raindrop vs future agi

Before Launch and After Launch

Raindrop monitors AI products in production, and everything in it assumes real traffic: Signals that classify behaviors you describe in plain language, Stumbles (single failed runs) that recur into Issues ranked by severity, a Triage Agent that investigates root causes, alerts in Slack, and Experiments that compare cohorts of already-logged production events with confidence intervals. For the local loop it has Workshop, an MIT-licensed agent debugger and eval runner launched in May 2026.

Future AGI is a platform with six parts, open source under Apache-2.0 at its core since Q2 2026. Simulate runs multi-turn text and voice conversations against your agent. Evaluate has built-in evaluators ("50+ metrics", by its README) and Future AGI's own judge models, Turing Large, Turing Small and Turing Flash. Monitor is traceAI, on OpenTelemetry, plus Error Feed, generally available since July 2026, which groups failures and suggests root causes. Protect runs guardrails that block, warn, mask or log. The Agent Command Center is an OpenAI-compatible gateway to 100+ model providers. Optimize rewrites prompts with methods such as ProTeGi, PromptWizard and GEPA.

The overlap is production monitoring: both trace runs and both group failures. Everything else sits on one side. Raindrop has nothing to say about an agent before its first user, and it does not try to. Future AGI has a module for most things, and asks more of you to adopt them.

Side by Side

Future AGI wins most rows because it has most modules. The rows that should decide it are the ones about timing: can you test before users arrive, and can you learn from users once they do.

One Voice Agent, Three Weeks from Launch

A chain of dental clinics is launching a voice agent that books, moves and cancels appointments. It goes live in three weeks. After launch it will take a few thousand calls a day. Here is where each product earns its place across that timeline.

1. Weeks one to three: before any real caller

   Future AGI simulates multi-turn voice conversations against the agent: the caller who changes their mind twice, the one who gives a date in the wrong format, the one who asks for a dentist who left. Its metrics and TURING judges score the runs, and Optimize proposes prompt changes for the failures. Raindrop has little to offer here beyond Workshop for debugging individual runs locally; its core features need production events.

2. Launch day

   Future AGI can sit in the request path through its gateway, with Protect masking personal details and blocking disallowed tool calls in the traffic it sees. Raindrop starts ingesting real calls. If you have written a signal like "caller asked for a human and was not transferred", it begins counting from the first call.

3. Week six: the failure nobody simulated

   Callers from one clinic keep getting booked into a room that closed. No simulation had that clinic's room list. Raindrop surfaces it as a recurring Stumble that becomes an Issue, ranked by severity, with a Slack alert. Future AGI's Error Feed groups the failures and suggests a root cause.

4. The fix

   With Raindrop, ship the fix behind a flag and use Experiments to compare the old and new cohorts on the signal rate. With Future AGI, add the case to your simulation set and re-run it, so it never regresses.

The timeline makes the choice clearer than any feature table. Future AGI is the stronger tool before launch, and a capable one after. Raindrop is idle before launch and at its best after, when the question becomes which of a thousand small failures is costing you the most callers.

Personal Data: Redact What Is Stored, or Mask What Is Sent

Both products handle personal data, at different points. Raindrop's PII Guard redacts at ingestion, so names and numbers never land in Raindrop; its client-side version is regex-based and TypeScript-only, and edge PII redaction is listed under Enterprise. That keeps your monitoring data clean. It does not change what the agent sent to the model or the caller.

Future AGI's Protect acts on the traffic itself. It runs guardrail checks such as PII detection and prompt-injection detection on text, image and audio, and can mask, block, warn or log, either in the gateway or wherever your code calls its SDK. Its pricing page includes built-in guardrails on every tier. For the dental agent, that is the difference between "the monitoring vendor never saw the patient's phone number" and "the model never saw it either".

If you need a signed HIPAA business associate agreement, Future AGI lists a HIPAA BAA on its Scale add-on at $750 a month. Raindrop's plans page does not mention HIPAA; ask before you send it health data.

What Each Asks You to Adopt

Raindrop asks for an SDK or an OpenTelemetry exporter and a Slack workspace. It observes your agent from beside it rather than sitting in the request path, so adopting it adds no hop to a live call. The trade is that Slack is the only documented alert channel, and self-hosting is a beta for select partners.

Future AGI asks for more, in return for more. Its runtime controls need your model traffic routed through the Agent Command Center, or Protect SDK calls in your code, which makes it part of the request path. Self-hosting is Docker Compose only, with Helm charts on the roadmap; the requirements page lists PostgreSQL, ClickHouse, Redis, Temporal, MinIO and a code executor that runs in privileged containers, and the repository carries a LICENSE-EE file under which code in its ee/ directories needs an Enterprise licence key or a Future AGI Cloud plan in production. Its own issue tracker is candid about the rough edges of that stack, including a ClickHouse config crash-loop (#1540) and missing gateway logs (#1060). The managed service avoids all of that.

Where Each One Is Stronger

• Raindrop: turning production noise into a short list. Stumbles, severity-ranked Issues and the Triage Agent are built for the week-six problem above, when there are thousands of runs and a handful of failures that matter.
• Raindrop: measuring a change in production. Experiments compare logged cohorts before and after a model, flag or other change, with confidence intervals.
• Raindrop: less to adopt. An SDK or exporter and a Slack workspace, with nothing added to the request path.
• Future AGI: testing before users exist. Simulated conversations, including voice, and synthetic datasets.
• Future AGI: acting on traffic. Protect and the gateway scanners can block, warn, mask or log in real time.
• Future AGI: ownership and price of entry. An Apache-2.0 core you can self-host, unlimited seats, and a free tier large enough for a real pilot.

Some teams will want both: Future AGI's simulation and guardrails on the road to launch, and Raindrop's signals once the traffic is real. The products overlap little enough that running both is not wasteful, though it is two vendors to put through a security review.

Pricing: Two Different Shapes

Two worked examples, every assumption stated. Raindrop Pro at 200,000 events a month is $299 + $600 = $899. Future AGI, for 500 simulated four-minute voice calls in a month, is 2,000 minutes, less 60 free, times $0.08 at the listed starting rate: $155.20, before any other usage. They are not comparable bills because they buy different things, which is the point of this page.

Future AGI's free tier is the more generous of the two by any measure, and its unlimited seats matter for larger teams. Raindrop's free tier is a trial in practice: it stops ingesting at 1,000 events and keeps Issues and Experiments in preview. One caution on Future AGI: its pricing page and enterprise page do not agree on every SLA figure and which add-on gates what, so confirm the tier you need in writing.