6 Best Datadog On-Prem Alternatives in 2026

Datadog is a SaaS observability platform. There is no edition you install and run inside your own data center: metrics, traces, APM, RUM, and dashboards all run in Datadog's cloud. The closest thing is BYOC Logs (formerly CloudPrem), which stores logs in your own object storage but is logs-only, still routes through the Datadog SaaS control plane.

So if a requirements doc says "no third-party SaaS" or "telemetry cannot leave the network," Datadog is out before the evaluation starts. Teams hit this in defense, finance, healthcare, and government, anywhere data-sovereignty or air-gap rules apply. The good news is that several platforms are built to run entirely inside your boundary. Here are six, compared on how they collect data, where the AI runs, and what you have to operate yourself.

What to look for in a Datadog on-prem alternative

On-prem reviews ask questions that SaaS comparisons never do. The ones that decide the shortlist:

• Full data plane offline. Does ingest, storage, query, and the UI run with zero internet dependency, or just part of it?
• Bring your own AI models. Most regulated buyers cannot send prompts and telemetry to a vendor cloud or an external LLM API. Can the AI run against a model you control, including a self-hosted one?
• Operational footprint at scale. How many moving parts do you have to stand up and keep healthy (object storage, ClickHouse, Elasticsearch, collectors), and how do they behave under high cardinality and volume? On-prem means your team operates all of it.
• Controls and identity. Does it inherit your IdP, KMS, and RBAC, and who answers the phone at 3am?

Quick Picks

• Best unified observability platform for Kubernetes teams: Metoro
• Best open-source eBPF option: Coroot
• Best composable open stack to own: Grafana (self-hosted LGTM)
• Best for heavy log search on-prem: Elastic
• Best open-source OTel-native single app: SigNoz
• Best for existing Dynatrace shops: Dynatrace Managed

How We Compared These Tools

Every tool here can run inside your own infrastructure. We compared them on the criteria above: whether the entire data plane (including the AI) runs air-gapped, how telemetry is collected, what you operate yourself, and how pricing works. Pricing and feature details were verified on the publication date and can change.

1. Metoro

Air-gapped observability with an AI SRE for Kubernetes teams

Pricing: $20/node/month for BYOC, $30/node/month for fully air-gapped on-prem. No ingest or egress fees.
Setup time: BYOC under 5 minutes (one Helm install); full air-gapped on-prem around 20 minutes.

Metoro is a Kubernetes-native observability platform that combines full-stack telemetry (metrics, logs, traces, profiling, Kubernetes events, resources, and service maps) with an AI SRE. One Helm install deploys the collector, and eBPF handles zero-code instrumentation across your services, third-party containers, and runtime dependencies to achieve end to end coverage. No SDKs, no code changes, no restarts. Metoro is also fully OpenTelemetry-compatible: send custom OTLP metrics and traces alongside the eBPF telemetry.

Metoro can run fully on-prem and air-gapped, so your data never leaves your environment. The same data powers AI root cause analysis, AI deployment verification, and alert investigation. It detects issues from live traffic, investigates across code and infrastructure, and can open a review-ready fix PRs. You can use your own AI models for the AI-powered features.

Tool complexity: Low

Differentiator(s):

• Full on-prem setup takes around 20-minutes.
• eBPF auto-instrumentation: captures requests, queries, service dependencies, and profiling across pods without code changes.
• Fully air-gapped including the AI, with no call-home and no telemetry to vendors.
• Kubernetes-native telemetry model: correlates logs, traces, metrics, profiles, resource state, and Kubernetes events automatically.
• AI SRE workflows: root cause analysis, alert investigation, deployment verification, and fix PRs from runtime telemetry and code context.
• Inherits your controls: SAML, OIDC, LDAP, SCIM, RBAC, customer-managed KMS keys.
• Predictable per-node licensing that does not drift with ingested log, metric, or trace volume.

Don't use if:

• You are not running Kubernetes (Metoro is purpose-built for K8s).
• 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 full managed SaaS. Runs on Kubernetes, including OpenShift and Rancher, on bare metal.

2. Coroot

Open-source eBPF Kubernetes observability

Pricing: $1/CPU core/month for Enterprise. OSS (Apache 2.0) available.
Setup time: Minutes (Helm).

Coroot is the closest open-source analog to Metoro's collection approach. It gathers metrics, logs, traces, and profiles automatically with eBPF and no code changes, then builds a service map and adds cloud cost monitoring on top. It is Apache 2.0, Kubernetes-oriented, and priced simply at $1 per monitored CPU core per month for Enterprise, with no per-user or ingestion fees.

The catch for air-gapped buyers is the AI. Coroot's root-cause analysis is an Enterprise feature, and by default the LLM is external: the AI configuration docs list Anthropic, OpenAI, and any OpenAI-compatible API. You could in principle point that at a local model, but Coroot does not document a hardened air-gapped mode, and air-gapped install sits on the top tier. If a strict reviewer is involved, validate that path first.

Tool complexity: Low

Differentiator(s):

• eBPF zero-code collection, so you get visibility without instrumenting every service.
• Broad coverage in one open-source tool: metrics, logs, traces, profiling, and cost.
• Simple per-core pricing with no ingestion or per-user fees.

Don't use if:

• You need air-gapped AI out of the box (RCA reaches an external LLM by default).
• You want a large vendor with a long on-prem track record.

Deployment options: Self-hosted OSS, or Coroot Enterprise with SSO, RBAC, and air-gapped install on the top tier.

3. Grafana (self-hosted LGTM stack)

The composable open stack you operate yourself

Pricing: OSS free. Grafana Enterprise is a self-managed license (sales-quoted).
Setup time: Days for a basic stack, longer to productionize.

The Grafana LGTM stack is the default open answer to "we want Datadog, but ours." Self-host Grafana plus Loki for logs, Mimir for metrics, Tempo for traces, and Pyroscope for profiling, and forward telemetry with Grafana Alloy, Prometheus, or OpenTelemetry. Grafana Enterprise adds RBAC, premium plugins, and support on a self-managed license.

The air-gap catch is the AI. Grafana Assistant runs its UI in your self-managed Grafana, but the docs are explicit that "the prompts you submit and the query context needed to generate a response are sent to the Grafana Cloud backend for processing." Sift is a Grafana Cloud feature too. The only genuinely offline path is the open-source LLM app plugin pointed at your own model, which is bring-your-own, not a built-in SRE.

There is a bigger strategic risk to weigh. Grafana Enterprise, the paid self-managed (on-prem, with support) edition, is not taking new feature requests for on-prem until 2029, as the company concentrates investment on its Cloud business. This is specifically Grafana Enterprise, not the open-source self-hosted version. So the commercial on-prem stack you adopt today is effectively in maintenance while the roadmap moves to SaaS, which is a real weakness if you are choosing on-prem precisely because SaaS is off the table.

Tool complexity: High

Differentiator(s):

• Fully open and self-hostable for logs, metrics, traces, and profiling.
• Huge ecosystem and skills your team probably already has.
• Enterprise license adds RBAC, support, and premium plugins without leaving your infrastructure.

Don't use if:

• You lack the platform headcount to run several distributed systems and their object storage.
• You need first-party AI offline (Assistant and Sift depend on Grafana Cloud).
• You want an actively evolving on-prem product (Grafana Enterprise is not taking new feature requests for on-prem until 2029).

Deployment options: Self-hosted OSS or Grafana Enterprise (self-managed). Grafana Cloud is the SaaS option.

4. Elastic Observability (self-managed)

Deep log search with a documented air-gap path

Pricing: Tiered subscriptions (sales-quoted). Gold and Platinum are closed to new self-managed customers.
Setup time: Hours to days.

If logs are your loudest problem, Elastic is a serious on-prem option. The Elastic Stack is fully self-managed, and Elastic publishes a dedicated air-gapped install guide. Collection is flexible through Elastic Agent and Fleet, classic APM agents, Beats, and OTLP.

On AI, Elastic has the strongest air-gap story of the open options. Anomaly-detection ML runs locally with no external LLM, and the generative AI Assistant can point at a self-hosted vLLM model for air-gapped environments, a setup that "does not require any outbound network access." Check your version, since bring-your-own-LLM for the Observability Assistant is recent. The trade-off is that operating Elasticsearch at scale is a specialist skill, and useful features are spread across paid tiers.

Tool complexity: High

Differentiator(s):

• Best-in-class log search and a mature, documented air-gapped install.
• Multiple collection paths including native OTLP.
• Local anomaly-detection ML, plus a gen-AI assistant that can run against an on-prem LLM on current versions.

Don't use if:

• You want turnkey Kubernetes context without operating Elasticsearch.
• You need a single predictable price rather than tier-gated features.

Deployment options: Self-managed (air-gapped supported) or Elastic Cloud (managed SaaS).

5. SigNoz

OpenTelemetry-native, one open-source app

Pricing: OSS free. Enterprise Self-Hosted is a support contract (sales-quoted).
Setup time: Under an hour for a basic install, more to productionize.

SigNoz keeps logs, metrics, and traces in a single open-source application on a ClickHouse backend. It self-hosts on a single node or Kubernetes, and a manual install path covers air-gapped environments, introduced with SigNoz Foundry. There is an Enterprise Self-Hosted tier with a support contract.

The trade-offs are honest ones. SigNoz is OpenTelemetry-native, so collection comes from OTel instrumentation, not eBPF auto-discovery. Continuous profiling is still an open tracking issue, a real gap next to the eBPF tools. And AI is limited on-prem: the "Noz" AI teammate is available to SigNoz Cloud users, so do not count on it in an air-gapped install.

Tool complexity: Medium

Differentiator(s):

• Logs, metrics, and traces in one open-source app, no per-host SaaS bill.
• Manual install path for air-gapped environments and an enterprise self-hosted tier with support.
• Clean fit for teams already standardized on OpenTelemetry.

Don't use if:

• You need continuous profiling (not shipped yet) or eBPF auto-collection.
• You need an AI assistant in an air-gapped install (it is Cloud-only).

Deployment options: Self-hosted OSS, Enterprise Self-Hosted, or SigNoz Cloud.

6. Dynatrace Managed

A commercial APM vendor with a real self-hosted product

Pricing: Enterprise, sales-quoted.
Setup time: Hours (cluster install plus OneAgent rollout).

Among the commercial APM vendors, Dynatrace still ships a genuine self-hosted product. Dynatrace Managed "provides all the monitoring capabilities on-premise within your own data center," using the OneAgent collection model with Davis AI. For an enterprise already standardized on Dynatrace, it is the path of least resistance.

The fair warning is that the newest parts of the platform are moving to the cloud you cannot use. Dynatrace states that "all innovations based on Grail are only available in Dynatrace SaaS environments" (Dynatrace), and Grail underpins much of the newer analytics and automation tooling. So you get a solid on-prem APM, while the latest capabilities increasingly live in SaaS.

Tool complexity: Medium to High

Differentiator(s):

• Proven self-hosted product that runs entirely in your data center.
• OneAgent gives strong automatic instrumentation, with Davis AI on-prem.
• Natural choice if Dynatrace is already your standard.

Don't use if:

• You want the newest Grail-based analytics and automation (SaaS-only).
• You are running a cost-driven evaluation (pricing is enterprise-tier).

Deployment options: Dynatrace Managed (on-prem) or Dynatrace SaaS.

Comparison of Datadog On-Prem Alternatives

Conclusion

If on-prem is a hard requirement, start from what pushed you off Datadog. The constraint is rarely cost alone; it is that telemetry, and often the control plane, cannot leave your network. Datadog is not in that field, and several "on-prem" tools quietly route their best features through a cloud, so check where the AI actually runs.

For Kubernetes teams, Metoro is the cleanest starting point: the whole platform, including the AI SRE, runs air-gapped on your hardware, and eBPF means visibility without a multi-quarter instrumentation project. If you want to own an open, composable stack and have the platform team for it, go with Grafana or SigNoz. If logs are your center of gravity, Elastic. If you are already a Dynatrace shop, Managed is the low-friction path. You can test Metoro yourself.

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