galileo vs braintrust

What Each Says About the Other

Both vendors publish comparison pages. Galileo's (October 2025) calls Galileo an "end-to-end GenAI reliability platform that unifies evals, monitoring, and guardrails", sets it against Braintrust's "evaluation-first workflow", and says Braintrust has "no native runtime blocking". Braintrust's (April 2026) describes Galileo's evaluation logic as "vendor-maintained, opaque" against its own code-based scorers stored alongside application code.

Those pages are leads, not evidence; each picks the axis it wins on. Galileo's page also predates changes to its own product: Galileo Protect, the guardrail product of that era, was deprecated as of June 2026, and Galileo's docs now send new users to Agent Control. Everything below is checked against each vendor's own docs and pricing instead.

What Each Product Actually Is

Galileo is built around evaluation. It ships "20+ out-of-box evals for RAG, agents, safety, and security" plus custom evaluators, including agentic metrics such as Tool Selection Quality, Tool Call Error Detection and Session Success. Luna-2 is a family of small evaluation models at 3B and 8B parameters, $0.02 per million tokens, and "only available in the Enterprise tier". CLHF tunes a judge metric from as few as five annotated records. Agent Control, released in March 2026 under Apache-2.0, checks LLM and tool inputs and outputs and returns deny, steer, warn, log or allow; you deploy and run the open-source version yourself, and a centrally managed version for enterprise customers shipped in June 2026. Cisco announced its intent to acquire Galileo in April 2026, Splunk said in May that the deal was complete, and Galileo's release notes say that as of 7 August 2026 "Galileo is now Splunk Agent Observability". This page keeps the name most buyers still search for.

Braintrust is built around the eval. An Eval() combines data (test cases with inputs, optional expected outputs and metadata), a task (anything from one LLM call to a multi-step agent) and scorers or classifiers. Each run is an immutable, comparable experiment. Around it sit datasets, playgrounds, prompt versioning, tracing, online scoring of production logs, the MIT autoevals scorer library, a GitHub Action for CI, and Loop, an AI assistant that builds scorers, datasets and dashboards. SDKs cover TypeScript, Python, Go, Java, Ruby and C#.

Side by Side

Two rows need a footnote. "Real-time guardrails" for Galileo means either the hosted guardrails on its Enterprise tier or Agent Control, open source and run by you, or centrally managed for enterprise customers. And Braintrust's docs describe online scoring as asynchronous "with no impact on latency", which is also why it cannot stop a response: the score arrives after it.

Where Galileo Is Stronger

• Evaluation you do not have to write. 20+ built-in evals for RAG, agents, safety and security, with agent-specific metrics like Tool Selection Quality and Session Success.
• Cheap, fast judges at scale. Luna-2 small models at $0.02 per million tokens make scoring every call affordable, on Enterprise.
• Judges tuned from a handful of labels. CLHF adjusts a metric from as few as five annotated records.
• A path from eval to guardrail. Hosted real-time guardrails on Enterprise, and Agent Control, open source, which checks LLM and tool inputs and outputs with deny, steer, warn, log or allow decisions.
• Enterprise standing. Named customers include Twilio, Comcast, HP and ServiceTitan, and Galileo is now part of Cisco. On Enterprise it deploys hosted, in your VPC or fully on-prem, where Braintrust's self-host keeps the control plane in Braintrust's cloud.

The Splunk move matters most to one kind of buyer. Splunk said Galileo's technology will be built into Splunk Observability's AI Agent Monitoring, so a company that already runs Splunk for the rest of its observability has a procurement path worth asking about before it evaluates anything else.

Where Braintrust Is Stronger

• Evaluation as code. Scorers and Eval() definitions live in your repository and are reviewed like any other change; autoevals is MIT, so you can read exactly what a Factuality or Faithfulness scorer asks the judge.
• CI as a first-class workflow. The eval-action runs evals in GitHub Actions for node, python or go and posts a live summary comment on the pull request.
• Experiments as records. Every run is immutable and comparable, which makes "did this change make it worse" a lookup rather than an argument.
• Published overage rates. $4 per GB and $2.50 per 1,000 scores on Starter, $3 and $1.50 on Pro, so you can price growth before it happens. Galileo Pro "scales based on number of traces" without a published rate.
• Self-serve to the paid tier. Starter ("Sign up") and Pro ("Get Pro") both start without a sales call, with a monthly price and unlimited users. Galileo Pro is $100 a month billed yearly, and its pricing page routes it through "Book a Demo"; only Galileo Free is self-serve.
• Loop and SDK breadth. An AI assistant for building scorers and datasets, and SDKs in six languages.

Above the entry tier, Galileo's pricing page gives a buyer less to plan with: Pro "scales based on number of traces" without a published rate, and the features that most separate it - Luna-2, hosted guardrails, SSO, VPC and on-prem - are on Enterprise. That is a normal shape for an enterprise vendor. It does mean a team comparing the two on a spreadsheet can fill in every Braintrust cell from the public page and only some of Galileo's.

One Hallucinating RAG Agent, Both Tools

A policy assistant answers employee questions from an HR knowledge base and sometimes invents a policy that does not exist.

1. Galileo: built-in metrics, tuned, then guarded

   Turn on the built-in RAG evals and watch them on production traffic. Have reviewers annotate five or more records to tune the metric with CLHF. On Enterprise, Luna-2 makes scoring every response cheap enough to do. To act on an invented answer at runtime, Galileo points to its Enterprise real-time guardrails, or to Agent Control, which checks LLM outputs and can deny or steer; the open-source version is yours to deploy.

2. Braintrust: dataset, scorer, pull request

   Collect the bad production traces into a dataset and score it with the autoevals Faithfulness scorer plus a custom LLM-as-a-judge scorer for "cites a real policy". Every pull request that touches the prompt or retrieval runs the eval through the GitHub Action. Online scoring watches a sample of production afterwards, and an alert fires on a drop.

3. The difference

   Galileo can act on the answer at runtime, at Enterprise price or with an Agent Control deployment. Braintrust is stronger at making sure the next release does not bring the failure back, and tells you after the fact when production drifts.

Most teams with a hallucination problem need both halves: something to stop the worst answers now, and something to keep fixed answers fixed. The question is which half you buy and which you build.

Pricing and Buying

A worked example at 40,000 agent traces a month, assumptions stated. Galileo Pro covers the volume at $100 a month, paid as $1,200 a year and bought through a demo rather than a sign-up form. Braintrust at the same volume, assuming 40 KB of processed data per trace (1.6 GB) and one scorer on every trace (40,000 scores), fits inside Pro at $249 a month; on Starter it would be 0.6 GB over at $4 plus 30,000 scores over at $2.50 per 1,000, or $77.40 a month, with 14-day retention. Past 50,000 traces Galileo does not publish its rate, so the comparison stops there.

The more important line is the one about guardrails and Luna-2: on Galileo, the features that most separate it from Braintrust sit on the Enterprise tier, so a Pro-versus-Pro comparison understates what a Galileo buyer usually ends up negotiating.