Fiddler AI: Observability and Security for LLMs

Fiddler AI is an observability and security platform built for AI systems — both traditional ML models and LLM/agent applications. It combines real-time guardrails, trust scoring, custom evaluators, and tracing into a single platform. The pitch is unified visibility: monitor your predictive models and your LLM agents in one place rather than stitching together separate tools.

• You need production-grade guardrails on LLM outputs (hallucination detection, PII/PHI filtering, prompt injection defense, toxicity monitoring)
• You are running agentic AI in production and need trace-level observability of tool calls, reasoning steps, and outputs
• You have both traditional ML models and LLM applications and want unified monitoring
• You operate in a regulated industry (healthcare, finance, insurance) where audit trails and governance documentation are required
• You need custom evaluators that go beyond generic benchmarks to score domain-specific quality

• Real-time guardrails with low latency. Fiddler's Trust Service provides guardrails at <80ms latency for detecting hallucinations, toxicity, PII/PHI, prompt injection, and jailbreak attempts. This is fast enough to run inline on production agent outputs without unacceptable latency overhead.

• Fiddler Centor Models for trust scoring. Instead of just rule-based filters, Fiddler uses contextual ML models to score trust dimensions — faithfulness, legality, hateful content, harassment, and more. These are purpose-built models, not generic classifiers.

• Custom evaluators for domain-specific quality. You can define evaluators that measure what matters to your specific use case, not just generic LLM benchmarks. This is critical for agentic systems where "good output" is domain-specific.

• Bring-your-own-judge. You can use your own LLM as the judge for evaluations, which lets you leverage models you already trust for quality scoring.

• Flexible deployment. SaaS, VPC, or on-premise. This matters for regulated industries that cannot send AI telemetry to a third-party SaaS.

• Unified ML and LLM monitoring. If you already have traditional ML models in production, Fiddler monitors both in one platform. Most LLM observability tools are LLM-only.

• Enterprise customer base. Mastercard, US Navy, Nielsen, American Family Insurance, DTCC — Fiddler has traction in regulated enterprises that require serious governance.

• Pricing is not transparent at enterprise scale. The Free tier covers guardrails. The Developer tier is $0.002 per trace — clear enough. But Enterprise pricing is "contact sales" with no public numbers. For teams trying to budget, this means a sales conversation before you know your cost.

• Trace-based pricing can get expensive for high-volume agents. At $0.002/trace, an agent making 10 tool calls per task across 100,000 tasks/month generates 1,000,000 traces = $2,000/month on the Developer tier alone. Agentic systems generate far more traces than simple LLM calls.

• Heavier than pure-play LLM observability tools. If you only need LLM tracing and cost tracking, Fiddler is more than you need. Langfuse or Helicone are lighter and cheaper for LLM-only use cases. Fiddler's strength is unified ML+LLM monitoring plus guardrails — if you do not need that, you are paying for capability you will not use.

• Setup complexity for custom evaluators. Building custom evaluators requires understanding Fiddler's evaluator framework. The platform provides the infrastructure, but defining what "good" means for your domain is still your job. This is inherent to the problem, not a Fiddler-specific flaw, but it means the platform is not plug-and-play for quality monitoring.

• Enterprise features gated behind sales. SSO, RBAC, enterprise-grade infrastructure, on-premise deployment, and white-glove support are all Enterprise-tier. Small teams and startups will use the Free or Developer tier, which means no SSO and no VPC.

• Less community ecosystem than Langfuse. Langfuse is open-source with a large community. Fiddler is a commercial platform. If you value community-contributed integrations and the ability to self-host without a commercial relationship, Langfuse is the better fit.

• Free tier: Real-time guardrails for harmful content detection. No cost. Good for getting started with output safety.
• Developer tier: $0.002 per trace. Includes unified observability, custom evaluators, RBAC, SSO, SaaS deployment. This is the tier most teams will start with for production monitoring.
• Enterprise tier: Custom pricing. Adds enterprise-grade guardrails, VPC/on-premise deployment, dedicated support, and named CSM.
• Budget reality: for a mid-size agent deployment (50,000 tasks/month, ~5 traces per task), expect ~$500/month on the Developer tier. Scale from there.

Pricing verified against fiddler.ai/pricing on 2026-08-05. Enterprise pricing requires a sales conversation.

Teams in regulated industries running both traditional ML and LLM/agent systems in production, who need guardrails, governance documentation, and unified monitoring. If you are a small team building LLM-only prototypes, Fiddler is overkill — look at Langfuse or Helicone instead. If you are an enterprise with mixed ML/LLM workloads and compliance requirements, Fiddler is one of the most complete platforms available.

• OpenAI, Anthropic, Google model providers (via tracing SDKs)
• Amazon SageMaker (native integration, Fiddler runs inside SageMaker)
• Google Cloud (partnership for healthcare/regulated workloads)
• LangChain / LlamaIndex (tracing via OpenTelemetry)
• Custom Python/REST (Fiddler SDK for any application that can emit traces)