Agentic Index
DeepKeep vs Repello AI (2026)
This is the closest overlap in the AI security lane: both pair autonomous red teaming with runtime guardrails across models, agents and MCP, and both sell a continuous loop rather than a point in time test.
Repello leans developer native, shipping open source tooling including an Artemis GitHub Action so adversarial testing runs inside CI where code already moves. DeepKeep leans platform native, spanning pre deployment model scanning through runtime enforcement to AI Lens, which governs which models employees are permitted to use at all, and deploying SaaS, private cloud, on premises or fully air gapped, inline or out of band. Neither publishes a rate card. Note on both sides that DeepKeep's security grade rests on product capability with its own corporate attestation unconfirmed.
Choose DeepKeep if
- You need one product covering model scanning, runtime firewall and employee AI usage.
- Deployment has to reach fully air gapped environments.
- You want a named red teaming agent that pauses for review rather than running unattended.
Choose Repello AI if
- You want adversarial testing running inside CI through a GitHub Action.
- Open source tooling you can read and extend matters to your security engineers.
- Your scope is LLM, agent and MCP security without the employee usage governance layer.
| At a glance | DeepKeep | Repello AI |
|---|---|---|
| Category | Security / SOC agent | Security / SOC agent |
| Entry price | Contact for pricing | Contact sales |
| 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. | Open-source tooling available (Artemis GitHub Action, Whistleblower) |
| Pricing confidence | contact only | contact only |
| Feature | D DeepKeep |
R Repello AI |
|---|---|---|
| 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. |
Full / Explicit | Full / Explicit |
|
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 | Full / Explicit |
|
Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
Partial | Partial |
| 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. |
Full / Explicit | Partial |
|
Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
Full / Explicit | Full / Explicit |
|
Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
Full / Explicit | Partial |
| Solution readiness | ||
|
Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
Full / Explicit | 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. |
Partial | No / Not documented |
|
APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
Full / Explicit | 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 | Partial |
| 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 |
R Repello AI |
|---|---|---|
|
Entry price Lowest public entry point |
Contact for pricing | Contact sales |
|
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. | scope of AI systems under continuous red teaming and runtime protection |
|
Variable cost Workload / overage exposure |
High 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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