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Fiddler AI vs Waxell: Enterprise Observability vs. a Self-Serve Control Plane

#ai #agents #security #fiddlerai

A risk team at a large insurer already runs Fiddler. It has for years — dozens of predictive models under drift monitoring, with the audit artifacts that model-risk governance demands. Then the same organization starts shipping agents: a claims triage workflow, a document-summarization pipeline, a coding assistant rolled out to four hundred engineers. Fiddler's answer is coherent, because it extended the platform it already had. The same console now covers application, session, agent, trace, and span — and its guardrails intercept prompts and responses inline, scored by models that never leave the customer's environment.

Then a platform engineer at a ten-person startup asks a narrower question: my agents call sixty different MCP tools, one of those upstream servers quietly changed its tool definitions last week, and I need to know before an agent trusts the new version. That is a different kind of enforcement — not "is this content safe?" but "is this tool still the tool I approved?"

Both companies call themselves control planes, and both enforce policy before an action completes rather than describing it afterward. The useful question is not who enforces. It's what sits inside each perimeter.

Fiddler AI is an enterprise AI observability and security platform, originally built for predictive ML model monitoring, now positioned as "the control plane for AI agents." It gives hierarchical visibility from application down to span, and enforces policy inline — its guardrails evaluate every prompt and response against your thresholds and intercept violations "before it reaches the user or downstream system," scored in under 80ms by purpose-built Centor Models that run entirely inside your cloud or VPC. Its coding-agent control plane attaches to the LLM/MCP gateway you already operate, with no SDK and no agent rewrites. Waxell is an AI control plane that owns more of the path: it is the MCP Gateway rather than attaching to yours, brokering 160+ upstream connectors with per-tool fingerprinting and drift detection; Observe instruments the agent process itself in two lines of code across 50+ policy categories; Runtime gates each step of high-stakes workflows; and Endpoints reaches down to employee devices. Fiddler adapts to the path you already have. Waxell replaces it.

What is Fiddler AI built for?

Fiddler predates the agentic wave by years, and that history is a genuine asset rather than legacy baggage. It is one of a small number of vendors that can put traditional predictive model monitoring and agentic observability under one roof, which matters enormously in regulated industries where an insurer or a bank governs both a credit-scoring model and an LLM agent, and would prefer not to run two governance programs.

Enforcement is inline and explicitly documented. Fiddler Guardrails is described on its own product page as "the industry's fastest policy enforcement layer for agentic AI": every prompt and response is evaluated against your thresholds for hallucinations, jailbreaks, PII exposure, and unsafe content, "and intercepted before it reaches the user or downstream system." Content Safety Policies block harmful content; Security Policies intercept prompt injection; Privacy Policies cover 35+ PII entity types plus healthcare-specific types for HIPAA. Fiddler's own control-plane page draws the distinction sharply — "flagging is not enforcing" — and positions the platform as observing and enforcing on the same request. Anyone evaluating Fiddler should treat it as an enforcement product, not a dashboard.

The scoring architecture is the strongest technical differentiator. Fiddler's Centor Models — formerly Trust Models — run entirely inside the customer's cloud and VPC, with zero data egress from the customer's environment. No external API calls to a third-party LLM judge, which means no per-check fees stacking up invisibly at production volume, and a claimed sub-80ms latency. Fiddler publishes a TCO calculator built around exactly this argument. Teams that want their own evaluator can bring their own judge instead. This is a serious answer to a real cost problem.

The visibility model is hierarchical, not trace-by-trace. Fiddler's own framing is pointed: it describes itself as "a rich, contextually-aware performance experience that reveals system-wide insights," explicitly "not a one-trace-at-a-time debuggability experience." That means aggregate dashboards down through session, agent, trace, and span, hierarchical root-cause analysis that pinpoints the failing span with surrounding context, and cross-agent dependency mapping.

The coding-agent control plane attaches to infrastructure you already run. Announced June 23, 2026, following Fiddler's April 2026 acquisition of Lumeus, it integrates inline with the LLM/MCP gateway already in place — "no new gateway, no SDKs, no agent rewrites" — catching PII/PHI in a developer's prompt before it reaches the model, and secrets or sensitive data returned by a tool or MCP server before they enter context. For an organization that already standardized on a gateway, that is a low-friction adoption path and a legitimate architectural advantage.

Fiddler raised a $30M Series C in January 2026, led by RPS Ventures, bringing total funding to $100M.

Where the two architectures diverge

Both products enforce. They differ in what they own, and therefore in what they can see and act on.

Fiddler attaches to your gateway; Waxell is the gateway. Fiddler's inline enforcement rides the LLM/MCP gateway you already operate, which is why it needs no SDK and no agent rewrites. Waxell's MCP Gateway is itself the broker — one URL per tenant in front of 160+ upstream connectors. Neither is strictly better. Fiddler's model is faster to adopt if you have a standardized gateway; Waxell's gives it control over the tool catalog itself, which is where the next difference comes from.

Content-level enforcement versus tool-identity enforcement. Fiddler's published guardrail policies govern what flows through the path: safety, prompt injection, PII/PHI, secrets, faithfulness. Waxell adds enforcement on the identity and integrity of the tools themselves — every upstream tool carries a fingerprint with five trust states (Pending, Drift, Trusted, Blocked, Removed), so a server that silently changes its tool definitions is caught as drift rather than trusted by default, and tool descriptions are scanned for prompt injection at fingerprint time, before any agent calls them. Destructive actions can park for human approval. That is a category of control a gateway-attached layer does not surface, because it requires owning the catalog.

Policy surface. Fiddler's guardrails are organized into content safety, security, privacy/compliance, and accuracy policy families, backed by 100+ monitoring metrics. Waxell evaluates 50+ policy categories at execution — including operational ones that sit outside the safety/security frame: hard budget ceilings that stop a run at its limit, rate-limiting, scheduling, delegation, and kill switches at every level. Fiddler surfaces cost and spend analytics broken down by developer, model, and team; Waxell's cost policy is an enforced ceiling rather than a reporting view.

The device layer. Fiddler's answer to ungoverned AI is the gateway; Waxell Endpoints is a native macOS and Windows agent that discovers 60+ AI provider domains on employee machines and brings assistants under policy, including tools that never touch a corporate gateway at all. Different vantage points on the same shadow-AI problem — and if your exposure is a developer running an assistant that bypasses the gateway entirely, only the device layer sees it.

The MCP server itself. Fiddler ships an MCP server, but its documented purpose is exposing Fiddler's own observability data to assistants so an engineer can investigate traces from inside Cursor or Claude Code. Fiddler's documentation labels it "Private Preview — available to select design partners only," with no SLA or SLO and APIs that may change without notice. That is connectivity to Fiddler, not governance of your agents' outbound tool calls — a distinct thing from the inline gateway enforcement above, and a pattern shared across nearly every observability platform in the category.

There is no self-serve path. Fiddler publishes a real per-trace figure — $0.002 per trace on the Developer tier, unusual transparency in a market of custom quotes, and a point in its favor. But the buttons tell the story: Free says "Request demo." Developer says "Request demo." Enterprise says "Contact sales." There is no tier you can turn on yourself.

What Waxell adds

Observe installs with pip install waxell-observe, instruments an agent in two lines of code with no rebuilds, auto-instruments 200+ libraries, and evaluates each run against 50+ policy categories — Audit, Content, Control, Cost, Kill, LLM, Operations, Quality, Rate-Limit, Safety, Scheduling, Compliance, Delegation, Identity, Privacy, Reasoning — at 0.045ms p95 across 1000+ policies. Because the instrumentation is inside the agent process rather than on the network path, it sees reasoning steps and per-run cost whether or not that traffic crosses a gateway. Policy categories map to OWASP LLM Top 10, NIST AI RMF, ISO 42001, EU AI Act, GDPR, and HIPAA.

The MCP Gateway brokers 160+ upstream connectors, redacting PII in flight, blocking secrets so they never leave the gateway, scanning tool descriptions for prompt injection at fingerprint time, and tracking every tool through the five-state trust model. Runtime gates each step of high-risk financial, clinical, and infrastructure workflows, with kill switches at every level and US or EU data residency. Endpoints discovers AI across employee machines — 60+ provider domains, macOS and Windows.

And the entry point is different in kind: free to start, two-line setup, no demo request required.

Feature comparison

Capability Waxell Fiddler AI
Enforcement
Inline, pre-execution enforcement ✅ Yes ✅ Yes ("intercepted before it reaches the user or downstream system")
In-environment scoring, no external judge calls ⚠️ Policy engine runs locally; not marketed as a TCO argument ✅ Yes (Centor Models, zero data egress, <80ms claimed)
Content safety / injection / PII policies ✅ Yes ✅ Yes (35+ PII entities, HIPAA-specific types)
Per-tool fingerprinting + drift detection ✅ Yes (5 trust states) ❌ Not offered
Human-in-the-loop approval on destructive actions ✅ Yes ⚠️ Not stated
Hard budget ceilings that halt execution ✅ Yes ⚠️ Cost and spend analytics by developer/model/team
Kill switches ✅ Yes (every level) ⚠️ Not stated
Policy surface ✅ 50+ categories incl. operational ⚠️ Safety, security, privacy, accuracy families + 100+ metrics
Architecture
Owns the MCP/LLM gateway ✅ Yes (160+ connectors, one URL per tenant) ❌ Attaches inline to the gateway you already run
Requires no new infrastructure ⚠️ Introduces a gateway (self-host option available) ✅ Yes (no new gateway, no SDK, no agent rewrites)
In-process agent instrumentation ✅ Yes (two-line SDK, 200+ libraries) ⚠️ OTel-based; gateway-side capture stitched with agent-side OTel
Coding-agent / IDE governance ⚠️ Via Endpoints device discovery ✅ Yes (announced June 23, 2026; design-partner track open)
Shadow AI on employee devices ✅ Yes (Endpoints, 60+ domains, Mac + Windows) ❌ No (gateway-side only)
Governed execution runtime ✅ Yes (Runtime, early access) ❌ No
Visibility
Full agent traces ✅ Yes ✅ Yes
Hierarchical app → session → agent → trace → span ⚠️ Trace and session level ✅ Yes (core strength)
Predictive ML model monitoring ❌ Not a Waxell product ✅ Yes (original product line)
Pre-production evaluation (golden/challenger datasets) ⚠️ Testing in Runtime ✅ Yes
Vendor MCP server
Governs your agents' outbound MCP tool calls ✅ Yes (MCP Gateway) ❌ No (its MCP server exposes Fiddler's own data; private preview)
Pricing
Self-serve signup ✅ Yes (free to start, two-line setup) ❌ No (every tier routes to "Request demo" / "Contact sales")
Published price figures ✅ Yes (published tiers) ✅ Yes ($0.002/trace, Developer)

Three scenarios, two different perimeters

Scenario: you run predictive ML models and agents side by side under model-risk governance.
Fiddler is the stronger fit. Unifying drift monitoring on credit models with agentic observability and inline guardrails in one platform is something few vendors can do, and Fiddler's years in ML observability are not easily replicated. Waxell is not an ML model monitoring product.

Scenario: your agents call dozens of third-party MCP tools and you need to know when one changes.
Waxell. Owning the gateway is what makes per-tool fingerprinting, five-state trust, and drift detection possible — a server that silently rewrites its tool definitions is flagged rather than trusted. A layer that attaches to your existing gateway governs the data crossing it, not the identity of the catalog behind it.

Scenario: you already standardized on an LLM gateway and want enforcement without touching your agents.
Fiddler. Inline enforcement on the gateway you already run, with no SDK and no agent rewrites, is a genuinely lower-friction path than introducing a new broker. Waxell's equivalent coverage means routing tool traffic through Waxell's gateway or instrumenting agents with the SDK.

When to use Fiddler AI

  • You govern predictive ML and agentic AI together and want one platform, one audit trail, and one vendor across both.
  • You have a standardized LLM/MCP gateway and want inline enforcement attached to it without SDKs or agent changes.
  • In-environment scoring with zero data egress and no external LLM-judge calls matters for cost predictability and for procurement.
  • Hierarchical, system-wide observability across multi-agent systems — root-cause down to the failing span — is a primary need.
  • You are an enterprise buyer with a procurement process, and a demo-gated entry point is normal rather than an obstacle.

When to use Waxell

  • You need governance of the tool catalog itself — fingerprinting, drift detection, and prompt-injection scanning of tool descriptions before any agent calls them.
  • You want operational policy alongside safety policy: hard budget ceilings that halt execution, rate limits, scheduling, and kill switches.
  • Your shadow-AI exposure includes assistants on employee laptops that never cross a corporate gateway.
  • You want in-process instrumentation that sees agent reasoning and per-run cost independent of network path.
  • You want to start free with a two-line setup and prove value before a contract exists.

How Waxell handles this: Waxell is an AI control plane that owns the path rather than attaching to it. The MCP Gateway brokers 160+ upstream connectors behind one URL per tenant, redacting PII in flight, blocking secrets before they leave, and tracking every tool through five trust states so silent drift is caught rather than trusted. Observe instruments the agents you build in two lines of code and enforces 50+ policy categories at 0.045ms p95 — cost ceilings that stop a run, content and safety gates, kill switches — mapped to OWASP LLM Top 10, NIST AI RMF, ISO 42001, EU AI Act, GDPR, and HIPAA. Runtime gates each step of workflows where being wrong is expensive, and Endpoints finds the AI running on employee machines that never touches a gateway at all.

FAQ

Is Waxell a Fiddler AI alternative?

For agentic governance, yes, though the overlap is partial. Both enforce policy inline rather than reporting after the fact. Waxell adds governance of the MCP tool catalog itself — fingerprinting, drift detection, human approval on destructive actions — plus in-process agent instrumentation, an execution runtime, endpoint discovery on employee devices, and a self-serve entry point Fiddler does not offer. Fiddler is the better choice if you also need predictive ML model monitoring in the same platform, or if you want enforcement attached to a gateway you already run without touching your agents.

Does Fiddler AI block agent actions or just monitor them?

It blocks. Fiddler Guardrails is documented as a policy enforcement layer that evaluates every prompt and response against your thresholds and intercepts violations "before it reaches the user or downstream system," with content safety policies that block harmful content and security policies that intercept prompt injection, scored in under 80ms by models running inside your own environment. Fiddler's control-plane material makes the point directly — "flagging is not enforcing." Treat Fiddler as an enforcement product.

What is the main difference between Fiddler AI and Waxell?

What each one owns. Fiddler attaches inline to the LLM/MCP gateway you already operate, which is why it needs no SDK and no agent rewrites, and it governs the content crossing that path. Waxell is the gateway, and also runs inside the agent process and on the employee device — which is what lets it govern the identity and integrity of tools, enforce operational policy like budget ceilings and kill switches, and see agents whose traffic never crosses a gateway.

Can Fiddler AI govern my agents' MCP tool calls?

Partly, and it's worth separating two things. Fiddler's coding-agent control plane enforces inline on traffic through the LLM/MCP gateway you already run, including secrets or PII returned into context by an MCP server. Separately, Fiddler ships its own MCP server — but that one exposes Fiddler's observability data to assistants like Cursor and Claude Code, and its documentation labels it "Private Preview — available to select design partners only." Neither brokers your upstream tool catalog the way Waxell's MCP Gateway does, with per-tool fingerprints and drift states.

What did Fiddler's Lumeus acquisition change?

Fiddler acquired Lumeus in April 2026 to extend its control plane into the code-generation layer — the IDE, CLI, and MCP boundary where coding agents operate. The resulting AI Control Plane for Coding Agents was announced June 23, 2026, and integrates inline with an existing LLM/MCP gateway. Fiddler still runs a design-partner track alongside it. The underlying argument is well evidenced: Fiddler cites research that 28.6 million new hardcoded secrets hit public GitHub in 2025, and that MCP configuration files alone exposed more than 24,000 unique secrets last year.

How does Fiddler AI's pricing compare to Waxell's?

Fiddler publishes a real per-trace figure — $0.002 per trace on the Developer tier — which is more transparent than most enterprise governance vendors. But there is no self-serve path: the Free and Developer tiers both say "Request demo," and Enterprise says "Contact sales." Waxell is free to start with a two-line setup, so a single team can adopt governance before anyone signs anything.

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