AI & MCP
In depth: Speakeasy vs Runlayer
Nolan Sullivan
- 11 min read
NOTE
This comparison of Speakeasy and Runlayer reflects the state of both products as of June 2026. This is a fast-moving category and both companies ship quickly, so some specifics will age. We also won’t pretend to be neutral: we build Speakeasy, and we think the architecture we’ve chosen is the right one for enterprise AI governance. But we’ll show our work, link to primary sources for every claim we make about Runlayer, and be honest about where the two products genuinely overlap. If you think we need to update this post, let us know.
Speakeasy and Runlayer are AI governance platforms trusted by companies to secure AI usage across their organization. There is overlap in the use cases the platforms serve, but many features and implementation details differ.
- Runlayer is a tools gateway creating a golden path for tool use by securing MCP and skills usage across the organization.
- Speakeasy is an all-in-one AI control plane for governing and enabling safe AI usage. In addition to an MCP gateway, it includes observability, cost-tracking, assistant creation, and policy enforcement.
How is Speakeasy different?
What each product governs reveals its priorities:
- Runlayer focuses on MCP. Its documentation calls it “a unified control plane for internal MCP enablement.”
- Speakeasy focuses on end-to-end AI usage: model calls, MCP tool calls, agents, assistants.
Broadly, Speakeasy secures everything on the path between AI and your systems. Runlayer creates a pre-approved MCP registry.
2. Speakeasy secures and enables; Runlayer secures
What each product is trying to do for you is different:
- Runlayer is built to secure and gate AI usage: policies, approvals, threat scanning, and audit over MCP.
- Speakeasy does all of that and enables adoption: it builds connectors from your APIs, creates assistants, tracks cost and usage, and shows leadership the impact.
Broadly, Runlayer is a security product. Speakeasy is a security + enablement platform: it governs AI usage and helps the organization actually adopt it.
3. Speakeasy is proven in production; Runlayer is new
Track record is the part a checklist can’t capture:
- Speakeasy has years of enterprise production usage behind it, with API and SDK infrastructure relied on by some of the world’s largest companies, and the control plane builds on that foundation.
- Runlayer is a newer entrant to the category, with a limited public record of enterprise-scale deployments.
Broadly, one is a known quantity in production and the other is still establishing its track record. The rest of this post compares features as they stand today, which is where the two are closest.
Evaluating platform capabilities
We evaluate each platform against the core functions of an AI control plane: connect, secure, control, observe.
Connect: building MCP servers vs brokering them
Runlayer is the more MCP-specific product, but when it comes to building MCP servers, Speakeasy goes deeper, a result of our history as an artifact-generation company.
That’s important because the most valuable data to put behind AI is the data locked in internal APIs, and that data never shows up in a public catalog.
Point Speakeasy at an internal API and it generates a governed MCP server straight from the contract your teams already maintain. Those servers stay token-efficient through dynamic toolsets and tool-group filtering, so an agent loads only the tools a task needs instead of paying for every tool definition up front. Runlayer brokers MCP servers that already exist, from its catalog , an endpoint you point it at, or a custom server you build, and its MCP Builder scaffolds new ones from a natural-language prompt rather than your API contract. Speakeasy also ships a deeper catalog of pre-approved servers, 50+ to Runlayer’s 11.
Connect
Both connect to existing servers and SaaS tools. The dividing line is generation from APIs: Speakeasy manufactures governed connectors from the API contracts you already own; Runlayer manages the servers you bring it.
Secure: enforcement across surfaces vs MCP tool scanning
Speakeasy inspects and enforces across a wide surface: “every prompt, response, and agent action” in real time, with PII, credentials, and API keys blocked before they leave the perimeter and incidents routed to Slack, PagerDuty, or your SIEM. Because the plane sees the whole path, it correlates a block with the prompt that caused it, the identity behind it, and the API behavior underneath, the visibility Speakeasy argues “no single gateway possesses independently.”
Runlayer anchors enforcement to the MCP tool layer through two ML systems. ToolGuard is a suite guarding against tool poisoning, prompt injection, and output manipulation, with a Tool List Guard that scans definitions at registration, a Tool Call Guard that scans outputs in real time, and a Tool Intent Guard that compares requested versus actual behavior. It blocks by default with “typical scan times of 50-100ms.” AgentGuard adds the session-trajectory layer that single-call scanners miss, watching “the agent’s trajectory across a session” to catch reasoning pivots and slow-chain drift. It’s a credible posture for the MCP tool layer.
Secure
Speakeasy enforces across the full path, not just the MCP tool layer. Runlayer’s ToolGuard and AgentGuard are genuinely strong within that layer; the difference is breadth of surface, not whether enforcement happens inline.
Control: policy across every AI surface vs MCP-scoped policy
Similar to security, the governance stories are close. Speakeasy enforces role-based permissions at the server, toolset, and individual tool level, with access that automatically follows existing roles from your IdP and credentials managed centrally. The differentiator is consistency across surfaces: the same policy plane covers MCP, agents, and assistants, rather than governing the MCP layer alone.
Runlayer’s policy engine is genuinely strong too, combining principals (users, groups, roles, agent accounts), scope (servers or individual tools), and runtime conditions such as tool-argument restrictions, IP ranges, and OAuth properties, with session isolation so an agent can’t diverge mid-session. Paired with admin approvals, that’s a mature access model for MCP.
Control
Speakeasy applies one policy model across every AI surface, not just MCP. Runlayer’s runtime conditions and session isolation are a mature access model within the MCP layer.
Observe: full-path visibility vs MCP-layer analytics
Both products give leadership the dashboards they will ask for. Speakeasy’s analytics span employees, agents, and assistants, with human-versus-agent task views, cost-by-model breakdowns, and security signals. Speakeasy’s audit logs can be queried directly, analyzed via AI chat, or exported to your organization’s SIEM provider.
Runlayer similarly tracks usage across teams to measure impact and tool effectiveness, with a full audit trail of “every tool call, permission change, and access event” and the same SIEM-export expectation. The audit trail and adoption metrics focus on MCP usage rather than the bigger picture of AI usage.
Observe
Observability is close to a tie. The distinction is scope: Speakeasy’s trail correlates the prompt, identity, tool call, and API behavior in one place.
Architecture and delivery: flexible deployment vs MDM-centric agents
Both products can run at the edge; the difference is how much choice you get. Speakeasy supports three deployment modes: a cloud control plane and gateway, a device agent for managed machines, and plugins embedded directly in the AI clients and agents teams already use.
Runlayer leans on MDM-pushed endpoint agents. Its Shadow AI Detect (scheduled scans for shadow MCP servers and skills) and Enforce (blocking unmanaged MCP sources and policy-checking local tool calls in real time), along with auto-provisioning of MCP config into each client, run through Jamf, Intune, Kandji, Mosyle, and similar MDMs, with a CLI path for smaller rollouts.
Architecture and delivery
Speakeasy isn’t cloud-only: deploy via gateway, device agent, or in-client plugins, with no MDM required. Runlayer’s edge is off-network shadow-AI discovery through MDM-deployed device scanning.
Enterprise readiness and track record
Both Speakeasy and Runlayer are SOC 2 Type II certified, and both also meet HIPAA and GDPR. Speakeasy adds ISO 27001 certification. Both integrate with enterprise identity providers: SSO with Okta and Entra, plus SCIM and group sync.
The harder thing to evaluate is production track record. Speakeasy is not a new company: its API and SDK infrastructure runs in production at scale, generating SDKs relied on by companies like Google, Verizon, and Mistral. The control plane builds on that foundation along with the forward-deployed engineering that comes with it. Runlayer is a capable but newer entrant, with limited experience working with the world’s biggest companies. That isn’t a knock on the engineering. It’s simply a different risk profile, so a buyer should diligence references, uptime, and support model directly.
Enterprise readiness and track record
Compliance and identity are close to even. Track record is where Speakeasy’s history shows: years of enterprise production usage versus a newer entrant.
When to choose Speakeasy vs Runlayer
Choose Speakeasy if you need to govern more than MCP (agents, assistants, skills, and the APIs underneath), if turning your internal API surface into governed tools is central to your AI program, if you want one policy and audit model across every AI surface, or if ISO 27001 is a requirement.
Choose Runlayer if your AI program is squarely MCP enablement of popular SaaS tools, if catching and blocking shadow AI on managed laptops (including off-network) is a top priority, and if you already run an MDM fleet you’re comfortable extending. Runlayer’s ToolGuard, AgentGuard, and endpoint reach are genuine strengths inside that scope.
Recommendations by team type
Best fit by team
The bottom line
Runlayer is a capable, security-forward MCP platform. Inside the MCP boundary it’s strong: a thoughtful policy engine, real inline tool scanning plus a trajectory model, mature audit logs, and endpoint reach for shadow AI that a cloud plane can’t fully match.
Speakeasy is a holistic AI control plane built for enterprise governance. It governs every AI surface from one plane rather than the MCP layer alone, it manufactures governed connectors directly from your OpenAPI surface instead of brokering the ones that already exist, and it brings ISO 27001 alongside SOC 2 and hands-on integration engineering to the rollout.
The deciding question isn’t which product checks the connect, secure, control, and observe boxes. Both do. It’s whether you’re buying governance for MCP, or governance for everything your AI touches. If it’s the latter, that’s the AI control plane we’ve built.
Runlayer is a Model Context Protocol (MCP) platform that governs MCP servers, skills, and agents. Speakeasy is an AI control plane that governs every AI surface on the path between agents and systems, including assistants, skills, MCP servers, and the APIs and LLM calls underneath. The biggest practical differences are that Speakeasy generates governed connectors from your OpenAPI specs, while Runlayer brokers existing ones, and that Speakeasy’s governance and audit trail span more than the MCP layer.
An MCP platform governs the Model Context Protocol layer: the servers, tools, and clients that speak MCP. An AI control plane governs every AI surface on the path between agents and systems, so a single audit trail can correlate the prompt, the identity, the tool call, and the API behavior underneath. Runlayer scopes itself to “internal MCP enablement”; Speakeasy governs the full path.
No. Per Runlayer’s connectors documentation , a connector is a managed MCP server sourced from a catalog, an existing endpoint you point it at, or a custom server you build yourself. Its MCP Builder scaffolds servers from a natural-language prompt, not from an API contract. Speakeasy is OpenAPI-native and generates governed MCP servers directly from your existing API specs.
Yes. Runlayer’s homepage lists SOC 2 Type II, HIPAA, and GDPR. Speakeasy meets all three and adds ISO 27001 certification, so the two products overlap on SOC 2, HIPAA, and GDPR and diverge on ISO 27001, which Runlayer does not list.
Runlayer’s Shadow AI feature uses endpoint agents deployed through MDM tools like Jamf, Intune, Kandji, and Mosyle. Detect runs scheduled scans for shadow MCP servers and skills, and Enforce blocks unmanaged MCP sources and policy-checks local tool calls in real time. This gives device-level reach but adds an MDM dependency and per-OS deployment overhead.
Speakeasy is not cloud-only. It offers three deployment modes: a cloud control plane and gateway, a device agent for managed machines, and plugins embedded directly in the AI clients and agents teams already use, none of which require an MDM. Runlayer, by contrast, leans on MDM-deployed endpoint agents, whose documented strength is off-network discovery of shadow MCP servers in device config files.
When the AI program is squarely MCP enablement of popular SaaS tools, Runlayer’s catalog and approvals are a fast, clean path. When off-network discovery of shadow MCP servers in config files on managed laptops is a top priority and an MDM fleet already exists, Runlayer’s MDM-deployed device scanning is purpose-built for it. Its ToolGuard and AgentGuard models are also strong within the MCP tool layer.
Questions about this comparison, or think we’ve got something wrong? Talk to our team.