AI & MCP
The best AI governance platforms in 2026
Nolan Sullivan
June 17, 2026 - 11 min read
NOTE
This roundup reflects the state of each product as of June 2026. AI governance is a fast-moving category and these 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 show our work, link to primary sources and to our in-depth comparison of each platform, and are honest about where every tool here is strong. If you think we’ve got something wrong, let us know.
AI governance moved from a deferred concern to an infrastructure problem in 2026. Gartner expects spending on AI governance platforms to pass $1 billion by 2030 , and reports that organizations running one are 3.4 times more likely to rate their governance program effective. The category filled with vendors fast, and the label “AI governance platform” now covers products that do very different things.
This roundup compares five of the platforms enterprises evaluate most often: Speakeasy, Runlayer, MintMCP, TrueFoundry, and Fiddler AI. The goal is to make the differences concrete, because the deciding question is rarely which product checks the most boxes. It is which job you are hiring the platform to do.
How we evaluated AI governance platforms
The platforms here are sorted by what they govern and where they sit relative to AI traffic, against the four core functions of an AI control plane:
- Connect: how the platform brings AI to the systems and tools it needs, from a catalog of pre-approved servers to building governed connectors from your own APIs.
- Secure: what it inspects and blocks in real time, from PII and credential leaks to prompt injection and tool poisoning.
- Control: how it scopes access to identity, applies policy, and approves high-risk actions.
- Observe: what it records, how the audit trail is structured, and whether it exports to the SIEM and warehouse the security and compliance teams already run.
One structural question cuts across all four: does the platform sit on the request path, where it can enforce policy before an action executes, or does it watch from the side and report after? Only an on-path platform can block. We cover why that distinction decides whether governance works in what is an AI governance platform.
How the platforms compare on features
The matrix below maps each platform against the capabilities that separate them. A ✅ means the capability is native and documented, ⚠️ means it is partial or conditional, and ❌ means it is absent. The profiles that follow explain each row in context.
Feature comparison
1. Speakeasy: the AI control plane
Speakeasy is an all-in-one AI control plane for governing and enabling safe AI usage across an organization. It sits in the traffic path, so policy is enforced by the plane itself rather than scored and handed back to application code. In addition to an MCP gateway, it ships real-time threat detection, policy enforcement, audit logging, identity, and adoption analytics, covering employees using Claude, ChatGPT, Cursor, and Copilot, plus the agents and assistants built on company data.
Two things separate it from the MCP-scoped tools below. It governs more than MCP, applying one identity, policy, and audit model across every AI surface. And it builds governed connectors directly from the API contracts your teams already maintain, where the most valuable internal data actually lives, keeping them token-efficient through dynamic toolsets so an agent loads only the tools a task needs.
Speakeasy is also 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, and the control plane builds on that foundation. It holds SOC 2 Type II, ISO 27001, HIPAA, and GDPR.
Where it fits: security and CISO teams governing all AI surfaces, platform teams turning internal APIs into governed tools, and leadership that wants adoption and cost analytics alongside enforcement.
Where it stops: Speakeasy does not ship a traditional LLM gateway today, so it does not route application traffic across model providers with failover and caching. It also does not do model evaluation: drift detection and hallucination scoring belong to a platform like Fiddler.
2. Runlayer: a security-forward MCP gateway
Runlayer is a tools gateway that creates a golden path for tool use by securing MCP and skills usage across an organization. Its documentation calls it “a unified control plane for internal MCP enablement,” and inside that MCP boundary it is strong.
Its security posture rests on two ML systems. ToolGuard scans tool definitions at registration, tool outputs in real time, and requested versus actual behavior, blocking by default. AgentGuard adds session-trajectory analysis to catch reasoning pivots a single-call scanner misses. Its policy engine combines principals, scope, and runtime conditions with session isolation, and its Shadow AI feature reaches off-network laptops through MDM tools like Jamf, Intune, and Kandji.
Where it fits: teams whose AI program is squarely MCP enablement of popular SaaS tools, and orgs that already run an MDM fleet and want device-level shadow-AI control.
Where it stops: Runlayer scopes itself to the MCP tool layer rather than every AI surface, and it does not build MCP servers from your API contracts or list ISO 27001. As a newer entrant, it has a shorter production track record. See the full breakdown in Speakeasy vs Runlayer.
3. MintMCP: a managed MCP gateway with agent monitoring
MintMCP is an enterprise MCP gateway and agent monitor. Its documentation describes it as “an enterprise gateway for Model Context Protocol (MCP)” that “sits between your AI clients and MCP servers.” It hosts MCP servers, exposes them to teams by role, and logs tool calls.
Its strength is managed hosting that removes operational work. Per its quickstart , MintMCP “offers 50+ managed connectors ready to deploy, or you can bring your own MCP servers,” and its MCP Store gives employees one-click access after SSO. Role-based virtual servers expose only the tools each team needs, and its Agent Monitor captures file reads, command execution, and MCP tool calls in coding agents like Claude and Cursor.
Where it fits: teams that want a fully managed MCP gateway with self-serve onboarding, and teams whose primary use case is monitoring coding agents.
Where it stops: MintMCP focuses on MCP and the agents that call it rather than the full AI path, has no documented path for building MCP servers from API contracts, and does not list ISO 27001 or GDPR. See Speakeasy vs MintMCP for the detail.
4. TrueFoundry: an AI gateway built for routing
TrueFoundry is a gateway company. Its flagship AI gateway routes application traffic across more than 250 LLMs with load balancing, failover, semantic caching, and cost controls, at a vendor-stated sub-3ms internal latency. An MCP gateway and an Agent Gateway extend that same routing infrastructure.
Its core job is moving model traffic reliably, and security and governance attach to that pipe. Its guardrails framework ships built-in checks and integrates external guardrail vendors, its access model goes deep with RBAC, ABAC, and Cedar and OPA policy support, and its Kubernetes-native stack adds model serving and fully self-hosted, even air-gapped, deployment.
Where it fits: ML platform teams routing high-volume model traffic across providers, and teams that self-host models on Kubernetes and want serving, fine-tuning, and the gateway from one vendor.
Where it stops: governance is the newest part of TrueFoundry’s portfolio. Detection runs through orchestrated third-party guardrails rather than native enforcement, and audit logs are gated to the Enterprise tier, per its pricing page . It also has no presence on the device or in the client, so AI usage that does not route through its gateway is invisible to it. See Speakeasy vs TrueFoundry.
5. Fiddler AI: AI observability and evaluation
Fiddler AI is an AI observability and evaluation company. Its heritage is ML model monitoring, and today it spans agent tracing , evaluations, guardrail scoring , and model risk governance, repositioned in January 2026 as the “AI Control Plane for Enterprise Agents” .
Observability is its home turf. It traces hierarchically from application to session to agent to span, runs root cause analysis to find the failing step, and applies 100+ quality metrics. Its Guardrails product scores safety across 11 dimensions , prompt injection, faithfulness for hallucination, and PII across 35+ entity types, with a free tier and air-gapped deployment. Its GRC offering produces model-risk evidence for frameworks like SR 11-7 and the EU AI Act.
Where it fits: ML and AI engineering teams debugging why agents fail, and model risk management functions in regulated industries.
Where it stops: Fiddler observes from beside the traffic rather than enforcing in it. Its Guardrails product is a scoring API, so the application’s own code decides whether to block, per its quick start . It has no MCP gateway, server registry, or credential management, and its RBAC governs access to the Fiddler platform, not which agents can reach which tools. See Speakeasy vs Fiddler AI.
How to choose an AI governance platform
The five platforms cluster into three jobs, and the right choice follows from which one you are solving:
- Govern all AI usage from one plane. If the job is securing and governing every AI surface, with inline enforcement, tool-level access tied to identity, and one audit trail across agents, assistants, MCP, and the APIs underneath, that is the AI control plane job, and it is the one Speakeasy is built for. Runlayer and MintMCP solve the MCP-scoped version of it well.
- Move model traffic reliably. If the immediate problem is routing application requests across many model providers with failover, caching, and cost controls, TrueFoundry is built for that and Speakeasy does not ship an LLM gateway today.
- Evaluate and debug AI behavior. If the job is span-level tracing, hallucination and drift scoring, and model-risk evidence, Fiddler goes deep where the others do not.
These jobs are not mutually exclusive. A common pattern is to govern access through an AI control plane and send traces to an evaluation platform for quality scoring, with neither displacing the other.
Summary: when to use each platform
When to use each platform
The bottom line
Every platform here is strong inside its scope. Runlayer and MintMCP secure the MCP layer, TrueFoundry moves model traffic, and Fiddler evaluates how AI behaves. The distinction that matters for governance is whether the platform sits on the request path, because that is the only place policy can be enforced rather than recorded after the fact.
Speakeasy governs every AI surface from one plane on that path, builds governed connectors from your API contracts instead of brokering the ones that already exist, and brings ISO 27001 alongside SOC 2 and hands-on integration engineering to the rollout. If you are governing everything your AI touches rather than the MCP layer alone, that is the AI control plane we’ve built.
An AI governance platform is the software an enterprise uses to control what its AI is allowed to do: connecting agents to the systems they need, enforcing policy on every prompt and tool call, and producing the audit record that proves it. The platforms that work combine AI enablement and AI security on one path. The ones that only document governance from the sidelines do not. We cover the category in depth in what is an AI governance platform.
The leading platforms enterprises evaluate include Speakeasy (an AI control plane governing every AI surface), Runlayer and MintMCP (MCP-focused gateways), TrueFoundry (an AI gateway built for routing model traffic), and Fiddler AI (AI observability and evaluation). The best choice depends on the job: governing all AI usage, routing model traffic, or evaluating AI behavior.
An AI gateway routes application traffic across model providers. An MCP gateway 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. We explore the distinction in AI gateway vs MCP gateway vs AI control plane.
To enforce policy, yes. A platform that sits on the path between agents and the systems they reach can block a violating tool call before it executes. A platform that observes from the side can only record that it happened after the fact. This is the structural difference between platforms that enforce governance and platforms that document it.
Yes, and the architectures often suit it. An enterprise can govern access through an AI control plane like Speakeasy while sending traces to an evaluation platform like Fiddler for drift monitoring and model-risk reporting. Gateway and control-plane vendors commonly integrate scoring services as guardrail backends.
Questions about this roundup, or think we’ve got something wrong? Talk to our team.