Both trace agents and score production traffic, but they are built for different buyers. Fiddler suits enterprises that want ML models, LLM apps and agents in one platform, with content guardrails and on-premises deployment; Raindrop suits product teams with an agent in production who want recurring failures found and changes compared on live traffic.[1][4]
Fiddler is a commercial platform that monitors classic ML models, LLM applications and agents together and adds real-time content guardrails, while Raindrop is a hosted monitor focused only on agents in production, classifying live traffic and grouping recurring failures into issues. Fiddler offers VPC and on-premises deployment on Enterprise; Raindrop's self-hosting is in beta. Both are independent, and neither has prompt versioning.
| Decision factor | Fiddler | Raindrop |
|---|---|---|
| Ownership | Independent; $30M Series C in January 2026, $100M raised in total | Independent; Series A led by CRV in September 2026, $50M raised in total |
| Scope | Classic ML models, LLM applications and agents | Agents and LLM applications in production |
| Offline evals | Yes: Evals SDK with 13 pre-built evaluators and custom ones | Partial: Workshop runs evals locally; Simulations in early access |
| Production scoring | Evaluator Rules on new spans, with backfill and downsampling | Signals on every new event; Issue Detection groups recurring failures |
| Run-time checks | Partial: real-time content guardrails; inline blocking through a third-party gateway | None documented |
| Deployment | SaaS; VPC, on-premises and SageMaker on Enterprise | Hosted; Enterprise VPC in beta for select partners |
| Agent SDKs | Python only; other languages through OpenTelemetry or REST | TypeScript, Python, Go; Rust and Java in beta |
| Self-serve pricing | Developer: $0.002 per trace (SaaS) | Pro: $299 a month plus $0.003 per event |
| Capability | Fiddler | Raindrop | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | Partial | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | Partial | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | Partial | ✓ |
| Code-based evaluators | ✓ | Partial | ✓ |
| Human review and annotation | Partial | Partial | ✓ |
| Prompt versioning and playground | — | — | — |
| Act on findings | |||
| Runtime controls on live agent actions | Partial | — | ✓ |
| Gateway or proxy in the request path | — | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | ✓ | Partial | Partial |
Fiddler started in classic machine learning monitoring and now covers LLM applications and agents too; since its January 2026 Series C it describes itself as a control plane for AI agents. If your organisation runs classic ML models alongside agents and wants one vendor watching all of them, Fiddler is built for that. It integrates with Amazon SageMaker, Databricks, MLflow, Snowflake and BigQuery, among others.
Raindrop is narrower by design. It watches agents in production, follows every message, tool call and decision in a run, and tries to tell you which failures keep happening. It does not cover classic ML models. For a product team whose only AI system is the agent, that focus means less to set up.
Fiddler gives you evaluators you configure. The Evals SDK runs experiments against golden datasets with 13 pre-built evaluators, custom LLM judges built from Prompt Specs, regex checks and any Python function. Evaluator Rules apply the same evaluators to new production spans that match filters, with backfill and downsampling to limit judge cost. Judges can run on Fiddler-hosted models or on your own provider keys.
Raindrop asks you to describe a behaviour in plain language and drafts a classifier, a Signal, that runs on every new event and backfills a sampled 90 days. You tune it by labelling samples, but you cannot supply your own judge prompt or model. Issue Detection groups recurring failures, and Experiments compare signal rates between cohorts of live traffic. Pre-release testing relies on Workshop, an MIT-licensed local tool, and Simulations, which is in early access.
Human review is light on both sides. Fiddler's span-level Annotations have been in public preview since August 2026, without a review queue. Raindrop lets you label dataset members and samples, also without a queue.
Cost visibility differs as well. Fiddler tracks token usage across agent interactions but stores money spent only when your trace source emits cost attributes, as LiteLLM does; an API for judge spend is in private preview. Raindrop calculates spend by model, provider and project from a versioned catalogue of public model rates, and its Experiments report median cost per event. One practical note for Fiddler users: Python 3.10 support in its SDK ends on 27 October 2026.
This is where Fiddler has something Raindrop does not. Fiddler Guardrails score requests and responses in real time for toxicity, prompt injection, jailbreaks, faithfulness, personal or health data and secrets, and your application decides whether to block. When Fiddler is plugged into a third-party gateway such as Kong, AgentGateway, LiteLLM or Portkey, it can block or redact inline. These are content checks; Fiddler documents no approval step or tool-call policy for agent actions. Raindrop documents no run-time checks at all; its PII Guard redacts data on ingestion into Raindrop.
Deployment follows the same split. Fiddler's Enterprise plan offers SaaS, VPC and on-premises, and Fiddler can run inside your AWS account as a SageMaker Partner AI App. Raindrop is hosted, with an Enterprise VPC deployment in beta for select partners. Both accept OpenTelemetry traces; Raindrop takes OTLP over HTTP only.
As of 20 October 2026, Fiddler's free plan covers guardrails only and is reached through a demo request. Its Developer plan is $0.002 per trace on SaaS and adds observability, experiments, custom evaluators and SSO. Enterprise, including VPC and on-premises, is quote-based. Raindrop's Hobby plan is free for 1,000 events a month, after which ingestion stops until the next month. Pro is $299 a month plus $0.003 per event up to 1M events, then $0.002, with unlimited retention and a 14-day trial.
A trace and an event are not the same unit, so price your own traffic against Fiddler's pricing and Raindrop's plans. Fiddler has no monthly platform fee on its self-serve plan; Raindrop's Pro has one.
We sell the layer this section describes. Read it with that in mind.
Stated plainly, since Prefactor publishes this page: Prefactor is an agent evaluation platform that scores each step of a run and acts on a failing score through runtime policies on tool calls and data access, approval holds and a kill switch for a run, an agent or a team. Of these two, Fiddler's guardrails come closest, but they check content rather than hold an agent's actions for approval; Raindrop alerts after the fact. Prefactor has no prompt playground or prompt registry, its platform is hosted, and it does not monitor classic ML models. See the full comparison of 15 platforms.
Reviewed against public product and documentation pages on October 20, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Prefactor scores each step of every agent run and can hold or stop an action when a score says it should not happen. Start on the free tier: 5,000 spans a month, no credit card required.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.