The Dradis Alternative
With AI Built In.

Dradis Echo is a context layer that connects Dradis to an Ollama instance you stand up. The most recent release describes what Echo is adding: user-defined prompts, and personal access tokens for agentic use. Those are integration primitives. They are a toolkit for your team to build AI on top of, not a finished AI feature in the box.

What you actually deploy is three products. Dradis, Ollama, and a model your team picked and validated. Three release schedules to track, three places a change can break the workflow, three failure modes when something goes wrong before a deliverable.

And the output your testers ship to clients becomes a function of which model you chose, how well the in-house prompt library is tuned, and whether the model version you upgraded to last week still produces the format your reports expect. The AI quality you sell to your clients is now an internal engineering project.

Neuron's AI ships as a fully supported module. Neuron supplies the model and runs the inference. The prompts that turn a finding into a paragraph ship with the platform, tuned for the pentest workflow. Updates ship together. One vendor stands behind the whole AI stack, not three open-source projects you stitched together. No Ollama to operate, no model selection to maintain, no prompt library to keep tuned against the model you deployed this quarter.

One vendor. One AI. The quality your testers ship is the quality we ship.

Both products bill per seat. The difference is how capabilities are priced.

Dradis bundles features into tiers. SSO, SAML, LDAP, and audit logging live together in a separate Enterprise tier. If your team needs any one of them, you take the bundle, at custom-quoted prices.

That changes the pricing question from "do I need this feature" to "is this tier worth it." For a team that needs SSO and nothing else identity-related, that is the difference between turning a feature on and signing a custom Enterprise contract.

Neuron's pricing is per seat plus optional modules. Every seat includes SSO and the rest of the identity surface. Modules like Client Portal, AI, Workflow Integrations, and File Shares are priced individually and added only when you need them. The decision is per-capability, not per-tier.

Pay for the capability, not the tier it ships in.

Active Directory is where most internal engagements actually live, and the standard tools for graphing attack paths were built for one tester at a time, against one database at a time. Switching engagements means clearing data and re-importing.

Neuron's Directory module runs the graph inside the engagement. Multiple testers collaborate on the same graph in real time. Each engagement keeps its own isolated data, so there is no clearing between projects and no risk of a query pulling from the wrong directory. Findings tie directly to the path.

Dradis has no equivalent.

Dradis handles a retest by duplicating the project and re-tagging findings. That works as a workaround, but a retest becomes a second project with no formal relationship to the first. The audit chain that ties remediation back to the original engagement is something the team rebuilds manually.

Neuron treats each retest round as a first-class record under the same engagement, with its own dates, scope, and attestor. Peer cosign is gated by severity policy, so critical findings can require a second tester to verify before the round closes. Once a round moves to ready for approval, per-finding mutations freeze. Every state change writes a QA log entry with actor, timestamp, and prior state. Custom field and document section schemas are configured once per assessment type, so a web app retest asks different questions than an Active Directory retest.

Dradis scheduling is a monthly calendar with .ics export. It tells you what is scheduled.

Neuron's schedule is a Gantt the team runs from. Drag bars to reschedule, drop on a tester to reassign, switch between me, users, teams, by-client, and by-engagement perspectives without leaving the view. Capacity conflict detection surfaces overload day counts, peak concurrent counts, and a next-free window calculation. The Health Dashboard ranks twelve categories of risk before they hit kickoff. Retests appear as first-class allocatable scope on the Gantt alongside phases and assessments.