raindrop vs galileo

Production Monitoring and an Eval Platform

Raindrop, YC W24, monitors agents in production. It ingests production traces, including over OpenTelemetry, with SDKs for the Claude Agent SDK, Vercel AI SDK, LangChain and CrewAI. Signals turn a plain-language description into a classifier; failures in single runs are Stumbles, which recur into Issues ranked by severity; a Triage Agent hunts the root cause; Experiments compare cohorts of already-logged production events.

Galileo is built around evaluation. It ships 20+ out-of-box evals for RAG, agents, safety and security, including agentic metrics such as Tool Selection Quality, Tool Call Error Detection and Session Success; custom evaluators; Luna-2 small evaluation models; and CLHF to tune a metric from a few human labels. Cisco completed its acquisition of Galileo in May 2026, according to Splunk, and Galileo's technology is going into Splunk Observability. Its release notes say that as of August 7, 2026, "Galileo is now Splunk Agent Observability"; this page keeps the Galileo name, still used on galileo.ai.

The philosophical split is where evidence comes from. Raindrop trusts what production users did. Galileo trusts a well-built eval, run offline first and then promoted to production. Both positions are defensible, and the right one depends on how many users you have and how much a single bad run costs.

Side by Side

Read the two rows in the middle together. Raindrop can tell you, with a confidence interval, that the cohort after a change fared worse than the cohort before it, on the signals you chose. Galileo can stop a specific bad input or output before it reaches anyone. Those are different jobs, and most teams feel the need for one of them first.

One Model Swap, Both Tools

A team runs a customer-facing shopping agent and wants to move it to a newer, cheaper model. The question is simple and expensive to get wrong: is the new model at least as good for our users? Here is how each tool answers it.

1. Galileo: evaluate before, guard after

   Before switching, run the same set of representative sessions through both models and score them with Galileo's agentic metrics, such as Tool Selection Quality and Session Success, plus a custom evaluator for your catalogue rules. If a reviewer disagrees with a metric, CLHF retunes it from a handful of annotated records. Ship the model when the offline scores hold. In production, keep the same evals running, and if the new model shows a failure you cannot tolerate, promote the check to a guardrail: hosted on Enterprise, or through Agent Control in your own stack.

2. Raindrop: ship to a slice, then measure

   Ship the new model behind your own feature flag for part of your traffic; Raindrop does not split or route traffic itself. Once events are logged, create an Experiment with the baseline cohort set to the old model and the experiment cohort to the new one, pick the signals that define failure, such as "user had to repeat the request", and read the result: each signal's change with a 95% confidence interval and p-value, labelled likely better, likely worse or mixed, per event or per user.

3. What each told you

   Galileo told you the new model scores as well on cases you chose, before any customer saw it. Raindrop told you how real customers fared, after some of them had. The first protects you from the failures you anticipated; the second finds the ones you did not.

Volume decides which evidence you can get. Raindrop's docs state no traffic minimum, but two small cohorts make a noisy comparison by nature. An internal agent with 200 users a day will wait a long time for a clean Experiment result, and is better off with Galileo's offline approach. A consumer app with 200,000 users a day can get an answer from its own logged events that no offline dataset would give it.

Acting at Runtime

Raindrop does not intervene in a run. It detects, ranks and alerts, in Slack, which is its only documented alert channel. Its PII Guard redacts personal data at ingestion, so it never lands in Raindrop, but that controls what gets stored, not what the agent does.

Galileo can intervene, with two caveats. Its pricing page lists "Real-time guardrails" under Enterprise only, and Luna-2, the small model family its docs pitch as fast enough for runtime protection, is Enterprise-only too. The other route is Agent Control, Apache-2.0 since March 2026, which checks LLM and tool inputs and outputs against a policy and returns deny, steer, warn, log or allow; you run its server yourself on PostgreSQL, with Python and TypeScript SDKs. A centrally managed version for enterprise customers shipped in June 2026. The older Galileo Protect was deprecated in June 2026 in favour of Agent Control.

So if a runtime control matters and you are not buying Enterprise, Galileo's answer is an open-source service your team deploys and operates. That is a real option, and it is also a real piece of infrastructure.

Where Each One Is Stronger

Raindrop

• Evidence from production. Experiments put a confidence interval on how logged production behavior differed before and after a change. Galileo does not publish an equivalent.
• Speed to a check. Describing a behavior in a sentence and labelling a sample is quicker than building an evaluator, and Raindrop drafts the classifier for you.
• Triage. Stumbles roll up into severity-ranked Issues, the Triage Agent investigates, and a coding agent can work the issue over MCP, since anything you can do in the UI it can do too.
• Focus. One job, done with a lot of finish, and nothing to add to the request path.

Galileo

• Coverage on day one. Twenty-odd metrics you do not write, including agentic ones for tool selection, tool errors and session success.
• A path to runtime control. Hosted guardrails on Enterprise, or Agent Control in your own stack; Raindrop has neither.
• Judge economics and tuning. Luna-2 makes judging cheap enough for runtime, on Enterprise, and CLHF improves a metric from a handful of labels.
• Enterprise deployment. VPC and on-prem on Enterprise, and a free tier with unlimited users for trying it first.

The lists barely overlap, which is the most useful thing about this comparison. Teams that end up running both are usually not being indecisive: they want Galileo's evals and controls on the high-stakes path, and Raindrop's production signals on everything else.

What the Meters Count

The two meters count different things. Raindrop bills events; Galileo bills traces; a single agent run can be one trace and many events, depending on how each SDK records it. Check how your agent maps to each unit before comparing. As a worked example on Raindrop's side only: 50,000 events a month on Pro is $299 + $150 = $449. Galileo Pro covers 50,000 traces for $100 a month on a yearly bill, and the page says pricing scales with traces beyond that. Only the Free tier is self-serve, though: Pro and Enterprise both start with "Book a Demo".

The free tiers behave differently at the edge, too. Raindrop Hobby stops ingesting when you hit 1,000 events for the rest of the calendar month, and keeps its headline features in preview. Galileo Free gives five times the allowance in a different unit, with unlimited users.

Who Each Is Built For

The customer lists tell the story. Raindrop shows Speak, Vercel, Clay, Framer, AngelList and Browserbase, with a Tolan case study reporting 27.8% fewer memory issues, by Raindrop's account. Galileo cites Twilio, Comcast, HP, ServiceTitan and "six Fortune 50 companies". Raindrop is built for product teams that ship to many users and move fast; Galileo for organisations with security reviews, procurement and a need to prove control.

Galileo's ownership changed this year. The public facts are Cisco's April announcement, Splunk's May statement that the deal is complete, and the plan to build Galileo's technology into Splunk Observability's AI Agent Monitoring. What that means for standalone plans is not public, so if you are signing a multi-year contract, ask. Raindrop is independent, with a $15M seed led by Lightspeed in December 2025.