Both trace agents, run offline experiments and score production traffic with LLM and code evaluators. Fiddler suits enterprises that need one vendor for classic ML monitoring and agents, with content guardrails; Maxim AI suits agent teams that want simulation, prompt versioning and a gateway in one place. Whether you also run classic ML models narrows it quickly.[1][4]
Fiddler monitors classic ML models, LLM applications and agents in one platform and sells low-latency guardrails; Maxim AI focuses on agents, with pre-release simulation, prompt versioning and its own gateway, Bifrost. Fiddler has no prompt versioning or playground, and Maxim AI does not cover classic ML models. Both can block content only through a gateway: a third-party one for Fiddler, Bifrost Enterprise for Maxim AI.
| Decision factor | Fiddler | Maxim AI |
|---|---|---|
| Ownership | Independent; $30M Series C in January 2026 | Independent; $3M seed led by Elevation Capital in 2024 |
| Scope | Classic ML models, LLM applications and agents | LLM applications and agents, including voice |
| Prompt tooling | None documented | Playground, versions, deployment rules and prompt optimisation |
| Guardrails | Real-time content scoring; inline blocking through a third-party gateway | Through its own Bifrost gateway, on the Enterprise edition |
| Native agent SDKs | Python only; other languages via OpenTelemetry or REST | Python, JavaScript/TypeScript, Go and Java |
| Self-hosting | Enterprise: VPC or on-premises; also a SageMaker Partner AI App | Enterprise: in-VPC, fully isolated or data plane only |
| Free tier | Guardrails only, via a demo request | Up to 3 seats, 10k logs a month, 3-day retention |
| First paid tier | Developer: $0.002 per trace | Professional: $29 per seat a month, 100k logs |
| Capability | Fiddler | Maxim AI | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | Partial | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | 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 |
Fiddler's platform covers traditional ML model monitoring alongside LLM and agent observability, with integrations into Amazon SageMaker, Databricks, MLflow, Snowflake, BigQuery and Kafka. If your organisation already monitors fraud, credit or forecasting models and now adds agents, one vendor for both is the main reason to choose it. Fiddler raised a $30M Series C in January 2026 and repositioned around AI agents at the same time.
Maxim AI does not cover classic ML. It is built for LLM applications and agents, including voice agents, with SDKs in Python, JavaScript and TypeScript, Go and Java, and integrations with frameworks such as the OpenAI Agents SDK, LangGraph, CrewAI, Google ADK and LiveKit. Fiddler's native agent SDKs are Python only, covering LangGraph, LangChain, Strands Agents and Google ADK; other languages go through raw OpenTelemetry or its REST API.
Deployment and cost tracking follow the same split. Fiddler's Enterprise plan lists SaaS, VPC and on-premises deployment, and Fiddler can run inside your own AWS account as an Amazon SageMaker Partner AI App; the self-serve Developer plan is SaaS only. Maxim AI's in-VPC deployment on AWS, GCP or Azure is Enterprise only, fully isolated or as a data plane peered to Maxim's control plane. Fiddler tracks tokens and stores monetary cost only when the trace source emits cost attributes; Maxim AI logs cost per request with threshold alerts.
Fiddler's Guardrails score requests and responses in real time across content dimensions, faithfulness, personal data and secrets, using its own Centor Models. Your application decides what to do with the score. Inline blocking or redaction without code changes needs a third-party AI gateway: Fiddler supports Kong AI Gateway, AgentGateway, LiteLLM and, since October 2026, Portkey. The free plan covers these guardrails, accessed through a demo request.
Maxim AI owns the gateway. Bifrost is an Apache-2.0, OpenAI-compatible gateway with failover, caching, virtual keys and an MCP gateway, and its guardrails check inputs, outputs and MCP tool-call arguments and results and can block, redact or transform content. Guardrails are a Bifrost Enterprise feature. Neither vendor documents a human approval step or a policy engine for agent tool actions. If you already run Kong, LiteLLM or Portkey, Fiddler slots into it; if you have no gateway yet, Maxim AI supplies one.
Fiddler documents no prompt versioning or playground; its Prompt Specs are templates for its own LLM judges. Experiments run through its Evals SDK against datasets with 13 pre-built evaluators and custom ones, and span-level human annotations have been in public preview since August 2026, without a dedicated review queue.
Maxim AI includes a prompt playground, versions, deployment rules that release prompt changes without code changes, and prompt optimisation. It runs multi-turn text and voice simulations, test runs in CI through GitHub Actions, and annotation queues for internal or external raters. For teams that iterate on prompts daily, this is the clearer gap between the two. Fiddler, in turn, hosts its own judge model, Ministral 3 8B, generally available since September 2026, so LLM-as-a-judge evaluators can run without your own provider keys.
As of 20 October 2026, Fiddler's Developer plan is $0.002 per trace on SaaS and adds observability, experiments, custom evaluators, RBAC and SSO; at that rate, 100k traces would cost $200. Enterprise, which adds VPC and on-premises deployment, is quote-based. Maxim AI's Developer tier is free for up to 3 seats and 10k logs a month; Professional is $29 per seat a month with 100k logs, Business $49 per seat a month with 500k logs, and overage is $1 per 10k logs. Enterprise, the only plan with in-VPC hosting, is custom.
Fiddler's cost follows trace volume; Maxim AI's follows seats first. See Fiddler's pricing and Maxim AI's pricing.
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 what it finds through runtime policies on tool calls, approval holds for risky actions and a kill switch scoped to a run, an agent or a team. Fiddler and Maxim AI both block content through a gateway, and neither documents approval holds or tool-call policies. Prefactor has no prompt playground or prompt registry, does not monitor classic ML models and its platform is hosted, so Fiddler is the better pick for mixed ML estates and Maxim AI for prompt iteration. 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.