AI & MCP
In depth: Speakeasy vs Fiddler AI
Nolan Sullivan
- 12 min read
NOTE
This comparison of Speakeasy and Fiddler AI 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 Fiddler, and be honest about where Fiddler is strong. If you think we need to update this post, let us know.
Speakeasy and Fiddler AI both describe what they build as an AI control plane, which makes this comparison necessary. Underneath the shared phrase are two different products.
- Fiddler 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” .
- Speakeasy is a security and governance company. It builds the AI control plane as an enforcement layer: an MCP gateway in the traffic path, real-time threat blocking, policy enforcement, audit logging, and adoption analytics for securing and governing AI usage across an entire organization.
How is Speakeasy different?
1. Speakeasy enforces in the path; Fiddler observes beside it
Both companies use the words “control plane,” so the useful question is what each one can actually control:
- Fiddler’s platform ingests telemetry, evaluates quality, scores risk, and produces audit evidence. Its Guardrails product is a scoring API: the application sends text, receives scores per dimension, and the application’s own code decides whether to block. Fiddler’s quick start documents this directly, with the threshold check living in the developer’s application.
- Speakeasy sits in the traffic path. Its gateway is what the agent’s tool call goes through, so a policy violation is blocked by the plane itself, not by code each application team remembers to write.
The distinction matters most when something has to be stopped. An out-of-band scorer can tell you a response contained PII; a gateway in the path is what keeps that response from leaving the perimeter.
2. Speakeasy governs access; Fiddler evaluates quality and risk
The two products answer different questions:
- Fiddler answers “is our AI behaving well?”: drift, hallucination, safety scores, root cause analysis when an agent misbehaves, and compliance evidence for frameworks like SR 11-7 and the EU AI Act.
- Speakeasy answers “is our AI allowed to do this?”: which employees and agents can reach which tools, under what policy, with what credentials, and with every action attributed to an identity in one audit trail.
Both questions matter to a regulated enterprise, and they are not the same question. Quality evaluation does not grant or revoke access, and access control does not measure hallucination.
3. Speakeasy ships a gateway; Fiddler does not have one
The connection layer is where the product gap is widest:
- Speakeasy’s MCP gateway is the center of the platform: per-team registries, a 50+ server catalog, governed MCP servers generated from your APIs, managed OAuth, and reach into the AI clients employees use.
- Fiddler has no MCP gateway, server registry, or credential management. Its MCP server runs in the opposite direction, exposing Fiddler’s own observability data to assistants like Claude and Cursor. Its April 2026 acquisition of Lumeus.ai markets policy enforcement at the IDE, CLI, and MCP boundary for coding agents, but that capability is weeks old and not yet in Fiddler’s product documentation.
To be honest in the other direction: Speakeasy does not do model evaluation. Drift detection, hallucination scoring, and span-level root cause analysis are Fiddler’s depth, and a team that needs them will not find them in Speakeasy.
The rest of this post compares features as they stand today. Because the products overlap less than the shared vocabulary suggests, several rows are one-sided in each direction.
Evaluating platform capabilities
We evaluate each platform against the core functions of an AI control plane: connect, secure, control, observe.
Connect: a gateway in the traffic path vs integrations beside it
Speakeasy’s connection layer is the product: per-team registries with a catalog of 50+ pre-approved servers, governed and token-efficient MCP servers generated from the API contract your teams already maintain, managed OAuth, and plugins plus a device agent that reach the clients employees use (Claude Code, Claude Desktop, Cursor, ChatGPT).
Fiddler connects differently because its job is different. It ingests telemetry through OpenTelemetry and integrations with frameworks like LangGraph, Amazon Bedrock, AWS Strands, and Google ADK, so it can observe agents wherever they run. It does not stand between an agent and a tool, broker access to MCP servers, or manage the credentials behind them.
Connect
Connection is not the job Fiddler is built for. It integrates beside the traffic to observe it; Speakeasy is the path the traffic takes.
Secure: inline enforcement vs a scoring API
Fiddler Guardrails covers a wide detection surface: safety scoring across 11 dimensions , prompt injection and jailbreak detection, faithfulness scoring for hallucination, and PII detection across 35+ entity types, with sub-100ms latency, a free tier, and deployment into your VPC or air-gapped environment. It is also natively integrated into NVIDIA NeMo Guardrails , and gateway vendors list it as a supported guardrail backend.
The architectural difference is who enforces. Fiddler’s quick start shows the pattern: the application posts text to the Guardrails endpoint, gets scores back, and implements the blocking logic itself. Speakeasy inspects and blocks in the gateway, on every prompt, response, and tool call, whether or not the application team wrote anything. That position also covers threats a scoring API never sees: shadow MCP servers, tool-definition changes, and the agent actions between model calls.
Secure
Fiddler’s detection models cover dimensions Speakeasy doesn’t, hallucination above all. The difference is enforcement: Speakeasy blocks in the path; Fiddler returns scores and leaves the blocking to each application.
Control: policy at the point of use vs policy outcomes on a dashboard
Speakeasy enforces who can use what: role-based permissions at the server, toolset, and individual tool level, access that follows existing roles from your IdP, centrally managed credentials, and human-in-the-loop approval workflows for high-risk actions, with one policy plane across MCP, agents, and assistants.
Fiddler’s control story is governance in the model-risk sense. Its GRC offering covers audit trails across decisions and policy outcomes, compliance reporting, bias monitoring, and human-in-the-loop approvals for high-risk decisions. Its RBAC and SSO govern access to the Fiddler platform itself, not which employees or agents can reach which tools. The Lumeus acquisition points at tool-level enforcement for coding agents, but as of this writing it has no product documentation.
Control
Fiddler’s governance is the model-risk-management kind: evidence, reporting, and review. Speakeasy’s is the operational kind: the rule is evaluated on the request and the request passes or doesn’t.
Observe: usage and audit vs traces and evaluations
Observability is Fiddler’s home turf, and its depth here exceeds Speakeasy’s. Its agentic observability , GA since October 2025, traces hierarchically from application to session to agent to span, runs root cause analysis to find the failing step, and applies 100+ built-in and custom metrics, with evaluation served by its in-environment Centor Models. A team debugging why an agent fails will get more from Fiddler than from Speakeasy.
Speakeasy’s observability serves a different reader. It measures the organization: adoption analytics by team, employee, and assistant, cost-by-model breakdowns, and an audit trail that ties the prompt, the identity, the tool call, and the API behavior underneath together, queryable directly or exported to your SIEM. Fiddler’s audit evidence is about agent and model behavior; attribution of usage to employees and teams across the organization is not what it measures.
Observe
Fiddler observes the agent; Speakeasy observes the organization. A debugging team wants the former, leadership and security want the latter, and neither substitutes for the other.
Architecture and delivery: in the path vs out of band
Speakeasy deploys as a cloud control plane and gateway, a device agent for managed machines, and plugins embedded in the AI clients teams already use, so governance reaches AI usage that never touches a server-side gateway.
Fiddler deploys as SaaS, in your VPC, or on-premise per its pricing page , with Guardrails deployable air-gapped and a public-sector channel through Carahsoft. Pricing is published: a free Guardrails tier and a Developer tier at $0.002 per trace. Because it sits out of band, it has no presence in clients or on devices; the Lumeus acquisition brings IDE and CLI presence for coding agents specifically.
Architecture and delivery
The architectural positions are complementary rather than competing: one product is the path, the other watches from beside it.
Enterprise readiness and track record
Both companies hold SOC 2 Type II, per Fiddler’s security page , and both meet HIPAA. Speakeasy adds ISO 27001 certification, which Fiddler does not list. Both support SSO through Okta, Entra, Google, and Ping.
Fiddler is an established company in its own category. Founded in 2018 by ex-Facebook engineering lead Krishna Gade, it has raised $100M including a $30M Series C in January 2026, lists customers including Nielsen, the U.S. Navy, and Integral Ad Science, and appears in the Gartner Market Guide for AI Evaluation and Observability Platforms. That record is in observability and evaluation. The control plane positioning dates to the Series C announcement, and the enforcement capability it implies rests on an acquisition that closed in April 2026 and has not yet reached the docs.
Enterprise readiness and track record
Compliance is close to even apart from ISO 27001. As with the track record, the difference is domain: Fiddler is proven at watching AI systems; enforcing on them is the newest and least documented part of its story.
When to choose Speakeasy vs Fiddler
Choose Speakeasy if the job is securing and governing AI usage: standing between agents and systems with inline enforcement, scoping tool access to identity, managing credentials, approving high-risk actions, turning your APIs into governed tools, and measuring adoption across the organization.
Choose Fiddler if the job is evaluating and debugging AI behavior: span-level tracing and root cause analysis for agents, hallucination and safety scoring, drift detection, and model-risk compliance evidence for regulated industries. Its Guardrails free tier and air-gapped deployment lower the barrier for both.
Choose both if you need both jobs done. The architectures don’t conflict: Fiddler is the kind of scoring service a gateway calls, and gateway vendors already list Fiddler as a guardrail integration . An enterprise can govern access through Speakeasy and evaluate quality through Fiddler without either displacing the other.
Recommendations by team type
Best fit by team
The bottom line
Fiddler is a capable AI observability and evaluation company. Within that scope it goes deep: hierarchical agent tracing, root cause analysis, hallucination and safety scoring, drift monitoring, and the compliance evidence regulated industries ask for, deployable down to air-gapped environments.
Speakeasy is a security and governance company. The control plane sits in the traffic path, so policy is enforced by the plane rather than scored and handed back to application code, access is scoped to identity at the tool level, credentials are managed centrally, and the audit trail attributes every action to a person or agent across the organization.
The shared phrase hides the difference: Fiddler’s control plane watches and scores; Speakeasy’s decides what passes. If you’re evaluating quality, buy the watcher. If you’re governing usage, that’s the AI control plane we’ve built, and the evaluation checklist shows the difference row by row.
Fiddler AI is an AI observability and evaluation platform: agent tracing, root cause analysis, hallucination and safety scoring, drift monitoring, and model-risk compliance evidence. Speakeasy is an AI control plane for security and governance: an MCP gateway in the traffic path, inline policy enforcement and threat blocking, tool-level access control, credential management, and adoption analytics. Fiddler measures how AI behaves; Speakeasy governs what AI is allowed to do.
Fiddler repositioned as the “AI Control Plane for Enterprise Agents” alongside its January 2026 Series C. Its platform is observability, evaluation, guardrail scoring, and audit, which controls through visibility and evidence rather than enforcement in the traffic path. An AI control plane in the sense Speakeasy builds one is the layer traffic flows through, where policy is evaluated and enforced on every request.
No. Fiddler’s docs contain no product that proxies or governs MCP traffic. Its MCP server works in the opposite direction, exposing Fiddler’s observability data to assistants like Claude and Cursor. Following its April 2026 acquisition of Lumeus.ai, Fiddler markets policy enforcement at the IDE, CLI, and MCP boundary for coding agents, but that capability is not yet in its product documentation.
Not by themselves. Fiddler Guardrails is a scoring API: per its quick start , the application sends text to the endpoint, receives scores across dimensions like safety, faithfulness, and PII, and implements the blocking logic in its own code. Speakeasy enforces in the gateway, so blocking applies to every prompt, response, and tool call without each application team writing enforcement code.
No. Drift detection, hallucination and faithfulness scoring, and span-level root cause analysis are Fiddler’s domain, and Speakeasy does not offer them. Speakeasy’s security layer detects and blocks PII leakage, prompt injection, shadow MCP servers, and tool poisoning in the traffic path. Teams that need both access governance and quality evaluation can run both products together.
Fiddler holds SOC 2 Type II per its security page and is HIPAA compliant . It does not list ISO 27001. Speakeasy meets SOC 2 Type II and HIPAA and adds ISO 27001 certification.
Yes, and the architectures suit it. Fiddler is built to be called for scores and to ingest telemetry from systems it doesn’t sit inside, which is why gateway vendors list it as a guardrail integration . An enterprise can route and govern AI usage through Speakeasy while sending traces to Fiddler for evaluation, drift monitoring, and model-risk reporting.
Questions about this comparison, or think we’ve got something wrong? Talk to our team.