See why teams choose Barndoor over Runlayer for MCP gateway security, AI agent governance, and governed automation. Compared feature by feature.
Key takeaways
- Barndoor binds every agent to a single profile: identity, credentials, MCP tool access, and LLM spend together in one policy-enforced object. Runlayer’s Agent Accounts also give each agent its own persistent identity and credentials, but spend and cost tracking live separately, rather than in the same identity record.
- Barndoor data protection tokenizes sensitive values with reversible placeholders, so downstream systems keep working on de-identified data; Runlayer’s data layer classifies and scans for sensitive content but doesn’t publish a reversible tokenization mechanism.
- Barndoor data protection offers metadata-based policies such as conditional rules and field controls, not offered by Runlayer.
- Barndoor Automations compiles a plain-English process description into a blueprint — deterministic logic runs the routine steps, and a model steps in only where a step genuinely needs judgment — inheriting the builder’s exact access and spend controls; Runlayer’s Platform Agents are configurable agent workers triggered by a schedule, webhook, or Slack event, with no documented deterministic/judgment split.
Where Barndoor takes a different approach
Inline data protection on model and tool traffic
Runlayer runs two gateways: an LLM Gateway that proxies and routes model calls and an MCP Gateway with Guard – ToolGuard, PII detection, and AgentGuard. The sensitive data detection layer is documented to be on the tool-call path only.
Barndoor LLM Gateway and MCP Governance run on the same policy engine, with inline DLP, PII detection, and prompt-injection screening for both LLM Gateway and MCP Governance. Barndoor data protection also offers field-level data controls that support wildcard and custom name-pattern matching.
Reversible tokenization vs. classification and scanning
Runlayer automates data classification and real-time scanning of tool calls, outputs, and sensitive data, but it’s unclear what happens to the data once it’s flagged.
Barndoor data protection pairs pattern-based, model-based, and custom classifiers with seven enforcement actions on anything they catch – block, tokenize, redact, mask, omit, alert-only, or allow. Tokenize replaces a sensitive value with a reversible placeholder, so a downstream system (or a human reviewer) can still process the request without ever seeing the raw value.
Agent identity bound to the agent
Definition of AgentProfile: Barndoor’s unified record binding an agent’s identity, credentials, MCP tool access, and LLM spend into a single, policy-enforced profile, independent of the human who deployed it. Available today for select customers; broader rollout in progress.
Runlayer’s governance model is identity-aware policy scoped by user, group, role, agent account, client, connector, tool, and OAuth/network state, and its Agent Accounts do give each agent its own persistent identity with independently-issued credentials. What isn’t unified there: spend and cost tracking live in a separate ROI & Observability feature, not inside that same identity object.
Barndoor has the ability to issue each agent its own non-human, machine-to-machine credential, and AgentProfile extends that into a single record for identity, credentials, tool access, and spend, so an agent’s activity, cost, and access are never pooled into a shared team or user account, and an agent can be deprovisioned on its own without touching the human account that created it.
Deployment: SaaS, private cloud, or air-gapped
Runlayer’s infrastructure (ECS Fargate, EKS, multi-AZ failover) is cloud-native and Runlayer-managed; the documentation doesn’t describe a self-hosted or air-gapped deployment path.
Barndoor Pro runs as SaaS. Barndoor Enterprise adds private cloud, on-premises, and fully air-gapped deployment options, the deciding factor for regulated teams (finance, healthcare, defense, government) that can’t route agent and tool traffic through a third party’s cloud at all.
Feature comparison
Here’s how Barndoor and Runlayer compare across governance, data protection, automation, and deployment.
| Capability | Runlayer | Barndoor |
|---|---|---|
| Governance model | Identity-aware policy scoped by user, group, role, agent account, client, connector, tool, OAuth state, and network/runtime conditions. | Unified policy engine spanning LLM Gateway and MCP Governance; RBAC and ABAC with non-human, per-agent credentials, extended by AgentProfile to bind identity, access, and spend into one record independent of the human who deployed the agent. |
| AI spend visibility | AI spend monitoring and cost attribution by team. | Token and cost budgeting with per-team, per-user, and per-model caps (LLM Gateway, Enterprise); per-agent spend visibility via AgentProfile (select customers today). |
| Governed automations | Platform Agents – configurable agent workers (custom instructions, a model, curated MCP connectors) triggered by a schedule, webhook, Slack message, or audit event; no documented deterministic-logic/judgment-call split. | Barndoor Automations enables teams to create precise, reliable, and distributable AI workflows built on the open source Frags runtime engine. Barndoor also governs third-party automations (e.g. n8n, Workato) as MCP servers. |
| Audit & observability | Complete audit logging of actor, client, connector, tool, and policy outcome, with analytics for security and platform teams. | Action-level audit trails tying every action to a user, role, or agent across both model calls and tool calls, centralized usage dashboards, and log export/streaming on Enterprise. |
| Supported AI clients / IDEs | LLM Gateway: Claude Code, Cursor, GitHub Copilot, Cline, etc. via Anthropic/OpenAI-compatible endpoints. MCP Gateway separately claims major AI clients for tool/connector access. | Any AI app, agent framework, or MCP server – local, vendor-hosted, or third-party – plus hundreds of Barndoor-hosted MCPs, without requiring client-side changes. |
| MCP change management | Scans tool definitions at registration and inspects runtime activity. No documented re-scanning or alert when a previously-approved tool’s definition changes after the fact. | Tracks every connected MCP server’s tool inventory and version history, maps which policies a change would impact, and issues alerts when a server’s tools change. |
| Model-layer (LLM) gateway | Separate LLM Gateway: virtual keys, cost-based routing and budget-triggered downgrading, session visibility across major AI clients. No documented DLP or injection detection on this path. | LLM Gateway on the same policy engine as MCP Governance: model routing/failover, per-team/per-model budgets, plus inline DLP, tokenization, and prompt-injection detection on prompts and responses. |
| Deployment model | Cloud-native, Runlayer-managed infrastructure (AWS ECS Fargate / EKS); no published self-hosted or air-gapped option. | SaaS (Pro); SaaS, private cloud/on-premises, or air-gapped (Enterprise). |
| Pricing model | Not published; demo-gated. | Free 14-day trial, no credit card required; Pro and Enterprise plans, contact sales for pricing. |
| Shadow AI / unmanaged agent discovery | Discovers unmanaged agents, MCPs, skills, and plugins already running across the organization as a named, dedicated capability. | Governs and catalogs every MCP connection made through Barndoor’s gateway; doesn’t publish a standalone scanner for agents or tools running entirely outside the platform. |
| DLP capabilities | Guard (ToolGuard, PII detection, AgentGuard) scans tool registrations, tool-call outputs, and session trajectories for PII and runtime threats; findings go to a dashboard, audit logs, and SIEM export, with session-level alert or block. No published redact, mask, tokenize, or omit action on a flagged value, and the separate LLM Gateway docs don’t describe PII detection on model traffic itself. | Seven enforcement actions on detected sensitive data – block, tokenize (reversible), redact, mask, omit, alert-only, allow – applied inline at both the LLM Gateway and MCP Governance layers. Includes metadata-based policies such as conditional rules and field controls. |
| No-code agent creation & MCP catalog | No-code agent builder for employees, plus access to a catalog of pre-built MCPs for enterprise tools. | Barndoor MCP library plus any third-party or local MCP server under the same access policy, with Automations for building and distributing governed agentic workflows; no published equivalent to a from-scratch no-code agent builder. |
Frequently asked questions
What’s the core governance difference between Runlayer and Barndoor?
Runlayer governs the MCP and AI-client layer — which tools an agent can call, scoped by user, role, and OAuth session. Barndoor governs that same tool layer and the model layer underneath it in one policy engine, issuing each agent its own non-human credential and unifying it with tool access and LLM spend through AgentProfile, and enforcing data protection with reversible tokenization rather than classification alone.
Does Runlayer offer anything like Barndoor Automations?
Runlayer offers Platform Agents: configurable agent workers with custom instructions, a model, and curated MCP connectors, triggered by a schedule, webhook, or Slack message. Barndoor Automations takes a different approach: you describe the process you want in plain English, and Barndoor compiles it into a blueprint — deterministic logic runs the routine steps, and a model steps in only where the process genuinely needs a judgment call, handing back a structured decision so the blueprint keeps running unattended. Every blueprint inherits the exact access and spend controls of the person who built it, so it can safely reach systems the rest of the team can’t, without standing up a separate permission system. It also extends to automation you’ve already built elsewhere: workflows running in n8n or Workato come under the same governance as any other MCP server, with no rebuild required. The moment a blueprint is published, it’s live as both an API and an MCP endpoint, with a full run history of every step it took and every decision it made.
Which AI clients/IDEs does each platform support?
Runlayer supports major AI clients, including Cursor, Claude Code, ChatGPT, VS Code, GitHub Copilot, Codex, and Windsurf. Barndoor is built to work with any AI app, agent framework, or MCP server – local, vendor-hosted, or third-party – plus hundreds of Barndoor-hosted MCPs, without requiring changes on the client side.
Is Barndoor SOC 2 / ISO 27001 certified?
Yes, Barndoor is SOC 2 Type II and ISO 27001 certified.
How does pricing compare?
Barndoor offers a 14-day free trial with no credit card required across MCP Governance and LLM Gateway, then Pro (SaaS) and Enterprise (SaaS, private cloud, or air-gapped) plans, both quote-based. Runlayer’s pricing is not published.
Does Runlayer’s LLM Gateway include the same data protection as its MCP Gateway?
Runlayer runs a separate LLM Gateway for model traffic alongside its MCP Gateway, where Guard (ToolGuard, PII detection, AgentGuard) scans tool registrations, tool-call outputs, and session trajectories for PII and runtime threats, routing findings to a dashboard, audit logs, and SIEM export with session-level alert or block.
That detection is scoped to tool-call traffic. Its documentation does not describe PII detection on model traffic, and Runlayer doesn’t publish redact, mask, or tokenize actions on a flagged value. Barndoor LLM Gateway and MCP Governance share one policy engine, so prompts, responses, and tool calls all get the same inline DLP, tokenization, and injection-detection treatment under one audit record.
Can Runlayer deploy on-premises or air-gapped?
Runlayer’s documented architecture (ECS Fargate, EKS, multi-AZ failover) is cloud-native and run on Runlayer’s own AWS infrastructure, with no published self-hosted or air-gapped deployment path. Barndoor Enterprise supports SaaS, private cloud/on-premises, and fully air-gapped deployments for teams that can’t route agent and tool traffic through a third party’s cloud.
Does Runlayer alert you when an MCP tool’s definition changes after it’s been approved?
Runlayer scans a tool’s definition at registration and inspects runtime call activity afterwards. Its published docs don’t describe re-scanning an already-approved tool or alerting when its definition changes later.
Barndoor tracks every connected MCP server’s full tool inventory and version history, flags which policies a change would touch, and alerts the moment a server’s tools move, so a definition change after approval is surfaced.
Conclusion
Barndoor unifies identity, credentials, MCP tool access, and LLM spend into one policy-enforced object, where Runlayer’s architecture splits identity/access from cost tracking.
Barndoor governs access across one policy engine spanning both the model layer and the MCP layer, with the same inline DLP, tokenization, and injection detection enforced on both, and agent identity bound to the agent itself rather than a shared session.
Barndoor also adds a third pillar – Automations. Rather than a separate agent-configuration tool, Barndoor Automations is a governed capability based on the same access controls. Builders describe a process in plain English to create a blueprint that runs deterministic logic on its own and hands off to a model only when judgment is needed.
Ready to see it for yourself? Book a demo or start a free 14-day trial – no credit card required.
Last updated: October 8, 2026
