Compare the Top AI Control Planes that integrate with Cursor as of October 2026

This a list of AI Control Planes that integrate with Cursor. Use the filters on the left to add additional filters for products that have integrations with Cursor. View the products that work with Cursor in the table below.

What are AI Control Planes for Cursor?

AI control planes are centralized platforms that help organizations govern, manage, secure, and observe AI models, agents, applications, and infrastructure across enterprise environments. These platforms provide a unified layer for controlling access to AI services, routing model requests, enforcing policies, managing credentials, tracking usage, and monitoring performance across multiple models and AI providers. AI control planes often include capabilities such as model gateways, AI agent governance, guardrails, cost management, observability, security controls, audit logging, rate limiting, and policy enforcement. Many solutions integrate with large language models (LLMs), AI agents, cloud AI services, inference platforms, identity systems, developer tools, and enterprise applications to provide centralized oversight of an organization's AI ecosystem. By consolidating AI management and governance into a common control layer, AI control planes help organizations reduce risk, control costs, improve visibility, and scale AI adoption across teams and applications. Compare and read user reviews of the best AI Control Planes for Cursor currently available using the table below. This list is updated regularly.

  • 1
    Obot MCP Gateway
    Obot is an open-source AI infrastructure platform and Model Context Protocol (MCP) gateway that gives organizations a centralized control plane for discovering, onboarding, managing, securing, and scaling MCP servers, services that connect large language models and AI agents to enterprise systems, tools, and data. It bundles an MCP gateway, catalog, admin console, and optional built-in chat interface into a modern interface that integrates with identity providers (e.g., Okta, Google, GitHub) to enforce access control, authentication, and governance policies across MCP endpoints, ensuring secure, compliant AI interactions. Obot lets IT teams host local or remote MCP servers, proxy access through a secure gateway, define fine-grained user permissions, log and audit usage, and generate connection URLs for LLM clients such as Claude Desktop, Cursor, VS Code, or custom agents.
    Starting Price: Free
  • 2
    Peta

    Peta

    Peta

    Peta is an enterprise-grade control plane for the Model Context Protocol (MCP) that centralizes, secures, governs, and monitors how AI clients and agents access external tools, data, and APIs. It combines a zero-trust MCP gateway, secure vault, managed runtime, policy engine, human-in-the-loop approvals, and full audit logging into a single platform so organizations can enforce fine-grained access control, hide raw credentials, and track every tool call made by AI systems. Peta Core acts as a secure vault and gateway that encrypts credentials, issues short-lived service tokens, validates identity and policies on each request, orchestrates MCP server lifecycle with lazy loading and auto-recovery, and injects credentials at runtime without exposing them to agents. The Peta Console lets teams define who or which agents can access specific MCP tools in specific environments, set approval requirements, manage tokens, and analyze usage and costs.
    Starting Price: Free
  • 3
    Preloop

    Preloop

    Preloop

    Preloop is the open source AI agent control plane for agents that take real actions. It combines an MCP firewall for tool access, an AI model gateway for cost, safety, and attribution, policy-as-code with human approvals, runtime session observability, and audit trails in a single self-hostable platform. AI agents can deploy code, change infrastructure, move money, touch production data, and burn model spend in seconds, so Preloop helps teams control what agents can do, how much they spend, and which actions require human approval. It works with OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any MCP-compatible agent or managed runtime. Access rules can inspect arguments and context, not just tool names, with CEL expressions for fine-grained conditions. Teams can start with observability, then layer in approvals and deny rules without SDKs or invasive app changes.
    Starting Price: $290 per month
  • 4
    SuperBased

    SuperBased

    SuperBased

    SuperBased is a local-first control plane for AI coding agents that lets developers see, control, and right-size agent activity from one binary running on their own machine. It reads native session data from 40 coding tools without requiring a proxy, SDK rewrite, or special configuration, supporting agents such as Claude Code, Codex, Cursor, GitHub Copilot, OpenCode, Gemini CLI, Kilo Code, Qwen Code, Aider, Devin, and others. The dashboard tracks provider-reported token usage, cache reads and writes, costs, sessions, and projected next-message spend across tools that normally keep their data separate. Developers can also launch more than 20 CLI agents as terminal sessions, monitor several repositories from one screen, attach to a running agent, take over the keyboard, and hand control back when needed. Model routing helps teams match tasks to appropriate models, while egress gates can hold commands before execution so users can stop or redirect costly or risky actions.
    Starting Price: $0.90 per month
  • 5
    Humatron AI

    Humatron AI

    Humatron AI

    Humatron is an enterprise AI workforce platform that turns AI agents into secure, trusted, vendor-neutral AI workers that operate across teams, tools, and business workflows. Organizations can build, hire, customize, onboard, train, monitor, and govern AI workers from a single control plane, managing them more like digital employees than standalone assistants. Each worker can be powered by modern core agents such as Claude, OpenAI, Manus, OpenClaw, Hermes, Cursor, or custom agentic systems, allowing companies to mix or replace underlying models without changing the workforce layer. AI workers can act autonomously, initiate and complete tasks, collaborate across teams, learn new skills, and interact through existing channels such as email, Slack, Teams, Zoom, and phone. Builds define reusable tools, rules, behaviors, and agent integrations, while each hired worker becomes an isolated instance customized for a specific company, team, and job.
    Starting Price: $1 for 100 credits
  • 6
    Singulr

    Singulr

    Singulr

    Singulr is an enterprise AI governance and security platform that provides a unified control plane to help organizations discover, secure, and optimize AI adoption at scale. It addresses the growing gap between rapid AI usage and limited governance by delivering complete visibility into all AI systems in use, including homegrown applications, embedded AI, public tools, and shadow AI that often remains invisible to security teams. It continuously discovers and inventories AI assets across the organization, creating a real-time map of agents, models, and services, while assessing their risk through contextual analysis of data handling, model lineage, vulnerabilities, and compliance implications. Through its Singulr Pulse intelligence layer, it evaluates millions of AI systems, assigns risk scores, and supports automated onboarding workflows that reduce approval cycles from weeks to hours without compromising security.
  • 7
    Bevel

    Bevel

    Bevel

    Bevel is a vendor-agnostic, Git-backed control plane for enterprise AI agents, where an organization’s agents, context, skills, tools, permissions, and identities are defined as files the company owns in its own infrastructure and served to any agent runtime over MCP. Context is stored as typed knowledge nodes with provenance for every fact, including where it came from, who last changed it, and when it was verified, then compiled into a graph that can be traversed, updated, and used for dashboards. Skills are written as plain Markdown procedures that process owners can read, review in diffs, and port across runtimes. Tool manifests define available capabilities, while secrets stay in a vault and access rules determine which agents may read specific files or call endpoints. Each agent has its own identity, credentials, and scope so actions remain attributable.
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