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Get it running
tonight.

Desktop appKubernetes
v0.4.132 · free · no signup
Option 01 · Desktop

Install it on your machine

One installer. The database and the scan workers are already inside, so there is nothing to provision and nothing to connect.

Download Classifyre

macOS · Windows · Linux — free, no signup

All builds on GitHub →

Workspaces

Run isolated workspaces locally.

Scan workers, sandboxed

Extraction and detection run as isolated local processes — the same code the cluster runs as Kubernetes Jobs.

Local folders as a source

The desktop build can scan a directory on your machine, a source type the server build deliberately does not expose.

Everything stays put

Sources, credentials, findings, and cases live on your disk. Nothing is uploaded to us.

First run

Four steps to your first finding

Nothing here needs a config file. Every step is in the app, and each one has a page on the docs site when you want the detail.

Tour the app
  1. 01

    Download and open

    Grab the build for your platform from GitHub Releases and install it the usual way. First launch initialises the embedded database — give it a few seconds.

  2. 02

    Connect a source

    Point it at something you already run — a database, an S3 bucket, a Confluence space, or just a local folder. Credentials are encrypted at rest.

    Configuring sources →
  3. 03

    Switch on detectors

    Enable the built-in packs you care about — PII, secrets, security, moderation, quality. They work on the first scan with no model setup.

    Pre-built detectors →
  4. 04

    Run a scan, open a case

    Findings land ranked by importance. Group them into inquiries and cases, or add an AI provider and let the autopilot work them between scans.

    AI providers →
Option 02 · Kubernetes

Or scale it on your cluster

The same open-source core as a Helm chart — web, API, worker, and ephemeral scan Jobs that fan out under load and scale to zero between runs.

Full deployment guide

Helm, OCI-native

No repo add step

Inspect before installing
helm show chart oci://registry-1.docker.io/classifyre/classifyre-core
Install
helm install classifyre \
  oci://registry-1.docker.io/classifyre/classifyre-core \
  --namespace classifyre --create-namespace \
  --version 0.4.132 \
  -f values.yaml
Prerequisites
Kubernetes
≥ 1.26
Helm
≥ 3.8 (OCI native)
Ingress
nginx by default
PostgreSQL
14+ external, or embedded for demos
Images · multi-arch

linux/amd64 + linux/arm64

Kubernetes deployment guide

Values files for k3s, an external database, and CloudNativePG; ingress, TLS, scaling, and storage.

Read the docs →

Database

Schema layout, migrations on upgrade, and connecting managed PostgreSQL.

Read the docs →

Object storage

Optional buckets for scan logs and uploaded artifacts.

Read the docs →

Upgrades & versioning

How image tags track the chart appVersion, and what a version bump implies.

Read the docs →
Not sure which

Pick by where the data has to stay

Both runtimes carry the same features, and a namespace export moves your work from one to the other, so this is not a decision you are locked into.

Choose desktop when

  • You are evaluating, or you investigate alone.
  • The corpus is on your machine or reachable from it.
  • Nothing may leave the laptop.
  • You want to be scanning within ten minutes.

Choose Kubernetes when

  • A team shares the instance, split into workspaces.
  • Scans need to run on a schedule, unattended.
  • The estate is large enough to need workers fanning out.
  • It has to sit inside your existing cluster and network.

Wondering what the enterprise layer adds on top of either runtime? It is SSO, roles, and per-workspace authorization — not features held back from the open-source core.

Open source vs Enterprise

Point it at something real.

The fastest honest test is a system you already run. Install it, connect one source, and see what the investigator turns up.

Download Classifyre

macOS · Windows · Linux — free, no signup

Try the live demo
Source on GitHubSupported sourcesDocumentation