Agentic Index
DeepKeep vs Mindgard (2026)
DeepKeep and Mindgard both red team the customer's own models, apps and agents, and both add runtime protection; neither publishes a price. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
DeepKeep documents more of the grid, 8.5 of 14 against 7. DeepKeep adds Reddy, a red teaming agent that plans attack paths and pauses at key decisions for the security team to steer, an inline AI Firewall that logs every interaction, and an Agent Scanner for n8n, CrewAI, Make and Dify agents, deployable from SaaS to air gapped. Mindgard's red teamer works like an adversary, from reconnaissance that finds shadow AI through multi step attacks, continuously or in CI/CD, with a Python SDK, CLI and API, and expert red teaming services sold beside it. On the grid DeepKeep is Full on deployment, human oversight and observability where Mindgard is None or Partial; Mindgard is Full on its API where DeepKeep is Partial. Choose DeepKeep for steerable testing and air gapped deployment; choose Mindgard to wire red teaming into your pipeline.
On the Agentic Index AI SOC ranking, neither DeepKeep nor Mindgard clears the bar, which asks for all five investigation loop capabilities documented in full. DeepKeep does not document workflow orchestration in full; Mindgard documents two of the five in full. 23 of the 85 vendors in the lane clear it. See the AI SOC ranking
On the Agentic Index agent infrastructure ranking, neither DeepKeep nor Mindgard clears the bar, which asks for all five production contract capabilities documented in full. DeepKeep does not document security and identity governance in full, nor model flexibility and routing; Mindgard documents one of the five in full. 34 of the 186 vendors in the lane clear it. See the agent infrastructure ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. DeepKeep and Mindgard are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 956 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose DeepKeep if
- The security team should steer the red teaming agent at key decisions.
- Deployment must reach air gapped environments; DeepKeep is Full and Mindgard None.
- Every AI interaction should be logged at an inline firewall.
Choose Mindgard if
- Red teaming should run in CI/CD through an SDK, CLI and API; Mindgard is Full on its API.
- Finding shadow AI is part of the job.
- Human red team experts should be available beside the platform.
| At a glance | DeepKeep | Mindgard |
|---|---|---|
| Category | Security / SOC agent | Security / SOC agent |
| Entry price | Contact for pricing | Contact sales; no prices published. Demo on request. |
| Free / trial | No free tier or trial published. A Scan your Agent form offers an agent attack surface scan but it routes to a submission form rather than self serve access. | Not published |
| Pricing confidence | contact only | contact only |
| Feature | D DeepKeep |
M Mindgard |
|---|---|---|
| Action & orchestration | ||
|
Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
Full / Explicit | Full / Explicit |
|
Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
Partial | Partial |
|
Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
Full / Explicit | Full / Explicit |
| Knowledge & context | ||
|
Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
Partial | Partial |
|
Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
No / Not documented | No / Not documented |
| Control & trust | ||
|
Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
Full / Explicit | Partial |
|
Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
Partial | Partial |
|
Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
Full / Explicit | Partial |
|
Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
Full / Explicit | No / Not documented |
| Solution readiness | ||
|
Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
Partial | Partial |
| Platform extensibility | ||
|
Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
No / Not documented | No / Not documented |
|
APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
Partial | Full / Explicit |
|
Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
Full / Explicit | Full / Explicit |
| Specialist automation | ||
|
Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
No / Not documented | No / Not documented |
Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | D DeepKeep |
M Mindgard |
|---|---|---|
|
Entry price Lowest public entry point |
Contact for pricing | Contact sales; no prices published. Demo on request. |
|
Pricing confidence How public the numbers are |
Contact only | Contact only |
|
Billing Primary billing axis |
Not published. No seat, token, interaction or scan unit disclosed anywhere retrieved. | Inference, not stated by the vendor: AI systems under continuous testing plus any expert services. |
|
Variable cost Workload / overage exposure |
Medium variable cost | Medium variable cost |
|
Free tier / trial Try before you buy |
No free tier
|
No free tier
|
|
Buying motion Self-serve vs sales call |
Sales call | Sales call |
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