Metoro
Metoro is a Kubernetes-native observability platform whose eBPF collector feeds an AI SRE agent that detects, root-causes, and opens fix pull requests for
Metoro is the rare AI SRE tool where the agent has real context to work with: the eBPF DaemonSet pre-correlates seven signal types with Kubernetes identity, so root-cause analysis starts from actual runtime data instead of guesswork. The free Hobby tier is a real small-cluster plan (1 cluster, 1 user, 2 nodes, 28-day retention), not a trial, and Scale at $20/node/month with $0.20/GB excess ingest over 100GB per node undercuts Datadog for Kubernetes-native teams. Against Grafana plus a separate AI layer, the win is one query language and one agent over the same store. Skip it if you're not on Kubernetes — there is genuinely nothing here for VM or serverless estates.
Verified 4d ago · liveness 82/100 · cite: rightaichoice.com/tools/metoro
- SRE teams running Kubernetes at scale
- Platform engineering teams wanting zero-instrumentation observability
- DevOps teams cutting MTTR with autonomous alert investigation
- Organizations needing BYOC, on-prem, or airgapped Kubernetes observability
- Teams not running Kubernetes
- Organizations with VM-based or serverless workloads
- Buyers who need mobile or desktop monitoring apps
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Skip Metoro if you don't run Kubernetes — every signal it collects, every context field the AI reasons over, and the per-node billing model all assume containerized workloads on a cluster.
Excess ingest on Scale runs $0.20/GB over 100GB per node per month, so a chatty service can push a single node's bill well past $20.
The free Hobby tier covers a single 1-user, 2-node cluster — enough to evaluate, not enough for a team. Scale at $20 per node/month with $0.20/GB excess over 100GB per node fits mid-size Kubernetes teams and typically lands under Datadog for observability-plus-AI, since Datadog bills per host with separate line items for APM and log volume. Enterprise adds on-prem, BYOC, SSO/SAML, and custom SLAs for large regulated clusters.
In short
Metoro — Metoro is a Kubernetes-native observability platform whose eBPF collector feeds an AI SRE agent that detects, root-causes, and opens fix pull requests for. Best for SRE teams running Kubernetes at scale, Platform engineering teams wanting zero-instrumentation observability, DevOps teams cutting MTTR with autonomous alert investigation. Free to start; paid plans from $20/mo.
What's new in Metoro
Checked 4 days agoAcross the latest 5 updates: 2 feature updates, 2 launches and 1 changelog entry.
Metoro speeds up permission-filtered listings up to 100x
Permission checks are now fetched once per request and evaluated in memory, so dashboard and alert search on a 500-dashboard install dropped from around 4 seconds to tens of milliseconds. Semantics are unchanged.
Metoro on-premises 11.0.0 ships new ClickHouse telemetry storage engine
11.x creates only new ClickHouse tables with JSON-native attributes and retention-cohort partitioning. There is no in-place upgrade from 10.x, so existing 10.x hubs must stay put pending migration tooling.
Metoro adds KEDA autoscaling (alpha)
Any Metoro query aggregating to a single series can drive a KEDA ScaledObject through a Prometheus-compatible MetoroQL endpoint, so no separate Prometheus pipeline is needed. Breaking interface changes are expected while it is alpha.
Webhook notifications for Metoro AI SRE Guardian
AI SRE notifications can now be delivered to webhooks alongside Slack and email, covering investigation completions and deployment and verification events.
Metoro launches Kubernetes-native RBAC
Row-level telemetry controls and CRD-managed permissions let platform teams manage Metoro access control through Kubernetes and GitOps, with auditable results.
What people actually say about Metoro — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
71 mentions across 4 sources (Hacker News, YouTube, Product Hunt, Bluesky) · researched Jul 6, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +eBPF-based zero-instrumentation telemetry eliminates SDK overhead and code changes.
- +Automated root cause analysis with evidence summaries and fix PRs.
- +Deployment verification by comparing pre- and post-deployment telemetry.
- +Unified query language (MetoroQL) across logs, metrics, traces, and profiling.
- +Single Helm install deploys in under 5 minutes with no configuration.
- −Autonomous fix PRs raise security and reliability concerns.
- −False positives possible in noisy or naturally spiky environments.
- −Limited track record at scale — still an early-stage product.
- −No clear data residency guarantees for compliance-sensitive teams.
- −Dependencies on eBPF may limit kernel version compatibility.
- • Scaling costs can rise quickly with node count; no fixed per-node price disclosed. May need additional costs for Prometheus/OTel hosting if relying on external data source.
Viability Score
How well maintained and how widely used is Metoro? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- eBPF kernel-level telemetry collection with no SDKs, code changes, or restarts
- DaemonSet collector capturing logs, metrics, traces, profiling, and Kubernetes events
- Zero-code distributed traces for HTTP, gRPC, Kafka, and database protocols
- Continuous on-CPU profiling with per-process flame graphs
- Kubernetes resource viewer with versioned change history and point-in-time diffs
- Deployment context linking git SHA to affected workloads, author, and PR
- AI autonomous issue detection and root cause analysis
- AI alert investigation returning root cause and next steps before on-call digs in
- AI deployment verification comparing pre- and post-deployment telemetry
- Automated fix pull requests through the GitHub integration
- MetoroQL unified query language across all telemetry signals
- PromQL support and OpenTelemetry / Prometheus-compatible ingestion
- Advisor for right-sizing, OOM detection, and CPU throttling findings
- Kubernetes-native RBAC with row-level telemetry controls managed via CRDs
- Metoro MCP Server for pulling production insights into local development agents
About Metoro
Metoro installs as a single Helm chart that runs a DaemonSet on every Kubernetes node, then pulls telemetry out of the Linux kernel with eBPF. No SDKs, no application code changes, no container restarts. One collector produces logs, metrics, traces, continuous CPU profiling, Kubernetes events, versioned Kubernetes resource history, and deployment context tied to a git SHA — all pre-correlated with workload identity, so services and pods line up before an investigation starts. Anything eBPF can't see can arrive through OpenTelemetry or Prometheus-compatible ingestion. The paid part of the product is the AI SRE layer that sits on that data. It detects unusual behavior autonomously, investigates firing alerts and returns root cause plus next steps before on-call logs in, verifies each deployment by comparing pre- and post-deployment telemetry, and opens fix pull requests through the GitHub integration. It also ships an Advisor for right-sizing, OOM, and CPU throttling findings, runbook-following investigations, an MCP server for pulling production context into local coding agents, KEDA autoscaling driven by MetoroQL queries, and Kubernetes-native RBAC with row-level telemetry controls managed through CRDs and GitOps. The intended buyer is an SRE, platform engineering, or DevOps team already running Kubernetes in production — EKS, GKE, AKS, OpenShift, or bare metal — that wants faster incident response without a months-long instrumentation project. Deployment options cover Metoro Cloud, BYOC (Metoro in your own VPC, managed by Metoro), and on-premises including airgapped. The trade-off is scope: collection, context, and pricing are all framed in Kubernetes terms, so VM-based, serverless, and mixed estates get nothing from it.
Behind the Verdict
What separates Metoro from the wave of AI SRE startups is where the data comes from. The collector runs as a DaemonSet and captures signals in the kernel, which means traces are created without touching application code and profiling runs continuously without redeploying anything. Every signal arrives already mapped to services, pods, namespaces, and deploys, and Kubernetes resources are stored with full change history so you can diff cluster state at a point in time. That is the context the AI layer reasons over, and it is why the agent's alert investigations, deployment verifications, and fix pull requests are worth reading rather than skimming. The product surface is broader than the homepage suggests. Beyond dashboards, service maps, cost monitoring, uptime and cron job monitoring, there is an Advisor covering right-sizing, OOM, and CPU throttling; a Kubernetes resource viewer with versioned diffs; runbook-following investigations; an MCP server so local coding agents can query production; and KEDA autoscaling fed by a Prometheus-compatible MetoroQL endpoint. Kubernetes-native RBAC landed recently, letting platform teams manage access through CRDs and GitOps with row-level telemetry scoping. Where it gets awkward: adoption is per-node and per-cluster, so the free tier caps at 2 nodes and one user — fine for evaluation, useless for a real team. AI SRE token usage is passed through at the underlying model cost rather than bundled, so agent-heavy shops should set usage limits and watch that line separately from the platform subscription; Enterprise can route requests through their own AWS Bedrock keys. On-premises 11.0.0 moved to a new ClickHouse storage engine with JSON-native attributes and retention-cohort partitioning, and there is no in-place upgrade from 10.x — a real migration project if you run on-prem. Finally, the whole thing is Kubernetes-only by design. If your estate is VM-based, serverless, or heavily mixed, Metoro covers none of it, and you should be looking at a general-purpose observability vendor instead.
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Real-world workflow fit
Concrete scenarios for the personas Metoro actually fits — and what changes day-one when you adopt it.
Install Metoro with one Helm chart; within five minutes the DaemonSet is streaming logs, metrics, traces, profiling, and Kubernetes events into a single store. Connect Slack and PagerDuty, then let the AI SRE agent investigate the next firing alert.
Outcome: The alert arrives on Slack already carrying root cause and suggested next steps, so the on-call engineer starts from an answer instead of a dashboard.
Use the Kubernetes resource viewer with versioned change history and point-in-time diffs to compare cluster state before and after a rollout, then review the Advisor's right-sizing, OOM, and CPU throttling findings for the workloads it flags.
Outcome: Config drift and resource waste get caught from recorded state rather than reconstructed after the fact.
Connect GitHub so Metoro can inspect code changes tied to each deployment; the deployment verification workflow compares pre- and post-deploy telemetry and flags regressions.
Outcome: Regressions surface during the deploy window with evidence attached, and fix pull requests arrive as a reviewable draft rather than a late-night page.
Use Cases
- Detect and root-cause production incidents in Kubernetes clusters without an instrumentation project.
- Verify every deployment against pre- and post-deployment telemetry to catch regressions immediately.
- Have AI investigate each firing alert so on-call wakes up to root cause and next steps instead of raw noise.
- Triage noisy alerts versus real incidents using AI alert investigation with supporting evidence.
- Debug a production issue in the same query language you used to detect it, without switching tools.
- Find underutilized, overutilized, and CPU-throttled workloads with Advisor right-sizing recommendations.
- Run BYOC, on-premises, or airgapped Kubernetes observability in security-sensitive environments.
- Autoscale workloads with KEDA using MetoroQL queries instead of maintaining a separate Prometheus pipeline.
Models Under the Hood
as of 2026-09-25
Limitations
- Metoro is Kubernetes-only by design — collection, context, and pricing are all framed around nodes, clusters, and workloads, so VM-based, serverless, and mixed estates are not served.
- The free Hobby tier caps at 1 cluster, 1 user, and 2 nodes with 28-day retention.
- Scale is $20 USD/node/month with $0.20/GB on excess ingest over 100GB per node, so cost scales with node count and log volume rather than user count.
- AI SRE model usage is passed through at the underlying model cost rather than bundled into the platform price, which means agent-heavy teams should set usage limits; Enterprise can bring their own AWS Bedrock keys.
- SSO/SAML, RBAC, and audit logs are gated above Hobby.
- On-premises 11.0.0 introduced a new ClickHouse storage engine and has no in-place upgrade from 10.x, so existing 10.x hubs must wait for migration tooling.
- KEDA autoscaling is alpha and may change its interface.
as of 2026-10-04
Verification history
We have re-verified Metoro 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Metoro tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/mo
Ideal for
A single engineer evaluating Kubernetes observability on a small dev or staging cluster of two nodes or fewer.
What this tier adds
Free entry point: 1 cluster, 1 user, 2 nodes, 28-day retention, basic dashboards, and limited AI root cause analysis and alert investigation.
Scale
$20/node/mo
Ideal for
A platform or SRE team running production Kubernetes clusters that needs unlimited clusters, users, and nodes plus the full AI SRE agent.
What this tier adds
Adds unlimited clusters, users, and nodes, unlimited dashboards and alerting, and full AI root cause analysis and alert investigation, billed at $20 USD per node per month with $0.20/GB on excess ingest over 100GB per node.
Enterprise
Custom
Ideal for
Large or regulated organizations that need on-prem or BYOC deployment, custom SLAs, SSO/SAML, and a support channel.
What this tier adds
Adds bulk discounts, 24/7 white glove support and onboarding, custom SLAs, on-premises and bring-your-own-cloud deployment, a dedicated Slack channel, and SSO/SAML with RBAC.
Where the pricing makes sense
The company stage and team size where Metoro's pricing actually pencils out — and where peers do it cheaper.
The free Hobby tier covers a single 1-user, 2-node cluster — enough to evaluate, not enough for a team. Scale at $20 per node/month with $0.20/GB excess over 100GB per node fits mid-size Kubernetes teams and typically lands under Datadog for observability-plus-AI, since Datadog bills per host with separate line items for APM and log volume. Enterprise adds on-prem, BYOC, SSO/SAML, and custom SLAs for large regulated clusters.
Setup time & first value
How long it actually takes to get something useful out of Metoro — broken out by persona, not the marketing-page minute.
A single Helm install is the whole setup: the DaemonSet is ready in about 30 seconds, signals flow at roughly two minutes, and the AI agent is monitoring by the five-minute mark. No SDKs, code changes, or restarts. Expect the same timeline across EKS, GKE, AKS, OpenShift, and bare metal; the longer work is wiring alert destinations and GitHub rather than getting telemetry in.
Switching to or from Metoro
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Datadog: run Metoro alongside on the same clusters, compare coverage, and retire Datadog dashboards after validating signal parity.
- →From Grafana: import existing Grafana dashboards into Metoro, then move queries to MetoroQL where eBPF coverage replaces hand-built panels.
- →From a Prometheus stack: point Prometheus/OpenTelemetry ingestion at Metoro and keep existing scrape configs while the kernel collector fills gaps.
- →From a homegrown eBPF or agent-based collector: keep the Helm install path and drop per-service instrumentation work as coverage is confirmed.
- →From on-prem Metoro 10.x: there is no in-place upgrade to 11.0.0 — plan a parallel install and wait for migration tooling from the vendor.
- ↗To Datadog: export dashboards and rewrite MetoroQL queries in Datadog's query language; expect to pay per host with APM and logs billed separately.
- ↗To Grafana plus Prometheus: export dashboards from Metoro, keep the OpenTelemetry pipeline, and rebuild AI alert investigation as manual on-call work.
- ↗To another Kubernetes observability vendor: retain the OpenTelemetry and Prometheus ingestion paths so custom signals port cleanly.
- ↗To a cloud-native managed service: use the BYOC or on-prem options only if the destination can run a DaemonSet with kernel access.
Integrations
Resources & Guides
- Documentationmetoro.io
Docs · Metoro
Full product docs from metoro.io
- Documentationmetoro.io
Llms · Metoro
Full product docs from metoro.io
- Quickstartmetoro.io
Getting Started · Metoro
Get up and running fast from metoro.io
- Documentationmetoro.io
Overview · Metoro
Full product docs from metoro.io
- Documentationmetoro.io
Overview · Metoro
Full product docs from metoro.io
- Documentationmetoro.io
Metoroql · Metoro
Full product docs from metoro.io
- Documentationmetoro.io
Overview · Metoro
Full product docs from metoro.io
- Resourcemetoro.io
Knowledge Base · Metoro
Helpful link from metoro.io
- Resourcemetoro.io
Blog · Metoro
Helpful link from metoro.io
Tutorials & Learning
YouTube returned 6 videos for “Metoro”, and we withheld 6: 6 could not be judged, because “Metoro” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Metoro.
Official links
Tools that pair well with Metoro
Common stack mates teams adopt alongside Metoro, with the specific reason each pairing earns its keep.
Corelayer
AI-native incident response that finds production root causes, cuts alert noise, and opens fix PRs — deployable on-prem or in your cloud.
Dash0
OpenTelemetry-native observability with an AI SRE that investigates incidents and opens fix PRs.
Comet
Opik, Comet's open-source LLM observability and eval platform, turns agent traces into root-cause groupings and git-committed code fixes
Featured Head-to-Head Comparisons
Metoro vs Spider Cloud
Metoro and Spider Cloud serve completely different domains—Kubernetes SRE vs. web data extraction. Choose Metoro if you run Kubernetes and need AI-driven incident response with zero-instrumentation observability. Choose Spider Cloud if you need fast, reliable web scraping for AI agents, with a pay-as-you-go model and deep LLM integrations.
Metoro vs Temporal Ai
Temporal AI and Metoro solve completely different problems: Temporal is a durable execution platform for building reliable AI agents and workflows that survive failures, while Metoro is a Kubernetes-native AI SRE agent for autonomous observability and incident response. Pick Temporal if you need to orchestrate long-running, fault-tolerant processes with human-in-the-loop and state persistence. Pick Metoro if you manage Kubernetes in production and want zero-instrumentation observability with AI-driven root cause analysis and automatic fix PRs.
Metoro vs Presto Voice
Presto Voice and Metoro serve entirely different domains: drive-thru voice AI for QSRs vs. Kubernetes-native AI SRE. Your choice depends solely on whether you need to automate restaurant order-taking (Presto) or reduce MTTR in Kubernetes operations (Metoro). There is no overlap—select based on your industry and operational focus.
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Frequently Asked Questions
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