ingested GB plus indexing.
Setup time: Minutes per host; cloud integrations are agentless.

Datadog is the broadest SaaS observability platform, and its hybrid coverage is genuinely good on the collection side. The Datadog Agent runs the same way on bare metal, VMs, containers, and Kubernetes, on-prem or in any cloud, and it now ships with a Datadog distribution of the OpenTelemetry Collector. For managed cloud services, agentless integrations pull from AWS CloudWatch, Azure, and GCP, so you get one view across AWS, Azure, GCP, and private cloud.

The catch for hybrid is that there is no on-prem backend. The Agent always sends data to Datadog's cloud, and the closest thing to self-hosting, CloudPrem, covers logs only and still runs the control plane and UI in SaaS. Its AI features, Watchdog and Bits AI, are SaaS-only too. If any part of your estate has a "telemetry cannot leave the network" rule, Datadog is out for that part before you start.

Tool complexity: Medium

Differentiator(s):

• Same Agent across on-prem and every major cloud, plus agentless cloud integrations.
• Very broad product surface: infra, APM, logs, RUM, security, in one platform.
• Strong out-of-the-box dashboards and integrations.

Don't use if:

• You have data-residency or air-gap requirements (no real on-prem backend).
• You are cost-sensitive at high log or host volume (per-GB and per-host bills add up).

Deployment options: SaaS only. CloudPrem keeps log storage in your infra but the control plane stays in Datadog's cloud.

3. SolarWinds

Traditional IT and network monitoring with a self-hosted option

Pricing: Self-hosted from ~$5/node/month (Essentials), ~$9 (Advanced), subscription with a 3-year commit; SaaS is usage-based (logs ~$5/GB/month).
Setup time: Hours to days (Orion platform install plus polling setup).

SolarWinds is the long-standing IT operations vendor, and it is the one tool here with deep roots in traditional network and infrastructure monitoring rather than cloud-native observability. It ships two distinct products. SolarWinds Observability Self-Hosted (formerly Hybrid Cloud Observability) runs the Orion-based platform inside your own infrastructure behind your firewall, and SolarWinds Observability SaaS is the cloud-hosted, OpenTelemetry-native product. They are separate products, not one platform in two modes, which is the first thing to get straight.

For hybrid estates, the self-hosted product is the draw: on-prem control with multi-cloud visibility for AWS, Azure, and GCP, and it is strong on what SolarWinds has always done well, SNMP and WMI polling of network devices, servers, and VMs. Kubernetes monitoring lives in the SaaS product, via a Helm-deployed collector that scrapes Prometheus metrics, events, and logs, with no eBPF.

Two caveats for a modern hybrid buyer. First, even on the self-hosted product, the AI-driven anomaly alerting runs in SolarWinds' cloud AIOps service and needs outbound connectivity, so it is not an air-gapped fit. Second, deep Kubernetes and APM coverage sits in the separate SaaS product, so a team that wants both rich network monitoring and cloud-native depth may end up running both. Pricing recently moved to subscription-only with a 3-year commitment.

Tool complexity: Medium

Differentiator(s):

• A genuinely self-hosted, on-prem product (Orion-based) for teams that need the control plane in-house.
• Best-in-class traditional network and infrastructure monitoring via SNMP and WMI.
• Multi-cloud visibility across AWS, Azure, and GCP.

Don't use if:

• You need air-gapped AI (anomaly detection phones home to SolarWinds' cloud).
• Kubernetes and APM are your priority (those live in the separate SaaS product, with no eBPF).

Deployment options: SolarWinds Observability Self-Hosted (on-prem, Orion platform) or SolarWinds Observability SaaS (cloud).

4. New Relic

Turnkey SaaS coverage with usage-based pricing

Pricing: 100 GB/month ingest free, then $0.40/GB; users from $49/month, Full Platform Pro $349/user/month (annual).
Setup time: Minutes per host; cloud integrations are agentless.

New Relic is a clean fit if SaaS is acceptable across your whole estate. Its infrastructure agent covers on-prem and virtual hosts, it ingests OpenTelemetry and Prometheus data, and agentless integrations connect AWS, Azure, and GCP accounts into one platform. New Relic AI sits on top as an LLM-based assistant that queries your telemetry and explains issues.

The hybrid limitation is the same as Datadog's: there is no self-hosted backend, so all on-prem telemetry has to leave your premises for New Relic's cloud, which rules it out for air-gapped or strictly regulated workloads. Pricing is usage-based, with a generous 100 GB free tier and then $0.40 per GB plus per-user fees, so cost depends heavily on how chatty your fleet is and how many full-platform users you need.

Tool complexity: Low to Medium

Differentiator(s):

• Turnkey unified view across on-prem and the three major clouds.
• Strong OpenTelemetry and Prometheus ingest.
• Generous free ingest tier and a single consumption model.

Don't use if:

• You need an on-prem or air-gapped backend (it is SaaS only).
• You have many full-platform users or very high ingest (costs climb).

Deployment options: SaaS only (US or EU region).

5. Grafana

The composable open stack you can self-host

Pricing: OSS free; Grafana Cloud Pro $19/month plus usage; Enterprise self-managed from $25,000/year.
Setup time: Days for a basic LGTM stack, longer to productionize.

Grafana is the most flexible option on deployment because the same LGTM components, Loki, Mimir, Tempo, and Grafana, run whether you self-host on-prem or use Grafana Cloud. Grafana Alloy is its OpenTelemetry collector with service discovery for AWS, Azure, and GCP, and the Cloud Provider Observability app brings all three into one view. For hybrid teams that want telemetry to stay on-prem, self-hosting LGTM is a real answer.

Two things to weigh. First, you operate the stack yourself, which is a meaningful platform-engineering commitment. Second, the intelligence layer is tethered to Cloud. Grafana Assistant runs its UI in self-managed Grafana but keeps the backend in Grafana Cloud, and Sift is Cloud-only. The self-managed Enterprise line (GEM, GEL, GET) is also in maintenance, with no new features and end-of-life on February 1, 2029. So a fully on-prem Grafana gives you the data but not the newest AI.

Tool complexity: High

Differentiator(s):

• The same open stack self-hosts on-prem or runs as Grafana Cloud.
• Open standards throughout (Prometheus, OTel, Loki, Tempo), so little lock-in.
• Multi-cloud collection via Alloy and the Cloud Provider app.

Don't use if:

• You want a turnkey tool rather than a stack to operate.
• You need first-party AI in a fully air-gapped deployment (Assistant needs a Cloud backend).

Deployment options: Self-hosted OSS, Grafana Enterprise (self-managed, in maintenance), or Grafana Cloud.

6. Elastic Observability

Flexible, air-gappable deployment with heavy log search

Pricing: Resource-based (Hosted) or usage-based (Serverless); enterprise tiers sales-quoted.
Setup time: Hours for a managed cluster; longer to operate at scale.

Elastic is a strong fit when you need deployment flexibility and log search is central. The Elastic Agent is a single agent for logs, metrics, traces, and security data, now bundling an OpenTelemetry collector, and Elastic runs as Elastic Cloud SaaS or fully self-managed via ECK and ECE. Critically for hybrid, air-gapped deployments are fully supported with the same capabilities as connected ones, and there are agentless integrations for AWS, Azure, and GCP.

Elastic also handles the AI question better than most for on-prem: the AI Assistant can connect to a self-hosted LLM such as vLLM with no outbound access, and its ML anomaly detection runs locally. The trade-offs are honest ones. Collection leans on OpenTelemetry and agents rather than eBPF auto-discovery, and operating Elasticsearch at scale is a real commitment in storage and tuning. Pricing is resource- or usage-based and sales-quoted at the enterprise tier.

Tool complexity: Medium to High

Differentiator(s):

• Self-managed, cloud, and genuinely air-gapped deployment options.
• AI Assistant can run against a self-hosted LLM, so AI works on-prem.
• Best-in-class log search and 450+ integrations.

Don't use if:

• You want eBPF auto-instrumentation rather than agent and OTel collection.
• You do not want to operate Elasticsearch storage and scaling yourself.

Deployment options: Elastic Cloud (SaaS), self-managed (ECK, ECE), and air-gapped on-prem.

Comparison of Hybrid Cloud Monitoring Tools

Conclusion

Start from the constraint that made you say "hybrid" in the first place. If the only reason is that workloads are spread across clouds and everything can still talk to a SaaS, Datadog and New Relic give you the broadest turnkey coverage. The moment any part of your estate cannot send telemetry off-site, the field narrows fast, and you should check where each tool's AI actually runs, because several route their best features through a vendor cloud.

For Kubernetes teams that need both halves, Metoro is the cleanest starting point: eBPF gives you the same zero-code collection everywhere, BYOC keeps the data plane inside your own cloud account, the air-gapped option keeps it fully on-prem, and the AI SRE keeps working on models you control. If you want an open stack you operate, Grafana or Elastic are the better fit, with Elastic the stronger choice when AI has to run on-prem. You can try Metoro yourself.

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