Mindgard Pricing: Demo-Led Scope and Quote Questions

Mindgard pricing starts with a demo and a scoped sales conversation. The public pages reviewed on 18 October 2026 describe a platform for discovering and assessing AI systems, then testing and defending them, but they publish no numeric amount. Procurement therefore needs a module map and workload assumptions before a Mindgard quote can be compared with another AI security proposal.

TL;DR

• Mindgard sends buyers through a demo-led commercial path and publishes no numeric amount on the reviewed platform or demo pages.
• Public scope includes discovery and reconnaissance plus automated red teaming and assessment. It also includes runtime protection and threat response alongside continuous assurance.
• Ask the quote to name applications, agents, models, tools, environments, test cadence, runtime paths, support, retention, and professional services.
• Keep pre-release adversarial testing and production threat response separate from authenticated HTTP authorization. Give each control its own acceptance tests.

Mindgard pricing is demo-led

The Mindgard homepage presents security for models and agents as well as applications, with a sales route instead of a public rate card. The demo page asks the buyer to book a demonstration and describes discovery with AI red teaming, runtime protection with threat response, and governance with continuous assurance. Neither reviewed page displays a numeric package amount.

A current written quote is the authority for price. It leaves billing units, package boundaries, minimum term, included capacity, and overage treatment to the sales process. The absence of a public amount gives no basis for inventing a per-model or per-request rate.

I would send back a quote containing only "Mindgard platform." Finance cannot reconcile that label at renewal, and engineering cannot test which work the order bought.

Start the procurement file with the review date and final URLs. Add a screenshot of the demo route. Mutable product pages should never be the only record behind a multi-year budget assumption.

Platform modules define the quote

Mindgard's AI security platform describes an attack-oriented workflow across AI systems and agents. Discovery and reconnaissance identify exposed components and attack paths. Automated red teaming and assessment test controls. Runtime protection and threat response address production activity. Continuous assurance retests defenses as applications and attacks change.

Those functions consume different resources. Reconnaissance across one public chatbot differs from testing an agent with multiple tools. A scheduled assessment differs from continuous testing on every release. Runtime monitoring adds production integration and availability obligations alongside telemetry and response obligations that a pre-release exercise may avoid.

Build a one-page scope sheet with a box for each application. Under each box, list the model endpoints and agents, then connected tools and data sources. Add separate rows for development and staging. Add another row for production. Put a red mark beside each runtime path and a blue mark beside each scheduled test. The vendor can then tie a commercial line to a visible unit.

The AI red teaming controls guide gives buyers an acceptance structure for recurring tests. Use it to distinguish an assessment report from an operating control.

Red teaming needs a stated cadence and target set

Automated red teaming can mean a one-time campaign, a recurring schedule, a release gate, or continuous testing. The quote should name which pattern applies. It should also state how many applications and agents enter the target set, which environments are permitted, and how new versions enter scope.

Ask Mindgard to define the attack library and customization boundary. Record whether the service tests model behavior alone or also exercises application logic, connected tools, and agent actions. Require rules of engagement and rate limits for production targets. Add emergency contacts. Evidence should include the tested version and route, the attack input and observed behavior, then the severity rationale and retest outcome.

A PDF with forty findings can look substantial while hiding a weak operating model. My preference is a smaller finding set with reproducible steps and named owners, followed by closed retests. That record lets a security architect confirm that remediation changed the exposed path.

Professional services also need their own line. Human-led campaigns and workshop time should be separated from custom attack development and remediation advice. The contract should identify included hours and the rate or process for additional work.

Runtime protection creates another commercial boundary

Mindgard's runtime threat detection and response page connects reconnaissance and automated red teaming with production defense. It describes finding exploitable paths and prioritizing vulnerabilities before applying fixes and validating that improvements hold under attack. It also frames guardrails as controls that should be tested against bypass attempts.

A runtime quote should identify the integration point. Ask which request and response paths Mindgard receives and which model and agent frameworks are supported. Record where enforcement occurs. Document expected latency and capacity, plus regional routing and failure behavior. Require an answer for what happens when the runtime service or a policy dependency becomes unavailable.

Threat response adds people and process. The quote should name alert destinations and triage ownership. It should also state response coverage and evidence retention alongside support targets. If the buyer expects Mindgard to block a request, the acceptance test must produce a blocked event on the intended production-like path. A dashboard alert alone proves detection.

The AI runtime security checklist can anchor this review. It keeps data-plane behavior and incident workflow in the same test packet without treating them as one feature.

Quote normalization needs eight written answers

Ask the proposal to identify the licensed modules and the unit used for each. Then request included capacity and overage treatment. Applications and agents may be scoping dimensions. Model routes and environments may also matter, along with assessments and test runs. Runtime interactions are another plausible dimension, but only Mindgard's written quote can establish the commercial meter.

The quote should also state deployment model and required buyer-operated infrastructure. Add data handling and retention. Specify evidence export separately. Record onboarding and custom integration work. Require support coverage and service commitments. Capture contract term and renewal assumptions. Record any limit on attack-library updates or continuous assurance.

Keep model-provider charges and cloud infrastructure outside the Mindgard license unless the order explicitly includes them. Store internal engineering time and remediation work on separate budget lines. A product can find a vulnerability without paying the team that changes the application.

Finally, ask for a workload example calculated with the proposed units. It should use the buyer's actual number of target applications and environments. It should also use the intended test cadence and runtime volume. Avoid converting that example into a universal rate.

Testing and runtime defense stay separate from request authorization

Mindgard focuses on finding exploitable AI risk and validating defenses across models and agents as well as applications and tools. An authenticated HTTP authorization decision determines whether this supplied identity may send this classified content to this LLM destination under the active enterprise policy.

Red teaming tests the application and its controls. Runtime threat detection identifies malicious behavior on instrumented paths. An identity-aware policy point evaluates the individual routed request and preserves the policy outcome independently of the calling application.

Local agent execution and browser-only interactions sit outside an HTTP LLM proxy unless the relevant traffic is deliberately routed through it. The same applies to STDIO tool calls. Endpoint and agent controls own those surfaces. The AI threat modeling guide helps assign each attack path to a control before procurement.

DeepInspect

DeepInspect governs authenticated HTTP requests deliberately routed between users or agents and LLM endpoints. It evaluates application-supplied identity and role with data classification and destination under versioned policy. It then creates an independent record of the decision before forwarding allowed traffic.

Mindgard can test that route and expose attack paths around the application. Keep the quote and acceptance tests tied to each product's actual boundary. Book a technical deep dive at deepinspect.ai.