What each is for, where Aembit is strong, and how the two differ, capability by capability.
Aembit · Based on the vendor's public documentation, last reviewed .
Aembit
Aembit is an identity and access platform for workloads and AI agents. Edge components inject short-lived credentials into workload requests, and its MCP Identity Gateway applies access policy and tool rules to every MCP request.
Aembit Cloud is a SaaS control plane. Aembit Edge runs in your environment, on Kubernetes, virtual machines, ECS, Lambda or CI/CD runners, and the MCP Identity Gateway is hosted by Aembit or self-hosted on a Linux host.
Vekri
Runtime authorization for AI agents. Vekri Gateway checks every tool call against a verified identity, a task scope and Cedar policy; privileged writes wait for a person's approval of the exact operation; a broker executes with credentials the agent never sees.
In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri.
Side by side
| Capability | What it means | Vekri | Aembit |
|---|---|---|---|
| Decides each tool call inline | Every agent tool call is allowed or denied before it reaches the protected system. | Yes Vekri Gateway decides every MCP tool call sent through it before the broker executes it; a denied call never reaches the system. It is a boundary only when it is the only path to that system. | Documented The MCP Identity Gateway sits in the path of every MCP request and evaluates access policy, including tool allow and block rules, before a call reaches the server. [1] [3] |
| Verified workload identity | An agent running as a workload proves who it is with a credential the platform issued, not a shared key. | Yes Kubernetes projected service-account tokens, verified with TokenReview before Vekri issues a short-lived token for the run; every call is then authenticated. SPIFFE X.509-SVIDs from a configured trust bundle (no Workload API yet). | Documented Trust Providers attest workloads on AWS, Azure, Google Cloud, Kubernetes service accounts, GitHub Actions, GitLab jobs, Terraform Cloud and Kerberos. [4] |
| Employee agents use company SSO | Agents that employees run sign in as that employee, through the company identity provider. | Yes Vekri is the MCP authorization server for Claude Code and federates to your OpenID Connect provider. SAML sign-in is not available yet. | Documented Users sign in through the company's OIDC or SAML identity provider when their agent connects, and Aembit then issues the agent a token for the gateway. Single sign-on is required by default. [1] [5] |
| Task-scoped permissions from the server | What an agent may do is set per task by the server, not by the agent or its prompt. | Yes A run scope for workloads, or a protected access assignment for employees. The effective permission is always an intersection, never a widening. | Not in public docs Access policies pair a client with a server and can add conditions such as time or location. A scope issued by the server for each task is not described. [5] |
| Policy as code, deny by default | Decisions come from versioned policy, and anything not allowed is refused. | Yes Cedar over typed operations. Unknown operations and evaluation errors are denied, and a revision can be simulated before it is published. | Partly Access policies are configured in the console, the API or Terraform, and tool rules match exact names or glob patterns; in allow mode, unmatched tools are denied. A policy language is not described. [3] [9] |
| Approval of the exact operation | A person approves one specific operation, and the approval cannot be reused for another. | Yes Privileged writes wait for another person to approve that operation, bound to its digest, short-lived and single use, with separation of duties. | Not in public docs Approval of an access request or a single tool call by a person is not described in the public documentation. |
| Agent never holds backend credentials | Secrets for the protected system stay out of the agent's reach. | Yes A broker executes with narrowly scoped Kubernetes or GitHub App credentials. Incoming tokens are never forwarded. | Documented The agent holds only a token for the gateway, which keeps downstream credentials in memory and never exposes them to agents. Aembit Edge injects short-lived credentials into workload requests. [2] [6] |
| Audit evidence of decision and outcome | Each call leaves a durable record of what was decided and what happened. | Yes Intent is recorded durably before execution and the outcome after; an uncertain outcome is never retried blindly. | Documented Gateway traffic produces mcp.request and mcp.response events with the user, and authorization events record each policy decision; logs can be streamed to a SIEM. Events for agent-to-gateway connections are planned. [2] [7] |
| AI agents on workstations | Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there. | Partly Vekri Endpoint discovers coding agents, desktop and IDE AI apps, local models and their MCP servers on macOS and Linux, and its local MCP gateway decides each MCP tool call sent through it (Claude Code verified connecting; tool calls verified with an MCP client library). On macOS, a network gateway mediates MCP requests to approved remote servers, and agent control stops AI agents the organization has not allowed. Hooks observe built-in tools without blocking them; Windows is not supported. | Not in public docs Discovering or monitoring coding agents on employee devices is not described in the public documentation. |
| Prompt and output inspection | Detects prompt injection or sensitive data in what models read and write. | Partly Tool results on Vekri Gateway and the local MCP gateway are inspected for prompt injection before the agent reads them; sensitive-data categories are detected on the device, off by default. Browsers and file uploads are not inspected. | Partly The gateway can send MCP tool listings, inputs and outputs to CrowdStrike AIDR and enforce its allow, block or transform verdict. Prompts and completions exchanged with a model are not inspected. [8] |
| Systems covered | Which systems, platforms or agents the product works with. | Kubernetes and GitHub, with GitLab, PostgreSQL, MySQL, MongoDB, Slack, Jira, Linear, Asana, ClickUp, monday.com, HubSpot, Pipedrive, Zendesk, Freshdesk and custom OpenAPI connectors in preview. | Workloads on AWS, Azure, Google Cloud, Kubernetes, virtual machines and CI/CD platforms, reaching cloud services, SaaS APIs and remote MCP servers over streamable HTTP. |
| Deployment | How the product is delivered. | In Vekri's cloud, or self-hosted in your Kubernetes cluster with no connection to Vekri. | Aembit Cloud is a SaaS control plane. Aembit Edge runs in your environment, on Kubernetes, virtual machines, ECS, Lambda or CI/CD runners, and the MCP Identity Gateway is hosted by Aembit or self-hosted on a Linux host. |
Every agent tool call is allowed or denied before it reaches the protected system.
An agent running as a workload proves who it is with a credential the platform issued, not a shared key.
Agents that employees run sign in as that employee, through the company identity provider.
What an agent may do is set per task by the server, not by the agent or its prompt.
Decisions come from versioned policy, and anything not allowed is refused.
A person approves one specific operation, and the approval cannot be reused for another.
Secrets for the protected system stay out of the agent's reach.
Each call leaves a durable record of what was decided and what happened.
Finds AI coding agents and other AI applications on employee devices, and watches or controls what they do there.
Detects prompt injection or sensitive data in what models read and write.
Which systems, platforms or agents the product works with.
How the product is delivered.
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Contact Sales
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