# Anteon: eBPF Kubernetes Monitoring and Load Testing in One Repo

> Anteon (formerly Ddosify) pairs an eBPF agent that maps Kubernetes service traffic with a Go load engine. Here is what the repository actually contains, how the self-hosted stack is distributed, and where the tool stops being the right choice.

**getanteon/anteon** — Anteon (formerly Ddosify): eBPF-based Kubernetes Monitoring and Performance Testing

- Repository: https://github.com/getanteon/anteon
- Website: https://getanteon.com
- Stars: 8,519 · Forks: 382
- Language: Go
- License: AGPL-3.0
- Published: 2026-09-21 · Updated: 2026-09-21 · Language: en
- Canonical page: https://hysenlabs.com/projects/getanteon-anteon

## What Anteon Solves, and Who Ends Up Using It

Anteon is an open-source, eBPF-based Kubernetes monitoring and performance testing platform, formerly named Ddosify. The problem it targets is the cost of visibility. The README states that Anteon automatically generates a service map of your cluster without code instrumentation or sidecars, and that you do not need to change code, restart services, or add extra components to get those insights. That is the core pitch: instead of instrumenting each service or running a sidecar proxy per pod, a single eBPF-based agent called Alaz observes traffic and reports what it sees.

The audience follows from that. Platform engineers who already run Kubernetes and want a service map with latency edges, marked in red when latency between services is high. Teams investigating slow SQL queries or services that respond slowly. Operators who want live CPU, memory, disk and network metrics per instance without deploying another exporter per node. Anteon also sends alerts to Slack when something unusual happens, such as a sudden CPU increase, according to the README.

The second half of the product is load generation. Anteon can generate performance tests from over 25 countries, offers a no-code scenario builder, and imports tests from Postman. The README describes performance testing as natively integrated with Kubernetes monitoring, which is the real differentiator: you can correlate a load test with what the cluster was doing at the time. This is a monitoring product with a testing arm, not a testing product with a dashboard bolted on.

## How the eBPF Agent and Load Engine Fit Together

The architecture visible in the repository splits into three pieces. The eBPF agent, Alaz, lives in its own repository (getanteon/alaz) and is what collects kernel-level data from cluster nodes. The Anteon Load Engine, historically Ddosify, is the Go code in the ddosify_engine/ directory of this repository. The self-hosted backend and UI live in selfhosted/, with installation instructions in that folder. The README points to Docker Hub images for both the engine and the self-hosted stack, and notes that Anteon is a Verified Publisher on Docker Hub, so there are no pull limits.

The data flow implied by the documentation is: Alaz attaches to the kernel on each node, observes network traffic between services, and reports the observed connections and their timing to the Anteon backend. The backend aggregates those observations into the service map and the metrics views. Because the observation happens at the kernel level rather than inside the application, no library or proxy sits in the request path. That is why the README can promise no instrumentation and no sidecars. The trade-off is equally structural: what the agent can see is bounded by what the kernel exposes and by what eBPF programs are permitted to attach in your environment.

The load engine is a separate concern. It is a Go program that generates traffic against a target, driven by scenarios that can be written by hand or built in the no-code builder. The connection between the two halves is that a test run and the cluster telemetry share a timeline, so a latency spike in the service map can be read against a load test that was running at that moment.

## Installing the Self-Hosted Stack and Running a First Test

The README does not reproduce installation commands. It directs readers to the selfhosted/ folder in the repository for installation instructions for the self-hosted version, and to the ddosify_engine/ folder for the load engine's own documentation covering installation, usage and features. The Docker Hub images are the distribution channel for the engine and the self-hosted stack. If you are evaluating the project, the first file to open is the README inside selfhosted/, because that is where the project says the steps live; this article cannot reproduce them faithfully.

What can be stated from the repository layout is the shape of the deployment. The selfhosted/ directory is a top-level entry alongside ddosify_engine/, and the release tags follow a selfhosted- prefix, with selfhosted-2.6.0 dated 2024-08-07 as the most recent release listed. That naming tells you the self-hosted stack is versioned independently from the load engine, so pinning a version means pinning the stack tag rather than a single project-wide number.

For the load engine, the practical entry point is the documentation under ddosify_engine/. A minimal workflow, based on the features the README describes, looks like this: define a scenario (by hand or through the no-code builder), point it at a target you own, and run it from the engine. The README's disclaimer is explicit that users must be the owner of the target system. If you want to import an existing API collection rather than write a scenario, the README states that tests can be imported directly from Postman.

The README lists Docker Hub as the place to get the engine and self-hosted images, and states that Anteon is a Verified Publisher there, so pulls are not rate limited. It does not give the image names or tags. Those are in the selfhosted/ instructions and the ddosify_engine/ documentation, which are the two files to read before you deploy anything. Before running anything against a cluster, also confirm which kernel and runtime the Alaz agent requires, since the agent repository is where that detail would be documented.

## Where Anteon Is the Wrong Tool

The most concrete limitation is visible in the release history. The most recent release listed is selfhosted-2.6.0 from 2024-08-07, and the two before it are from 2024-07-29 and 2024-05-27. The last push to the repository was on 2026-03-04, so commits continue, but the tagged releases for the self-hosted stack have not moved in a long time. If your adoption decision depends on a steady release cadence with changelogs you can track, that cadence is not evident here.

Second, eBPF is not free of preconditions. The README presents the agent as requiring no code changes and no sidecars, which is true from the application's perspective, but it does not mean the agent runs anywhere. Kernel version, kernel configuration, and the privileges granted to the agent's pods all affect whether eBPF programs can attach. The README does not document these requirements in the excerpt available; the agent repository is the place to check. A managed Kubernetes offering that restricts privileged workloads or blocks certain kernel features can make this class of tool unusable regardless of how good the UI is.

Third, if what you actually need is a scriptable load testing CLI in CI, the combined platform is heavier than the job. The repository bundles a monitoring stack, a backend, and a UI around the load engine. The engine's own documentation exists separately, but you are still adopting a project whose primary identity is Kubernetes monitoring. Teams that want a single binary they can run in a pipeline and forget about should weigh that.

Finally, the README carries an explicit disclaimer: Anteon is created for testing the performance of web applications, users must own the target system, and using it for harmful purposes is forbidden. That is a usage constraint, not a technical one, but it shapes who can deploy the load generation side at all.

## How Anteon Differs from Agent-Based APM

The natural comparison is with APM and distributed tracing systems that rely on language agents or OpenTelemetry instrumentation. Those tools give you spans with application-level context: function names, database statements as the driver sees them, custom attributes you attach in code. The price is exactly what Anteon avoids. You add a library or a collector per service, you redeploy, and you maintain the instrumentation as services change.

Anteon inverts the trade. Because observation happens in the kernel, it sees the connections between workloads without knowing anything about the code inside them. The README's framing, that the service map is generated without code instrumentation or sidecars, is the whole argument. The cost is resolution: an eBPF agent sees traffic and timing, not your business logic. A slow SQL query can be flagged as slow, as the README says, but the agent's view of it is network-level rather than a captured query plan.

A second comparison is with load testing tools that are purely clients, with no cluster visibility. Anteon's README repeatedly stresses native integration between testing and monitoring. If you already have a tracing stack you trust, adding Anteon's monitoring half may be redundant, and the load engine alone may be the only part worth taking. If you have no tracing at all and no appetite for instrumenting a large, polyglot cluster, the kernel-side approach is the lower-effort path to a service map.

## Licence, Upgrade Cost, and What the Repository Commits You To

Anteon is licensed under AGPL-3.0. That is a copyleft licence with a network clause: if you modify the software and let users interact with it over a network, the licence's obligations extend to those users. For an internal monitoring deployment where you run the stack unmodified, the practical effect is usually limited. For a product built on top of Anteon's code, or a hosted service that modifies it, the obligations are materially different from a permissive licence. This is not legal advice; the LICENSE file in the repository is the authoritative text and your counsel should read it.

Upgrade cost is shaped by the release structure. The self-hosted stack carries its own version line (selfhosted-2.6.0, selfhosted-2.5.0, selfhosted-2.2.0), and the Alaz agent lives in a separate repository with its own release cycle. That means an upgrade is not one version bump but a coordination problem: backend, agent, and the load engine can move independently, and a mismatch between agent and backend is the kind of failure that shows up as missing data rather than a clear error. The README does not document a compatibility matrix between agent versions and self-hosted versions, so pinning both and testing the pair together is the cautious approach.

One more thing to verify before you commit: the README states that the selfhosted folder contains the installation instructions, but the top-level README does not describe what the stack deploys, what ports it exposes, or what persistent storage it expects. Those details have to come from the folder itself. Treat the selfhosted/ README as the real installation document and the top-level README as an index.

## Conclusion

Adopt Anteon if you run Kubernetes and want a service map plus latency data without adding sidecars or touching application code, and if AGPL-3.0 fits how you distribute or host the software. Do not adopt it if you need a mature load testing CLI with a long release history, or if your cluster runs a kernel without the eBPF features the Alaz agent depends on. Before committing, verify three things: that your kernel and container runtime support the agent, what the self-hosted stack actually deploys into your cluster, and whether the AGPL-3.0 obligations are acceptable for your deployment model. The last push to the repository was on 2026-03-04, so check the release history before you plan an upgrade path.

## FAQ

### What is Anteon and what was it called before?

Anteon is an open-source, eBPF-based Kubernetes monitoring and performance testing platform, and the README states it was formerly named Ddosify. The load engine in the repository still carries the Ddosify name in its directory and documentation.

### Does Anteon require code changes or sidecars in my Kubernetes cluster?

According to the README, no. It states that Anteon creates the service map without code instrumentation or sidecars, using the eBPF-based agent Alaz, and that you do not need to change code, restart services, or add extra components.

### What licence is Anteon released under?

The repository is licensed under AGPL-3.0, as shown by the licence badge and the LICENSE file. The README does not discuss the practical implications of that licence for hosted or modified deployments.

### Where are the installation instructions for the self-hosted version?

The README points to the selfhosted/ folder in the repository for installation instructions for the self-hosted version, and to Docker Hub for the engine and self-hosted images. The top-level README does not reproduce the installation steps.

### Can Anteon generate load tests from multiple locations?

The README states that Anteon generates load and performance tests from over 25 countries worldwide, and that scenarios can be built without writing code. It also states that tests can be imported directly from Postman.

## Sources

- [getanteon/anteon on GitHub](https://github.com/getanteon/anteon)
- [License: AGPL-3.0](https://github.com/getanteon/anteon/blob/master/LICENSE)
- [Project website](https://getanteon.com)
- [README](https://github.com/getanteon/anteon/blob/master/README.md)
- [Releases](https://github.com/getanteon/anteon/releases)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/getanteon-anteon