Fiddler AI

Fiddler AI provides observability and security for AI agents and predictive applications. It evaluates agents and models during development and monitors them in production, with diagnostics covering more than 100 built-in and custom metrics. These include hallucination, toxicity, PII/PHI, drift, performance, and business KPIs. Real-time guardrails can detect risks and pause, reroute, or escalate agent actions. The platform records decisions, actions, evaluations, and policy outcomes to support audit evidence for requirements including GDPR, HIPAA, NAIC, and SR 11-7. Named native framework support includes LangGraph, Amazon Bedrock, and AWS Strands Agents; the platform is built on OpenTelemetry standards. Listed integrations include Snowflake, BigQuery, Amazon S3, Kafka, Airflow, Databricks, MLflow, Datadog, PagerDuty, Okta, and Google Workspace. Deployment choices include cloud SaaS, VPC, on-premises, and air-gapped environments. Fiddler states that it is SOC 2 Type II certified, complies with HIPAA, and encrypts data in transit and at rest. Free and Developer plans are listed at $0, with Enterprise pricing not listed.

Fiddler AI combines development evaluation, production monitoring, guardrails, and governance for AI systems.

Fiddler AI is aimed at enterprises managing AI agents and predictive applications at scale, including teams that need monitoring, guardrails, and audit evidence.

Fiddler AI is an observability and security platform for AI agents and predictive applications, spanning development evaluation and production monitoring. It best suits enterprises managing these systems at scale. Its appeal is the combination of diagnostics, intervention controls, and governance; teams seeking only lightweight tracing may not need that breadth.

Overview

Fiddler brings development tests and experiments together with production monitoring, so teams can evaluate models and agents before deployment and track them as they run. It offers more than 100 built-in and custom metrics, covering issues such as hallucination, toxicity, sensitive information exposure, drift, performance, and business KPIs. Cost and token tracking, LLM and agent tracing, retrieval tracing, and prompt management round out the observability toolkit.

The platform’s scope is a strength for organizations that need visibility and controls across an AI system’s lifecycle. The tradeoff is that its enterprise orientation may be more than a small team needs, particularly if the priority is a simple, bounded tracing service.

Key features

Diagnostics and controls

Real-time guardrails can detect risks and pause, reroute, or escalate agent actions. That makes Fiddler relevant where a risky output or action should trigger a response rather than merely appear in a dashboard. Its recorded decisions, actions, evaluations, and policy outcomes can also support audit evidence for GDPR, HIPAA, NAIC, and SR 11-7 requirements.

Frameworks, integrations, and deployment

Native support includes LangGraph, Amazon Bedrock, and AWS Strands Agents, with the platform built on OpenTelemetry standards. Connections include Snowflake, BigQuery, Amazon S3, Kafka, Airflow, Databricks, MLflow, Datadog, PagerDuty, Okta, and Google Workspace. Deployment choices range from cloud SaaS to VPC, on-premises, and air-gapped environments, a useful range for organizations with differing infrastructure and data-control needs.

Fiddler says it is SOC 2 Type II certified, encrypts data in transit using HTTPS/TLS 1.2 or higher and at rest using AES-256 or higher, and complies with HIPAA. Those controls will matter to regulated buyers, though they do not by themselves determine whether a particular deployment meets an organization’s requirements.

Pricing

Fiddler uses a freemium model. The Free plan costs nothing and includes real-time guardrails, latency under 80 ms, and Centor Models. It is a narrow entry point for trying guardrails, not a substitute for the broader observability and experimentation features.

The Developer plan is priced on request, billed per trace. It adds AI observability, tests and experiments, custom evaluators, bring-your-own-judge support, RBAC and SSO, and SaaS deployment. For teams needing evaluation and access controls without enterprise deployment options, this is the more capable starting tier; billing per trace is an important consideration as usage grows.

Enterprise has custom pricing and includes everything in Developer, plus enterprise-grade guardrails and scalability, SaaS, VPC, or on-premises deployment, dedicated support, a named customer success manager, and customized onboarding. It is the fit for organizations that need those deployment choices and direct support, but buyers will need a quote to assess cost.

Platforms

Fiddler is available through web and API access, with self-hosted deployment also supported. Enterprise deployment options include SaaS, VPC, and on-premises; the product page also identifies air-gapped deployment.

Who it's for

Fiddler is best suited to enterprises operating AI agents and predictive applications at scale, especially those that need development evaluation, production diagnostics, real-time guardrails, and audit-supporting records in one platform. A smaller team focused on basic request tracing may find the breadth and enterprise emphasis unnecessary.

Pros and cons

• Pros: More than 100 built-in and custom metrics cover both model risks and business KPIs, giving teams broad diagnostic coverage.
• Pros: Guardrails can pause, reroute, or escalate risky actions, enabling intervention rather than observation alone.
• Pros: SaaS, VPC, on-premises, and air-gapped deployment choices accommodate varied infrastructure needs.
• Cons: Developer pricing is billed per trace, so teams should account for usage as they scale.
• Cons: Enterprise pricing is custom, making direct price comparison difficult before a quote.
• Cons: The combination of lifecycle observability, governance, and enterprise deployment may exceed the needs of teams seeking only lightweight tracing.

Alternatives

Arize AX is worth considering for a free SaaS start with clear published limits: 10 issues per month, 25,000 trace spans per month, 1 GB of ingestion per month, and 15-day retention, with unlimited users and evaluations.

Evidently AI is a stronger fit for teams prioritizing an open-source framework: its Evidently framework is licensed under Apache 2.0.

Helicone is an alternative for teams looking for an open-source observability option with a free Hobby plan.

HoneyHive may suit teams whose needs fit a Developer tier capped at 10,000 events per month, five users, one workspace, 30-day retention, and 1,000 requests per minute.

Laminar offers a published Free plan with 1 GB of data, seven-day retention, one project, one seat, and no overage, useful for a tightly bounded start.

Langfuse is another freemium option, with a Hobby plan that includes 50,000 units per month, 30 days of data access, and two users.

LangSmith may be a fit for an individual developer starting with one seat and up to 5,000 base traces per month before pay-as-you-go charges.

LangWatch is an alternative for teams seeking an open-source option under Apache 2.0.

For broader comparisons, see LLM Observability Tools, Machine Learning Model Monitoring Software, AI Agent Observability Tools, AI LLM Observability Tools, and Model Monitoring Software.

Verdict

Choose Fiddler AI if your organization needs to evaluate and monitor agents and models while applying guardrails and maintaining governance records across deployments. Its main advantage is the breadth of that control plane; look elsewhere if you want only basic tracing or need a clearly priced enterprise tier before engaging a vendor.