fiddler.ai

Fiddler AI is an enterprise AI control plane: a single system for monitoring and evaluating AI agents, enforcing guardrails inline, and producing auditable governance records across the agent lifecycle. It is aimed at organizations running agents in production that need visibility into what agents do, control over what they are allowed to do, and evidence that those controls worked. It is not a model builder or a general-purpose LLM API; it sits around your agents and models as an operational and governance layer.

What the control plane actually covers

Fiddler describes its scope as "visibility, context, and control" delivered through evaluation, monitoring, enforcement, governance, and cost efficiency. In practice that breaks into four working areas:

• Monitoring and evaluation. A single view of agent performance across a fleet, with 100+ pre-defined metrics plus custom metrics. The stated goal is answering "are my agents working?" at the level of every action, token, and dollar, broken down by user and team.
• Inline enforcement. Guardrails and evaluations run on the agent's request and response path, so PII, PHI, and secrets are detected and redacted on the same request — before a prompt reaches the model and before a response reaches the developer.
• Auditable governance. Every enforcement decision is logged with who triggered it and what happened, producing audit evidence intended to map to GRC standards and AI regulations.
• Cost control. Evaluation runs in-environment using Fiddler's Centor Models rather than calling an external LLM API, which the company says can reduce evaluation total cost of ownership by up to 98% versus using foundation models.

The mechanism that matters most: in-environment evaluation

The design choice worth understanding is where evaluation happens. Many teams evaluate agent outputs by shipping prompts and responses to a hosted foundation model and scoring the result. That approach adds per-call cost, adds latency, and means sensitive content leaves your network.

Fiddler's alternative is to run guardrails and evaluations inside the environment using its own Centor Models, with no external LLM API calls. The company reports under 80ms in-environment enforcement latency, 99% precision blocking jailbreaks in production, and zero data leaving the network. Those are vendor-reported figures, so treat them as claims to validate in a proof of concept rather than settled benchmarks — but the architectural difference is real and testable: you can check whether evaluation traffic stays inside your VPC and whether latency fits your request budget.

A concrete example of why placement matters: a coding agent that reads repository files and writes code suggestions sits directly on the path where secrets and customer data appear. If redaction happens after the response is generated but before it reaches the developer, the same request can be filtered without a round trip to an outside service. That is the scenario Fiddler uses to illustrate enforcement on coding agents.

Who it is for

Fiddler frames its users as two groups: AI builders, who need to see whether agents are performing and debug them, and AI guardians — security, risk, and compliance functions — who need enforcement decisions recorded and defensible. The buyer's guide and control plane guide on the site are written for that joint audience, which is a signal about how the product is sold: as shared infrastructure between engineering and governance rather than a point tool for one team.

Deployment options support that positioning: SaaS, VPC, and AWS GovCloud, with Fiddler AI Observability runnable inside Amazon SageMaker Studio as an AWS Preferred Partner. The GovCloud option and the governance framing indicate government and regulated enterprise as target segments.

How to decide whether it fits

Use these conditions to judge fit rather than a feature checklist:

The vendor cites a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient governance-platform runtime enforcement. Whatever weight you give the forecast, it points at the distinction that separates Fiddler from observability-only tools: enforcement at runtime versus analysis after the fact.

Practical next steps

1. Read the control plane guide and buyer's guide on fiddler.ai — they define the category and the evaluation criteria the vendor expects buyers to use.
2. Request a demo and bring one real agent, including its worst-case data (PII, secrets, prompt-injection attempts).
3. Verify the three claims that carry the most risk for you: enforcement latency against your request budget, whether any data leaves your network in your deployment mode, and whether audit records satisfy your specific GRC framework.
4. Check pricing directly at fiddler.ai/pricing, since plan structure and eligibility depend on deployment mode and scale; the public materials do not state terms.

The main caveat: performance, precision, and cost-reduction figures come from Fiddler's own materials. Confirm them against your traffic and data before committing, and confirm which capabilities are included in the plan you would actually buy.

Fiddler AI protects sensitive data by enforcing detection and redaction inline, on the same request and response path that an AI agent uses. Before a prompt reaches the model, Fiddler checks it for personally identifiable information (PII), protected health information (PHI), and secrets, and redacts what it finds. It repeats that check on the way back, before a response reaches the developer or end user. Because enforcement runs in-environment, the data being inspected does not leave your network. This applies to organizations running agents in Fiddler's control plane across SaaS, VPC, or AWS GovCloud deployments.

Where enforcement sits in the request path

The key design choice is that Fiddler enforces inline rather than as a separate scanning job that runs after the fact. The request path looks like this:

1. An agent or application sends a prompt.
2. Fiddler inspects the prompt and detects PII, PHI, or secrets.
3. Detected items are redacted before the prompt is passed to the model.
4. The model returns a response.
5. Fiddler inspects the response and redacts sensitive content before it reaches the developer.

The practical effect is that the model never sees the raw sensitive values in the prompt, and the developer never receives them in the response. Both directions are covered by the same enforcement layer, so a leak introduced by the model's own output is caught as well as one introduced by user input.

Why in-environment matters

Fiddler states that enforcement runs in-environment with no external LLM API calls, and that zero data leaves your network. For PII and PHI specifically, this changes the compliance picture: the sensitive content being scanned is not shipped to a third-party evaluation service. Fiddler also cites an in-environment enforcement latency of under 80ms, which is the relevant number when deciding whether inline checks are tolerable on a live request path.

What gets detected and what gets recorded

The enforcement layer targets three categories: PII, PHI, and secrets. Fiddler reports 99% precision in blocking jailbreaks in production, which speaks to the detection quality of the guardrail layer generally rather than to PII recall specifically; treat the two as related but distinct claims.

Every enforcement decision is logged with who triggered it and what happened. That record is what makes the protection auditable: it produces evidence suitable for GRC standards and AI regulations, rather than leaving redaction as an invisible side effect. If your question is "can we show an auditor that sensitive data was handled correctly," the audit trail is the part that answers it.

How this fits the broader control plane

PII/PHI redaction is one function inside Fiddler's control plane, which also covers monitoring and evaluations, guardrails, governance, and cost visibility across a fleet of agents. Two connections are worth noting:

• Cost: Fiddler's Centor Models power guardrails and evaluations in-environment, and Fiddler claims this reduces evaluation total cost of ownership by up to 98% compared with using foundation models. The same in-environment design that keeps data inside the network is what avoids per-call external model costs.
• Governance: because enforcement decisions are recorded, the redaction layer feeds the same audit evidence used for broader AI governance.

Fiddler positions runtime enforcement as increasingly important, citing a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient governance-platform runtime enforcement.

Choosing whether this meets your requirement

Use the following conditions to decide:

If your need is purely post-hoc scanning of stored logs, the inline design is more than you need. If your need is preventing sensitive data from crossing the model boundary in the first place, inline enforcement is the relevant capability, and the in-environment constraint is the one to verify against your own network and residency requirements.

For current plan details and any limits on deployment options, check Fiddler's pricing page directly; the public materials describe the enforcement mechanism but do not specify plan-level terms.

An AI control plane gives an enterprise one place to see, constrain, and audit every AI agent it runs. Fiddler AI describes its product as "the control plane for enterprise AI agents," combining monitoring and evaluations, inline enforcement, and governance in a single system. It fits teams that have moved past a single prototype and now need fleet-wide visibility, runtime guardrails, and audit evidence across many agents and models.

The three jobs of a control plane

1. Central visibility across all agents

A control plane consolidates agent activity into one view. Fiddler describes a "single pane of glass" that shows the performance of all agents, with every action, token, and dollar attributable by user and team. That matters once agents are spread across teams: without a shared view, cost and behavior are only visible in each team's own tooling.

2. Inline enforcement on the request and response path

Enforcement happens where the request is made, not in a separate review step. Fiddler states its enforcement sits on the agent's request and response path and detects and redacts PII, PHI, and secrets on the same request — before a prompt reaches the model and before a response reaches the developer. The site cites 99% precision blocking jailbreaks in production and under 80ms in-environment enforcement latency.

A concrete example: a coding agent that pastes a customer record into a prompt. Inline enforcement redacts the sensitive fields before the model sees them, rather than flagging the incident afterward.

3. Auditable governance

Every enforcement decision is recorded with who triggered it and what happened, producing audit evidence the vendor says is ready for GRC standards and AI regulations. This is the difference between a guardrail that blocks something and a guardrail you can defend in an audit.

How Fiddler implements it

The cost claim is worth reading carefully: the savings come from running evaluations with in-environment models instead of calling external foundation-model APIs. If your evaluations depend on a specific external model's judgment, that trade-off needs its own assessment.

When a control plane is the right investment

A control plane earns its place when several of these are true:

• You run multiple agents or models across teams and cannot see their combined cost and behavior.
• You handle regulated or sensitive data and need redaction before the model, not after.
• You need audit evidence tied to specific enforcement decisions.
• You want to keep evaluation data inside your own network.

It is likely premature if you have one agent in pilot and no governance requirement yet — monitoring alone may cover you at that stage.

Questions to settle before choosing

• Where does enforcement run? Confirm it is inline on the request path, not a post-hoc log review.
• What does the audit trail contain? Ask for a sample record showing who triggered a decision and what happened.
• How are evaluations priced at scale? The 98% TCO figure depends on replacing external model calls; model your own volume.
• Which deployment fits your compliance posture? SaaS, VPC, and GovCloud are listed; confirm which applies to your data.

Fiddler's own framing cites a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient governance-platform runtime enforcement. Whether or not that timeline holds, the underlying point is the one to design around: visibility without enforcement and auditability is monitoring, not control.

Fiddler AI is an enterprise control plane for AI agents and models, combining monitoring, evaluation, runtime enforcement, and governance in one system. It is worth evaluating if your organization runs agents in production or at scale and needs visibility into cost and behavior, inline protection of sensitive data, and audit-ready governance records. The main evaluation questions are deployment fit (SaaS, VPC, or AWS GovCloud), how deeply you rely on AWS and SageMaker, and whether its in-environment evaluation model lowers your cost enough to justify switching. Pricing is not published in the available material, so treat cost as a demo-stage question.

What Fiddler AI actually covers

Fiddler positions itself as a control plane rather than a single-purpose monitoring tool. The core capabilities described on its site are:

• Fleet-wide visibility: a single view of every agent's performance, with actions, tokens, and dollars tracked by user and team.
• Inline enforcement: detection and redaction of PII, PHI, and secrets on the same request, both before a prompt reaches the model and before a response reaches the developer.
• Auditable governance: every enforcement decision is logged with who triggered it and what happened, framed as evidence for GRC standards and AI regulations.
• Evaluation and guardrails: powered by Fiddler's Centor Models, run in-environment without external LLM API calls.
• Metrics coverage: 100+ pre-defined metrics plus custom metrics.

The vendor cites 99% precision blocking jailbreaks in production and under 80ms in-environment enforcement latency. These are vendor-reported figures; ask for validation against your own traffic during a trial.

Deployment and integration considerations

Deployment options are the first filter, because they determine whether procurement and security review can move quickly.

Fiddler is an AWS Preferred Partner and supports running its observability within Amazon SageMaker Studio. If your ML stack is already on SageMaker, that reduces integration work; if you run multi-cloud or on-premises, ask specifically which capabilities follow you outside AWS.

A practical check: the inline enforcement claim depends on sitting on the agent's request and response path. Confirm with your architects whether that means a proxy, SDK, or gateway pattern in your environment, and what happens to latency and availability if the control plane is degraded.

Cost efficiency: the Centor Models argument

Fiddler's cost pitch is that Centor Models run guardrails and evaluations in-environment with no external LLM API calls, which the vendor says reduces evaluation total cost of ownership by up to 98% compared to using foundation models. The mechanism is straightforward: instead of paying per-token API fees to a frontier model for every evaluation and guardrail check, evaluation runs locally on smaller purpose-built models.

For example, a team evaluating every agent response with a foundation-model judge pays for input and output tokens on each call, and that bill scales with traffic. Moving those checks in-environment changes the cost structure from per-call API spend to infrastructure you already control.

Two caveats to test: the 98% figure is a best-case comparison and depends on your current evaluation volume and model choice, and in-environment evaluation shifts cost to compute you must size and operate. Ask for a TCO walkthrough using your own traffic estimates rather than accepting the headline number.

Governance and compliance fit

The governance case rests on audit evidence: each enforcement decision records the trigger and outcome. That is useful for GRC teams preparing for AI regulations, but "audit-ready" is a claim to verify, not assume. During evaluation, ask for a sample audit export and check whether it maps to the frameworks your compliance team actually reports against. Fiddler is named in Forrester's Agentic Control Plane and Responsible AI landscapes and Gartner's market guide for AI evaluation and observability platforms; these are analyst listings, not certifications.

The vendor also cites a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient runtime enforcement in governance platforms. Treat that as context for why runtime enforcement matters, not as evidence about any specific product.

A short evaluation checklist

1. Scope the pilot: pick one production or near-production agent and define success metrics (blocked jailbreaks, PII leakage rate, evaluation cost per 1,000 requests).
2. Confirm deployment: choose SaaS, VPC, or GovCloud and get security review started early, since this is usually the longest step.
3. Test enforcement inline: measure latency and behavior under load, including failure modes.
4. Run the TCO comparison: model evaluation costs with Centor Models against your current foundation-model judge spend.
5. Request audit samples: validate that governance records satisfy your compliance reporting.
6. Check support terms: the vendor emphasizes white-glove support; confirm what that includes at your tier.

When Fiddler AI is a strong fit — and when it isn't

It fits well when you run multiple agents in production, need both observability and runtime enforcement rather than separate tools, operate primarily on AWS, and have a compliance team that needs decision-level audit trails. It is a weaker fit if you need a lightweight monitoring tool for a single model, if your stack is heavily multi-cloud and you need uniform coverage everywhere, or if budget approval depends on published list pricing you can compare before a sales conversation.

Fiddler AI supports AI governance and compliance by combining runtime enforcement with an audit trail: it inspects agent requests and responses inline, blocks or redacts sensitive data before it moves, and records every enforcement decision with who triggered it and what happened. That record is positioned as audit evidence for GRC standards and AI regulations. The approach fits enterprises and government teams running AI agents in production that need provable controls, not just after-the-fact dashboards.

Where Governance Actually Happens: The Request and Response Path

Fiddler describes itself as a control plane for enterprise AI agents, with enforcement sitting inline on the agent's request and response path. In practice, that means checks run at two points:

• Before a prompt reaches the model — detecting and redacting PII, PHI, and secrets on the same request.
• Before a response reaches the developer — applying the same detection and redaction on the way out.

Because enforcement is in-environment, sensitive content is handled without routing data to an external LLM API for evaluation. Fiddler states zero data leaves your network under this model, and cites in-environment enforcement latency of under 80ms alongside 99% precision in blocking jailbreaks in production.

Why inline matters for compliance

Post-hoc monitoring can tell you an agent leaked data; it cannot prevent the leak. Inline enforcement is what turns a policy into a control. The same mechanism also produces the evidence trail, since each block or redaction is a logged event rather than an inference drawn later from logs.

The Audit Trail: What Gets Recorded

Every enforcement decision is recorded with two attributes: who triggered it and what happened. This is the core of Fiddler's compliance story — the output is a decision log tied to a specific actor and outcome, which the company positions as audit evidence ready for GRC standards and AI regulations.

For a governance or risk team, the practical test is whether that log answers the questions an auditor asks: which agent, which user or team, which policy fired, and what the system did in response. Fiddler's framing covers actor and outcome; teams should confirm during evaluation that the log export maps to their specific framework's evidence requirements.

Fleet-Level Visibility as a Governance Input

Governance also depends on knowing what agents are doing across the organization. Fiddler presents a single-pane view of all agents, showing every action, token, and dollar by user and team. Two governance-adjacent uses follow:

• Cost accountability — per-user and per-team spend attribution supports chargeback and budget controls.
• Coverage — a fleet view helps identify agents that are running without enforcement or evaluation attached.

Fiddler also offers 100+ pre-defined metrics plus custom metrics, which is where evaluation and monitoring feed the broader governance picture.

Runtime Enforcement and Deployment Failure

Fiddler cites a Gartner prediction that by 2030, 50% of AI agent deployment failures will stem from insufficient governance-platform runtime enforcement. The implication for compliance teams is that governance designed as a review process — policies, documentation, periodic audits — does not cover the moment an agent acts. Fiddler's argument is that the control has to execute at runtime, on the request path, and leave a record when it does.

Deployment and Security Context

Governance requirements often dictate where a tool can run. Fiddler states it is built for enterprise and government security and scale, available in SaaS, VPC, and AWS GovCloud, and runs as an AWS Preferred Partner within Amazon SageMaker Studio for observability. For regulated environments, the VPC and GovCloud options are the relevant conditions to check against your own data-residency rules.

How to Evaluate Fit

Use these as verification steps in a demo or trial rather than assumptions:

1. Map the enforcement points. Confirm that both pre-model and pre-response inspection are configurable for your agent traffic, and that redaction (not just detection) is available for the data classes you handle.
2. Test the decision log against your framework. Take one real audit question from your GRC standard and check whether the recorded actor-and-outcome data answers it without manual reconstruction.
3. Verify the data path. Ask specifically what leaves your environment during evaluation and enforcement, and confirm the answer holds for your chosen deployment (SaaS, VPC, or GovCloud).
4. Check latency against your SLA. The published figure is under 80ms for in-environment enforcement; validate it on your own request patterns.
5. Confirm cost attribution granularity. If chargeback or per-team budget control is a requirement, verify that token and dollar tracking resolves to the user and team level you need.

Pricing and plan details are published on Fiddler's pricing page; terms should be confirmed directly rather than assumed.