Both trace agents and score production traffic, but their centre of gravity differs. Maxim AI is strongest before release, with text and voice simulation and a broad evaluator library; Raindrop is strongest after release, finding recurring failures in live traffic. Maxim also offers run-time guardrails through its Bifrost gateway; Raindrop has none.[1][4]
Maxim AI is a hosted platform for simulating, evaluating and tracing agents before and after release, including voice agents, sold alongside Bifrost, its open-source AI gateway; Raindrop monitors agents already in production, classifying live events with custom signals and grouping recurring failures into issues. Maxim prices per seat with log caps; Raindrop prices per event. Both are independent.
| Decision factor | Maxim AI | Raindrop |
|---|---|---|
| Ownership | H3 Labs Inc., independent; $3M seed led by Elevation Capital in 2024 | Independent; Series A led by CRV in September 2026, $50M raised in total |
| Centre of gravity | Simulation and evaluation before release, plus production tracing | Production monitoring: Signals, Issue Detection, live Experiments |
| Simulation | Multi-turn text and voice simulation | Simulations on pull requests, in early access |
| Evaluators | Pre-built and custom LLM judges, JavaScript or Python code, API evaluators, human raters | Signals built by Raindrop's own models; no custom judge prompt or model |
| Prompt tooling | Playground, versions and deployment rules | None documented |
| Run-time controls | Through Bifrost only; guardrails are a Bifrost Enterprise feature | None documented |
| Free tier | Developer: up to 3 seats, 10k logs a month, 3-day retention | Hobby: 1,000 events a month, 14-day retention |
| First paid tier | Professional: $29 per seat a month, 100k logs | Pro: $299 a month plus $0.003 per event up to 1M events |
| Capability | Maxim AI | Raindrop | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | ✓ | ✓ |
| 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 | ✓ |
| Prompt versioning and playground | ✓ | — | — |
| Act on findings | |||
| Runtime controls on live agent actions | ✓ | — | ✓ |
| Gateway or proxy in the request path | ✓ | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | ✓ | Partial | Partial |
Maxim AI is built around testing an agent before users see it. You run test suites over datasets against prompts, no-code agents or any agent behind an HTTP endpoint, from the UI or SDK, on a schedule or in GitHub Actions. Its simulations play out multi-turn conversations in text or voice, with integrations for LiveKit, ElevenLabs and the OpenAI Realtime API. Production logs can then be scored automatically at session, trace or node level from the Professional plan upwards.
Raindrop starts from production. Signals classify every new event and can backfill 90 days of sampled history; Issue Detection groups recurring failures; Experiments compare signal rates between cohorts of live traffic. Pre-release testing is newer: Workshop is an open-source local debugger where a coding agent turns failures into evals, and Simulations, which replay production traffic and test cases against a change on each pull request, launched in early access in September 2026.
A team building a voice agent or wanting product staff to run test suites will find more in Maxim. A team whose agent is live and producing failures nobody has categorised will find Raindrop closer to the problem.
Maxim offers pre-built judges for faithfulness, toxicity and task success, custom LLM judges with your chosen model, instructions and scale, programmatic evaluators in JavaScript or Python, and evaluators that call an external API. Agent-specific evaluators cover trajectory, tool selection and task success. Human raters can review logs through annotation queues, and since January 2026 external raters can be added by email.
Raindrop's Signals are drafted from a plain-language description and tuned by labelling sample events as Match or Not Match. Since August 2026 they run on rd-signal-2, Raindrop's own classification system, and you cannot plug in a custom judge prompt or model. Human labelling exists on dataset members, but there is no dedicated review queue. Raindrop trades control for speed of set-up.
As of 20 October 2026, Maxim's Developer plan is free for up to three seats and 10k logs a month, with three days of retention. Professional is $29 per seat a month with 100k logs and seven days of retention, and Business is $49 per seat a month with 500k logs and 30 days. Overage on paid tiers is $1 per 10k logs. In-VPC hosting is Enterprise only. Bifrost's open-source edition is free.
Raindrop's Hobby plan is free for 1,000 events a month with one custom signal and 14-day retention. Pro is $299 a month plus $0.003 per event up to 1M events, then $0.002, with unlimited retention. Maxim's cost rises with headcount and log volume; Raindrop's with event volume. Note the short retention on Maxim's lower tiers if you need to look back across weeks. Check Maxim AI's pricing and Raindrop's plans.
Maxim's Bifrost is an Apache-2.0 gateway with provider failover, load balancing, caching, virtual keys and an MCP gateway. Its guardrails check model inputs and outputs and MCP tool-call arguments and results, and can block, redact or transform content; the pricing page lists guardrails as a Bifrost Enterprise feature. The evaluation platform itself alerts but does not block. Raindrop has no gateway and no blocking.
Product direction is worth checking too. In October 2026 Maxim's homepage and pricing page began leading with Bifrost, and the evaluation product moved under /evals. Its public changelog shows no dated 2026 entries, and the newest monthly update post covers December 2025, so recent platform changes are hard to verify. Raindrop shipped version 2.0 in June, a new classifier in August and Simulations in September 2026.
On instrumentation, Maxim ingests OTLP over HTTP and can forward traces to New Relic, Snowflake or any OpenTelemetry collector; Raindrop accepts OTLP traces over HTTP only and lists many framework integrations, most marked beta. Both list SOC 2 Type II, and Maxim also lists ISO 27001 and HIPAA BAAs on Enterprise.
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, approval holds for risky actions and a kill switch for a run, an agent or a team. Maxim's Bifrost guardrails can block content in model and MCP tool calls on its Enterprise edition; Raindrop documents no run-time controls, and neither documents approval holds. Prefactor has no prompt playground or prompt registry, no voice simulation, and its platform is hosted. 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.