Falco vs Tetragon vs Tracee: which one fits your Kubernetes runtime security shortlist
Falco is the alert-first choice, Tetragon is the option when in-kernel enforcement is required, and Tracee is the forensics-oriented choice. A platform team choosing runtime detection for Kubernetes nodes and workloads should first decide whether a suspicious syscall must be stopped by the kernel or only reported for an analyst or responder to act on. Falco, which is a CNCF graduated project, evaluates events in user space and forwards alerts to outputs such as standard output, files, syslog or HTTP(S) endpoints, so it fits teams whose response process is alert, triage and act. Tetragon applies filtering inside the kernel through TracingPolicy resources and can kill a process before it completes a system call, which matters when a policy must block rather than report. Tracee suits incident responders and forensics work that needs process lineage, file paths and network connections for a timeline. All three are licensed under Apache-2.0, so license alone does not separate them; governance, policy model and response model do.
Before you shortlist, confirm which of these matters most in your environment. If you need blocking, test a TracingPolicy on a non-production node pool and measure its effect on the workloads it touches, since the Tetragon documentation warns that policy authors need working knowledge of the Linux kernel and containers to avoid problems such as TOCTOU bugs. If you need detection with a broad rule library and alert routing, check Falco's rules and the Falcosidekick destinations your SIEM already reads. If your main need is evidence after an incident, check that Tracee's event fields cover the questions your forensics team asks. Also confirm whether your clusters allow the eBPF driver each tool requires, because node policy and kernel version can rule out options before features are compared. Scope the work in writing so that the test plan, rule ownership and tuning effort are visible before any tool is deployed.
Which runtime tool fits which scenario
This table maps six common Kubernetes runtime security scenarios to the tool that fits best and the condition that makes it the right choice. Read each row against your own requirements, and note where a second tool is needed, since these projects overlap but are not interchangeable.
| Scenario | Best fit | Why and where a second tool is needed |
|---|---|---|
| Alerts into an existing SIEM or Wazuh pipeline | Falco | Falco is alert-first and forwards events to files, syslog, HTTP endpoints or Falcosidekick. Wazuh publishes an integration where Falco on K3s with the modern eBPF driver writes JSON that a Wazuh agent reads. Confirm your own log path and parsing before relying on it. |
| Blocking a process in the kernel | Tetragon | Tetragon can filter and act on events in the kernel through TracingPolicy resources, for example killing a process when its privileges change. Falco documentation describes alerting, not blocking, so pair Falco with another control if you need prevention from it. Test policies carefully, since the Tetragon docs warn that kernel and container knowledge is needed to avoid issues such as TOCTOU bugs. |
| Incident forensics and process lineage | Tracee | Tracee records process lineage, file paths and network connections, and its documented use cases include forensics. Use it to reconstruct what happened on a node. Where you need alert routing to a SIEM at scale, forward Tracee output into your pipeline or add Falco for rule-based alerts. |
| Teams that already run Cilium | Tetragon | Tetragon is maintained within the Cilium project and fits teams already operating Cilium networking. Confirm how the policy model and observability stack interact with your Cilium version. If you do not run Cilium, weigh the dependence before choosing it for runtime detection alone. |
| Teams with strict CNCF governance requirements | Falco | Falco is Apache-2.0 and a CNCF graduated project, graduated on February 29, 2024. Tetragon is Apache-2.0 under the Cilium project, and Tracee is an Aqua Security open source project, so check each project's governance model against your procurement policy. Ask your assessor which governance evidence they accept. |
| Mixed Kubernetes plus cloud audit log monitoring | Falco, with a second tool for host depth | Falco plugins document sources beyond system calls, including Kubernetes audit logs, AWS CloudTrail and Okta, so one rule engine can correlate runtime and audit events. Tetragon and Tracee focus on kernel-level events, so confirm plugin coverage for your cloud accounts before assuming parity. |
How Falco, Tetragon and Tracee compare on capability
This table compares the three tools on licensing, latest supplied release, action on a match, Kubernetes awareness, data sources, metrics and install options. Where a cell reads as a confirmation item, the supplied facts did not settle that point, so verify it in your own environment before deciding. The main takeaway is that Falco alerts, Tetragon can act inside the kernel, and Tracee leans toward forensic event capture.
| Capability | Falco | Tetragon | Tracee |
|---|---|---|---|
| License | Apache-2.0 | Apache-2.0 | Apache-2.0 |
| Latest supplied release | 0.45.1, published October 19, 2026 | 1.7.1, published August 25, 2026 | 0.24.1, published November 19, 2025. Confirm whether newer releases exist before adopting. |
| Governance | CNCF graduated on February 29, 2024, originally created by Sysdig | Part of the Cilium project. Confirm the current governance model and maintainer mix with the project. | Aqua Security open source project. Confirm the maintainer base and release cadence on the repository. |
| Action on a match | Alerts to stdout, a file, syslog, a spawned program or an HTTP(S) endpoint. Blocking is not described in its documentation. | Filters, blocks and reacts in the kernel, such as killing a process when its privileges change. | Event capture and detections for monitoring and forensics. Confirm whether any response action is available in your version. |
| Kubernetes identity awareness | Kubernetes audit logs are a documented plugin source. Confirm the depth of workload context in your rules. | Understands namespaces and pods so policies can target individual workloads. | Tracks Kubernetes API connections as events. Confirm how workload identity is attached to each event. |
| Non-system data sources | Plugins for Kubernetes audit logs, AWS CloudTrail and Okta | Kernel functions via kprobes, tracepoints and uprobes | System calls, network activity and file operations via eBPF. Confirm plugin support for cloud or identity logs. |
| Policy model | Custom rules that alert on abnormal behavior | TracingPolicy Kubernetes custom resource, also loadable with the tetra CLI | Scopes and rules, used through the CLI or on Kubernetes |
| Metrics and CLI | Confirm metrics and output options in the Falco documentation for your deployment | Confirm metrics endpoints in the Tetragon documentation for your version | Prometheus metrics, a health endpoint and the separate traceectl CLI |
| Install options | Packaged for Kubernetes and hosts; eBPF probe is the default driver, with a kernel module option | Kubernetes deployment with the Cilium stack and the tetra CLI outside Kubernetes | Privileged Docker container with host PID and cgroup namespaces, or the aqua/tracee Helm chart as a DaemonSet |
Licensing and editions for each runtime tool
Falco is licensed under Apache-2.0, as shown in the falcosecurity/falco repository. It is a CNCF graduated project, a status it reached on February 29, 2024. Sysdig created Falco and its first commit dates to 2016, so the project began with a vendor but is now governed as a foundation project. The latest release in the supplied facts is 0.45.1, published on October 19, 2026.
- License: Apache-2.0 covers the open-source project. Your legal team should confirm that the terms fit your internal use, modification and redistribution needs.
- Governance: CNCF graduated status places project direction under a neutral foundation, which is the main reason to review Falco's governance model during procurement.
- Paid edition: The supplied facts do not document a paid Falco edition. Ask any commercial provider which support, hosting or extension terms they offer and whether those terms change the license of what you run.
- Companion components: Falcosidekick and plugins are separate projects. Check each component's license in its own repository before you bundle it into your deployment.
- Pinned version: Record 0.45.1 or your tested version in your change record, so the license and release review matches what runs in production.
Before deployment, read the LICENSE file in the Falco repository and confirm current commercial options with the vendor or project.
Tetragon is licensed under Apache-2.0, according to the cilium/tetragon repository. It is maintained as a Cilium project, which places its governance within the Cilium community rather than a standalone vendor project. The latest release in the supplied facts is 1.7.1, published on August 25, 2026. The supplied facts do not describe a paid Tetragon edition.
- License: Apache-2.0 applies to the Tetragon source. Confirm with your legal team that the license terms match your internal use and any redistribution of the binaries or container images you build.
- Maintainer model: Tetragon is a Cilium project. Check who funds and reviews releases for the component you plan to run, since that affects your roadmap risk.
- Commercial options: The facts do not document a paid edition or commercial support tier. Ask vendors and the community channels which support terms exist before you rely on them for production.
- Cilium ecosystem: If your cluster networking or support contracts depend on Cilium, include that dependency in your licensing review, because it affects which other components you must license and support.
- Pinned version: Record 1.7.1 or your tested version in your change record, along with the matching kernel and Kubernetes versions you validated.
Before deployment, read the LICENSE file in the Tetragon repository and confirm current commercial options with the Cilium project or a commercial provider.
Tracee is an Aqua Security open source project licensed under Apache-2.0, as described in the aquasecurity/tracee repository. Its governance follows the vendor's open-source model, so the project direction is set by the company that sponsors it. The most recent release in the supplied facts is 0.24.1, published on November 19, 2025. The supplied facts do not document a paid Tracee edition.
- License: Apache-2.0 covers the Tracee project. Confirm that its terms fit your internal use and any packaging you create for the Helm chart or container image.
- Vendor relationship: Aqua Security sells other products. The supplied facts do not say whether any Tracee feature is commercial, so ask Aqua Security directly which features, if any, sit outside the open-source release.
- Release recency: The last tagged release in the supplied facts is more than a year older than the other two tools' latest releases. Check the release activity and open issues before you adopt it.
- Companion tooling: The traceectl CLI is a separate component. Check its license and release status in its own location before you include it in your operating procedures.
- Pinned version: Record 0.24.1 or the version you tested, and note whether you installed it through the Helm chart or as a privileged Docker container.
Before deployment, read the LICENSE file in the Tracee repository and confirm current commercial options and release plans with Aqua Security.
How Falco rules, Tetragon TracingPolicy and Tracee policies differ in authoring
Falco, Tetragon and Tracee all ask your team to describe behavior in a policy file, but the artifact and the skill behind it differ. Falco uses custom rules that run in user space: a rule matches events from its driver and plugin sources and sends an alert to outputs such as standard output, a file, syslog, a program or an HTTP(S) endpoint. Authoring a Falco rule is mostly a question of knowing what the event fields mean and writing conditions that reduce noise, so the team needs strong detection engineering skills and a habit of tuning rules against real Kubernetes and host traffic before anything is routed to on-call. Because Falco alerts and does not block, a wrong rule costs analyst time rather than a stopped workload.
Tetragon uses TracingPolicy custom resources in Kubernetes, which attach to kprobes, tracepoints and uprobes and can act on a match. The same policy can be loaded with the tetra CLI or at startup outside Kubernetes. Here the team needs Linux kernel and container internals, not only detection skills, because a hook on the wrong function or a check made at the wrong point can be bypassed or misfire. The Tetragon documentation warns that writing tracing policies requires knowledge of the Linux kernel and containers to avoid issues such as TOCTOU bugs, where the value checked is not the value the kernel finally uses. Before enabling any enforcement action, run each policy in observe-only mode in a non-production cluster and review what it matches.
Tracee organizes detection around scopes and rules, which you can apply from the CLI or on Kubernetes through the aqua/tracee Helm chart. Scopes narrow what is traced (for example by container, process or user), and rules decide which events produce detections. Authoring is closer to Falco than to Tetragon in that it does not require writing kernel hooks yourself, but a team that wants forensic fields such as process lineage, file paths and network connections must still understand which events it needs and how much volume they produce. Confirm the current policy file format and scope syntax against the Tracee documentation for the version you deploy, since the policy model is the part most likely to change between releases.
How to test all three on your own Kubernetes nodes before choosing
This sequence lets your platform and security teams compare Falco, Tetragon and Tracee on the same nodes, with the same test behaviors and the same measurements, so the choice rests on your own evidence rather than on a feature list. Each step ends with a record your security review can check.
Select a non-production cluster that matches production
The platform team picks a non-production Kubernetes cluster whose node images, Linux kernel versions, container runtime and cluster version match production as closely as possible. Kernel version matters for eBPF behavior, so confirm the exact kernel build on each node pool with your operating system vendor or node image documentation. The step is done when the team has a written inventory of node pools, kernel versions and managed Kubernetes version, signed off by the platform owner.
Deploy Falco using the documented driver path
The platform engineer installs Falco first using the install path in the Falco documentation, keeping the default modern eBPF driver unless the test plan says otherwise. If the kernel module driver is under evaluation, record that choice separately, because it changes node privileges and kernel build requirements. The step is done when Falco pods run on every node in the test pool and a test alert is visible in standard output or the configured output channel. Remove Falco before deploying the next tool.
Deploy Tetragon and then Tracee one at a time
The platform engineer installs Tetragon with its Kubernetes deployment path, then removes it and installs Tracee using the aqua/tracee Helm chart as a DaemonSet, following the Tracee documentation. Running one tool at a time keeps kernel hooks and privileges from overlapping, which would make overhead and event counts impossible to attribute. The step is done when each tool's pods are healthy on all nodes and its own status or health endpoint reports ready, with the removal of the previous tool confirmed in the cluster.
Run one fixed set of test behaviors on each tool
The security engineer writes a short script of benign but suspicious behaviors that each product's documentation says it can observe, such as a shell spawned in a container, a write to a sensitive path and an outbound connection to an unexpected address. The same script runs against each tool on the same nodes, with the same workload and timing. Use test pods in a dedicated namespace and document each command. The step is done when a matrix shows, per behavior, whether each tool recorded an event, raised an alert or enforced a block, with the Tetragon enforcement case tested separately from detection.
Measure event volume and node overhead with your tooling
The platform team measures CPU, memory and event volume on the test nodes during the same workload window for each tool, using the monitoring stack your organization already runs. Compare results against a baseline captured with no runtime security tool installed. Tracee's Prometheus metrics can supply some of its own counts, while Falco and Tetragon need metrics collected from node and pod data. The step is done when the team holds baseline and per-tool figures for the same window and can state where its numbers come from. Do not compare them with figures from vendor marketing.
Verify that output reaches your SIEM or alerting pipeline
The security operations lead confirms that each tool's output actually lands in the system that will receive it in production. For Falco, check the output channel or Falcosidekick target. For Wazuh-based pipelines, Wazuh publishes an integration that reads Falco JSON from a file on a K3s cluster, so test that path if it applies. For Tetragon and Tracee, confirm the export format and transport you would use. The step is done when a test event from each tool appears, with its fields intact, in the destination SIEM or alerting tool.
Record privileges each deployment needs for review
The security engineer documents, for each tool, the Kubernetes and host privileges the deployment requires, including host namespaces, privileged containers, kernel capabilities and any kernel module loading. Tracee's documentation describes a privileged container with host PID and cgroup namespaces, and the Falco and Tetragon manifests should be read the same way. Attach the rendered manifests and the policy engine results to the record. The step is done when the security review has a per-tool privilege sheet it can approve or challenge, and the team knows which privileges would apply in production.
What implementing each runtime tool involves in practice
Falco is the lightest to start with at the policy level but still needs deliberate tuning. The first job is choosing a driver: Falco documents the modern eBPF probe as the default, built on the CO-RE paradigm, and a kernel module as the alternative. Confirm which driver your node images support before you write a single rule, because the kernel module depends on kernel build and on whether your managed Kubernetes provider lets you load modules on worker nodes. Rule tuning is ongoing work: default rules fire on legitimate administrative activity in most clusters, so someone must review noisy rules, add exceptions and keep custom rules under version control. Then decide where alerts go. Falco can write to standard output, a file, syslog, a program or an HTTP(S) endpoint, and Falcosidekick fans events out to many channels. A file read by a Wazuh agent, as in the Wazuh and Falco K3s integration, is a valid route, but it adds a second pipeline you must monitor and maintain.
Tetragon moves the hard work into policy design. Each TracingPolicy is a Kubernetes custom resource that hooks kprobes, tracepoints or uprobes and can act on a match, so the engineer writing it needs working knowledge of the Linux kernel and of containers. The Tetragon documentation warns that careless policies can run into problems such as TOCTOU bugs, which means a blocking policy that inspects arguments at the wrong moment can be bypassed or misfire. Budget time for testing each policy in a non-production cluster, observing what it matches, and only then enabling enforcement. Tetragon is tied to Cilium deployments in common setups, so confirm your CNI and kernel support before committing. The effort scales with how many policies you need, how many workloads they cover, and whether enforcement is in scope or only observation.
Tracee is typically run as a privileged container with host PID and cgroup namespaces when deployed through Docker, or as a DaemonSet from the aqua/tracee Helm chart on Kubernetes. Either way, the pod or container needs elevated host access to trace system calls, so your security review, pod security admission settings and node hardening standards must explicitly accept that privilege. Plan the Prometheus wiring as part of the deployment, since Tracee exposes Prometheus metrics and a health endpoint that your monitoring stack must scrape and alert on. Confirm that your retention and forwarding design for Tracee events exists before rollout, because forensic value depends on events reaching storage you control. For all three tools, the total effort depends on the clusters in scope, the number and complexity of policies or rules, and the alert or event destinations you must integrate. Ask any implementation partner for a written scope that names each cluster, each policy set and each downstream destination.
What drives the running cost of each runtime tool
No supplied fact gives a licensing fee for Falco, Tetragon or Tracee, since all three are Apache-2.0 open source. The running cost comes from the people and infrastructure around them: policy work, node testing, event storage, upgrades and the skills to maintain kernel-level logic. Use these cards to shape what you ask for in a written scope.
Rule and policy authoring
Falco rules are written in its own rule language, while Tetragon uses TracingPolicy custom resources and Tracee uses scopes and rules. Authoring is a one-time build cost for the first baseline, then a recurring cost each time a workload, image or business process changes. Ask the quote to separate the initial baseline from ongoing rule maintenance, and to name who owns the rules after handover.
Testing and tuning on nodes
Every rule or policy needs validation on representative nodes to separate real threats from noise from normal workloads. This is recurring, because cluster changes and new services shift the baseline. Ask whether the scope includes a test cluster that mirrors production, how many tuning cycles are included, and who signs off on suppressions and exceptions.
Event volume and SIEM storage
Falco, Tetragon and Tracee all produce event streams that you must route, store and search. Volume depends on how broad the rules are and how many syscalls or kernel functions they hook, so storage is a recurring cost that grows with the cluster. Ask the quote to state where events go (a file, syslog, Falcosidekick outputs or a SIEM), the retention period, and who pays for the receiving platform.
Node resource overhead
Each tool consumes CPU and memory on every node, and the cost of that overhead is paid in cluster capacity. Tetragon's in-kernel filtering is documented to reduce overhead for high-frequency events, but overhead still depends on the policies you load and the workload. Measure each candidate on your own nodes under load before sizing, and ask the quote to include that measurement in scope.
Upgrade work across release lines
Each project ships on its own cadence, and an upgrade means retesting rules, drivers and kernel compatibility. The current versions supplied are Falco 0.45.1, Tetragon 1.7.1 and Tracee 0.24.1, and their release histories differ. Ask for a written upgrade procedure, how many upgrade cycles a retainer covers, and how breaking changes in rule or policy formats are handled.
Kernel-level skills and staffing
Writing Tetragon policies requires knowledge of the Linux kernel and containers, and the documentation warns about pitfalls such as TOCTOU bugs. Falco rules are easier to start with, but tuning them still requires security and Kubernetes knowledge. This is the cost most often hidden in a plan: the person who can write and review kernel-level logic is scarce. Ask who on the vendor side holds that skill and what handover training is included.
What buyers ask before choosing or hiring help for runtime security
Can Falco block activity or only alert on it?
Falco detects and alerts. The Falco documentation describes detection and alerting, with alerts forwarded to other components that take action, and it does not describe Falco itself blocking activity. If you need a process killed or a syscall stopped in the kernel, you need a tool with an enforcement path, or you need to wire Falco alerts into a responder you already run. Confirm the response path you plan to use in the Falco documentation before you write it into a design. Falco alerts can reach standard output, a file, syslog, a spawned program or an HTTP(S) endpoint, and Falcosidekick forwards events to many channels, so the response component is a design decision, not a default.
Does Tetragon require Cilium?
Confirm this in your own environment before you commit. Tetragon is documented as an eBPF-based observability and runtime enforcement component, and it is Kubernetes-aware, but the deployment requirements depend on your CNI and how you install Tetragon in your cluster. Check the Tetragon overview and your CNI vendor's documentation, then test the install on a non-production cluster that matches your node images and kernel versions. Ask the team that operates your clusters which CNI they run today, because a change of CNI is a larger project than a change of runtime security tool.
How much effort does each tool need to run?
Effort comes from the policy work and the kernel and cluster context, more than from installing a binary. Falco needs rule tuning so alerts reflect your workloads, plus a decision about where alerts go. Tetragon needs TracingPolicy authoring, which the Tetragon documentation says requires knowledge of the Linux kernel and containers to avoid issues such as TOCTOU bugs. Tracee needs scope and rule configuration and a privileged DaemonSet or container deployment with host PID and cgroup namespaces. Ask each vendor or your provider for the policy count, the number of clusters and the node images in scope, because those numbers drive the work more than the tool choice does.
Can our team run this without outside help?
Often yes, if you have engineers who already run Kubernetes nodes and can read kernel-level events. The harder parts are policy authoring, tuning out noise, and keeping drivers and policies current across upgrades. Teams that lack kernel or container depth usually find Falco rule tuning easier to start with than Tetragon enforcement policies, because a mistaken enforcement policy can break workloads. A fixed-scope engagement from DeployOpen can plan, implement, document and hand over the work, so your staff keep the knowledge. Compare that against the time your own engineers would spend learning the tool.
What does the work need from our platform and security staff?
Platform staff need to own node images, kernel versions, cluster access for privileged workloads and the CNI decision. Security staff need to own detection content, alert triage, and the mapping of alerts to response steps. Ask who approves privileged DaemonSets, who can change enforcement policies, and who gets paged when an alert fires. Name these owners before the work starts, because unclear ownership is one of the most common reasons runtime security projects stall after go-live.
How does the driver choice affect Falco on managed Kubernetes?
Falco documents two drivers: the modern eBPF probe, built on the CO-RE paradigm and used by default, and a kernel module. Managed Kubernetes node pools often restrict loading kernel modules, which makes the modern eBPF probe the path to check first. Confirm with your provider which node images, kernel versions and privileges you can use, and test each driver on the node types you actually run. The Falco driver choice is covered in more depth in the sibling comparison on the modern eBPF probe and the kernel module.
What should we ask a provider for in a fixed-scope quote?
Ask for the scope in writing: the tools in scope, the clusters, node pools and kernel versions, the policy and rule count, the response paths you expect, and the test environment. Ask which deliverables you receive, such as deployed configuration, tuned rules or policies, runbooks and handover sessions, and what happens after go-live. Ask what is one-time work and what recurs, such as upgrades, rule maintenance and continuous testing, which DeployOpen offers as retainers. DeployOpen does not publish a price list, so a quote follows from the written scope, and the scope determines the cost.
What drives the cost of running runtime security in-house?
The main costs are staff time, test infrastructure and recurring maintenance. Staff time covers learning the tool, writing and tuning policies, and triaging alerts. Test infrastructure covers non-production clusters that match your production node images. Recurring costs include upgrades, driver and kernel compatibility checks, and policy updates as workloads change. Costs rise with the number of clusters, node images, compliance evidence you must keep, and the number of response paths. Ask your team to estimate each of these from your own environment, because no supplied figure applies to every organization.
How DeployOpen helps teams choose and implement a runtime security tool
DeployOpen plans, implements, hardens, documents and supports self-hosted open-source platforms inside the client's own infrastructure. For a Falco vs Tetragon vs Tracee decision, we start by mapping which clusters, nodes and response paths the team actually needs before recommending a tool or a driver.
How we scope
- Written scope covers which clusters, node pools and managed Kubernetes services are in or out of the evaluation.
- Scope records whether the team needs alerting only, in-kernel blocking, or forensic event capture.
- Engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts.
How we build
- We test the candidate tools on your own nodes, including kernel version and driver compatibility checks.
- We write and tune detection rules or TracingPolicy resources, then measure event volume and node overhead.
- We integrate alert outputs with your existing pipeline, such as SIEM, paging or ticketing, and test the routing.
How we hand over
- Handover documents the chosen tool, driver, policy set, rule ownership and the reasons behind each decision.
- Runbooks cover upgrades, rule changes, policy rollback and what to do when a node cannot load the probe.
- We prepare configuration and evidence for compliance assessments, though auditors and certification bodies issue any reports.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers after handover.
- Retainers can cover tracking of Falco, Tetragon or Tracee release changes and driver compatibility on your nodes.
- Support scope and response expectations are written into the retainer agreement before it starts.
