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. Hallucination, toxicity, PII/PHI, and jailbreak attempts get scored in real time by models running inside the customer's own environment. One vendor, one governance umbrella, predictive and agentic side by side.

Then a platform engineer at a ten-person startup asks the same question — how do I govern the three agents I shipped last quarter? — and discovers that every tier of Fiddler's pricing page, including the one labeled Free, terminates in "Request demo."

Those two situations describe the axis of this comparison better than any feature list. Fiddler is an enterprise observability and security platform that has repositioned, credibly and with funding behind it, as an AI control plane. Waxell is a control plane that starts at the enforcement layer rather than arriving there from monitoring, and that a single team can turn on without a sales conversation.

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, scores agent inputs and outputs in real time using purpose-built Centor Models that run in the customer's environment, and — following its April 2026 acquisition of Lumeus — is extending policy enforcement backward into the code-generation layer where coding agents operate. Waxell is an AI control plane built enforcement-first: Observe instruments agents in two lines of code and evaluates every run against 50+ policy categories before the next step executes, the MCP Gateway governs tool calls in flight across 160+ upstream connectors, and Runtime gates each step of high-stakes workflows. Fiddler tells you, with real precision, what an agent did and how risky it looked. Waxell decides whether it happens.

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 is governing both a credit-scoring model and an LLM agent, and would prefer not to run two governance programs.

The visibility model is hierarchical, not trace-by-trace. Fiddler's own framing is pointed about this: 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." In practice that means aggregate dashboards down through session, agent, trace, and span, with hierarchical root-cause analysis that pinpoints the failing span with surrounding context, plus cross-agent dependency mapping to surface bottlenecks in multi-agent systems.

The scoring architecture is the strongest technical differentiator. Fiddler's Trust Service runs purpose-built Centor Models — formerly Trust Models — entirely inside 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. Teams that want their own evaluator can bring their own judge instead. This is a real answer to a real cost problem, and it's more architecturally serious than wrapping a frontier model in a guardrail SDK.

The lifecycle is genuinely end-to-end, and getting longer. Development-phase evaluation with golden and challenger datasets, prompt experiments, and edge-case stress testing feeds production monitoring, which feeds back into the next iteration. In April 2026 Fiddler acquired Lumeus specifically to extend the control plane into the IDE, CLI, and MCP boundary where coding agents work — a sharp read of where risk is concentrating. Framework support runs on OpenTelemetry with native integrations for LangGraph, Amazon Bedrock, AWS Strands Agents, and Google ADK.

Fiddler raised a $30M Series C in January 2026, led by RPS Ventures, bringing total funding to $100M. This is a well-capitalized company executing a clear thesis.

Where does Fiddler's scope end?

The limits below are boundaries of a monitoring-rooted platform, not defects.

The enforcement mechanism is not publicly specified. Fiddler's copy uses enforcement language — "enforce runtime guardrails to safeguard operations," "real-time guardrails to detect harmful exposure" — and the Centor Models are described as "enabling real-time monitoring and guardrails." That is stronger than a pure dashboard. But Fiddler's public product pages do not state whether a guardrail synchronously blocks the agent's next action or scores and flags it for alerting, in the way some competitors spell out explicitly. The verbs on the agentic-observability page are overwhelmingly evaluate, monitor, analyze, improve, detect, and alert. A team that needs a hard, documented pre-execution block on a specific tool call should confirm the mechanism directly with Fiddler rather than infer it from marketing copy.

Coding-agent control is a design-partner program, not a shipped capability. The Lumeus-powered "Control Plane for Coding Agents" is explicitly recruiting design partners for "early access… before GA." It is a credible roadmap, announced with a serious acquisition behind it, but a team evaluating today is evaluating an invitation, not a product.

MCP support is connectivity, not governance — and it's in private preview. Fiddler's MCP server exposes Fiddler's own observability data to MCP-compatible 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. It does not govern an agent's outbound MCP tool calls, which is a different problem entirely. This pattern is consistent across nearly every observability platform in the category.

There is no self-serve path. Fiddler publishes real per-trace pricing — $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 actual 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

Waxell starts where Fiddler's language gets careful. Observe installs with pip install waxell-observe, instruments an agent in two lines of code with no rebuilds, auto-instruments 200+ libraries, and then 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. Budget ceilings stop execution when an agent hits them rather than surfacing on the invoice. Policy categories map to OWASP LLM Top 10, NIST AI RMF, ISO 42001, EU AI Act, GDPR, and HIPAA, so the same enforcement produces the attestation an auditor asks for.

Waxell also governs surfaces Fiddler doesn't reach. The MCP Gateway puts one URL per tenant in front of 160+ upstream connectors, redacting PII in flight, blocking secrets so they never leave the gateway, scanning tool descriptions for prompt injection at fingerprint time before any agent calls them, and tracking every tool through a five-state trust model — Pending, Drift, Trusted, Blocked, Removed — so a server that silently changes its definitions is caught as drift. Destructive actions park for a human. Runtime gates each step of high-risk financial, clinical, and infrastructure workflows, with kill switches at every level and US or EU data residency. Waxell Endpoints discovers AI across employee machines — 60+ provider domains, macOS and Windows — which is the same shadow-AI blind spot Fiddler's Lumeus acquisition is aimed at, addressed from the device rather than the IDE.

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

Feature comparison

Three scenarios, two different centers of gravity

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 in one platform is something few vendors can do, and Fiddler's years in ML observability are not easily replicated. Waxell governs the agentic half well but is not an ML model monitoring product.

Scenario: you need an agent stopped, not scored. Waxell. Policies evaluate before the step runs, budgets halt execution at the ceiling, kill switches exist at every level, and the MCP Gateway blocks or holds a tool call in flight. Fiddler's guardrails run in real time and in-environment, but the public documentation does not specify synchronous blocking, so a team with a hard enforcement requirement should get that answer from Fiddler directly.

Scenario: one team wants to govern three agents this week. Waxell. Two lines of code, free to start, no demo request. Fiddler's per-trace pricing is transparent, but every tier — including Free — routes through a sales conversation first.

When to use Fiddler AI

• You govern predictive ML and agentic AI together and want one platform, one audit trail, and one vendor relationship across both.

• Hierarchical, system-wide observability across multi-agent systems — root-cause down to the failing span — is your primary need.

• In-environment scoring with no external LLM-judge calls matters for cost predictability at production volume.

• 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 documented pre-execution enforcement — a policy that stops the action, not a score that describes it.

• You need governance of your agents' own MCP tool calls, with drift detection, secret blocking, and human approval on destructive actions.

• Your shadow-AI exposure is on employee laptops today, not only in the IDE.

• 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 where enforcement is the primitive and observability is what enforcement produces. Observe instruments the agents you build in two lines of code, auto-instrumenting 200+ libraries, and evaluates every run against 50+ policy categories at 0.045ms p95 — cost ceilings that halt execution, PII redaction, content and safety gates, kill switches — mapped to OWASP LLM Top 10, NIST AI RMF, ISO 42001, EU AI Act, GDPR, and HIPAA so the same enforcement yields an audit you can hand over. The MCP Gateway governs the tool calls those agents make across 160+ upstream connectors, with five-state tool fingerprinting that catches silent drift and human-in-the-loop holds on destructive actions. Runtime gates each step of workflows where being wrong is expensive, and Endpoints finds the AI running on employee machines. A dashboard after the fact is not governance. It's an autopsy.

FAQ

Is Waxell a Fiddler AI alternative?

For agentic governance, yes. Waxell covers agent instrumentation, execution-level policy enforcement, MCP tool-call governance, and endpoint shadow-AI discovery, and it does so with 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 — Waxell does not do that. The two products overlap on agentic observability and diverge sharply on enforcement depth and how you buy.

What is the main difference between Fiddler AI and Waxell?

Center of gravity. Fiddler is an observability platform that grew into a control plane, and its strongest capabilities are visibility ones: hierarchical tracing, root-cause analysis, and real-time scoring by models that run in your environment. Waxell is built enforcement-first, so policies gate the action before it runs across 50+ categories, and observability is the record that enforcement leaves behind.

Does Fiddler AI block agent actions or just monitor them?

Fiddler's public product copy says it lets teams "enforce runtime guardrails" and describes real-time guardrails against hallucination, toxicity, PII/PHI, prompt injection, and jailbreak attempts, scored in-environment at a claimed sub-80ms latency. What its public pages do not specify is whether a triggered guardrail synchronously blocks the agent's next action or scores and alerts on it. If hard pre-execution blocking is a requirement, ask Fiddler directly — this comparison does not assert an answer Fiddler has not published.

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 strategic read is sound, and Fiddler cites Snyk research that 80% of developers admit to bypassing security policies when using AI coding tools. The practical caveat is timing: Fiddler is recruiting design partners for early access before general availability, so this is roadmap rather than something a buyer can deploy today.

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.

Can I run Fiddler AI and Waxell together?

Yes, and it's a more natural pairing than most. Their strengths sit in different places: Fiddler's hierarchical multi-agent observability and predictive ML monitoring alongside Waxell's execution-level enforcement and MCP tool-call governance. Both instrument via OpenTelemetry, so the telemetry paths coexist. Teams consolidating to one vendor should decide whether their binding requirement is unified ML-plus-agentic visibility or hard enforcement at the point of action.

Sources

• Fiddler AI, Fiddler Agentic Observability — accessed July 27, 2026

• Fiddler AI, Plans and Pricing — accessed July 27, 2026

• Fiddler AI (Krishna Gade, Satish Veerapuneni), Fiddler Acquires Lumeus to Extend the AI Control Plane to Coding Agents — April 10, 2026

• Fiddler AI, Fiddler Series C: The Control Plane Moment for AI — January 2026

• Fiddler AI, Fiddler MCP Server — Fiddler Documentation, accessed July 27, 2026

Weighing an observability platform and want governance that stops the action instead of describing it? Get started with Waxell.