Arize vs Galileo vs Braintrust

By Camellia · Updated Nov 3, 2026

If you're shipping an LLM feature or an agent to real customers, you've probably noticed that "can we see what it did" and "was the answer any good" are two different purchases. Arize, Galileo, and Braintrust all sell some version of both, but they start from different places. Arize grew out of ML monitoring and has an open-source tracing project, Phoenix, next to its commercial product. Galileo started with evaluation and runtime guardrails. Braintrust built its product around the experiment: run a test set, score it, compare it to last week's version, and block the release if it got worse.

The other thing that changed is who owns two of them. Both Arize and Galileo were acquired in 2026, and the buying questions now include "what happens to this product inside a bigger company." This comparison is written for the person who has to pick one and defend the choice: a head of engineering, an AI/ML lead, or a CTO who needs evals tied to releases. For the wider field, start with the LLM observability roundup or the agent observability roundup.

Key Facts

• Dynatrace announced a definitive agreement to acquire Arize on August 13, 2026 for $915 million, and its completion post, dated October 1, 2026, says the acquisition "has completed."
• Cisco announced its intent to acquire Galileo on April 9, 2026, and a May 22, 2026 update on that post says the acquisition is complete. Splunk now lists the product as Splunk Agent Observability.
• Braintrust's published plans are Starter at $0 a month, Pro at $249 a month, and Enterprise on custom pricing.
• Arize AX Pro costs $50 a month and includes 50k spans a month, with no overage rate stated on the pricing page.
• Splunk's pricing page lists Agent Observability from $100 per month per 1.2 million spans annually, billed annually, with 500 million Luna tokens a year included.
• Galileo's docs claim its Luna-2 evaluator costs $0.02 per million tokens at 152ms average latency, against $2.50 and 3,200ms for GPT-4o as a judge. That is the vendor's own comparison, not an independent benchmark.

TL;DR

What Each One Is Built For

Arize sells two things under one name. Phoenix is the open-source, self-hosted tool: tracing based on OpenTelemetry, LLM-based evaluation, datasets and experiments, and a prompt playground, per its GitHub README. AX is the commercial platform on top, hosted as SaaS or self-hosted on Enterprise. The draw is that you can start on Phoenix for free and move to AX without changing the instrumentation story.

Galileo is the one that treats the judge itself as a product. Its docs describe an observability, evaluation, and guardrail platform for GenAI and agentic applications, and Cisco's announcement frames it as real-time observability and guardrails for multi-agent systems. The distinctive piece is Luna, a family of small evaluator models meant to score outputs more cheaply and quickly than prompting a frontier model every time.

Braintrust treats evaluation as the main loop. Per its evaluation docs, an eval has three parts: data, a task, and scorers. Scorers can be built-in autoevals, LLM-as-a-judge prompts, or custom code, and experiments are immutable records you can compare over time. You prototype in a playground, promote a good configuration to an experiment, run it in CI/CD, and feed interesting production traces back into datasets.

If you're still deciding whether you need evaluation tooling at all, read how to evaluate AI agents first. And if tracing is the bigger gap, general AI observability is the broader frame.

Ownership After 2026

This is the part older roundups get wrong, so it's worth being precise about dates. Signing and closing are different events.

Braintrust is the only one of the three that wasn't acquired in this window. That doesn't make it safer by itself, but it does mean one fewer roadmap decision is being made inside a larger platform company. For Arize, Dynatrace's completion post tells existing customers to talk to their account team about what the deal means for them, so pricing continuity is an open question to ask before you sign past a pilot. For Galileo, the move is already visible: the product is now sold on Splunk's pricing page rather than its own.

Evaluation Depth

The honest read: if the question is "did this prompt change make things worse before we shipped it," Braintrust is built around exactly that. If the question is "stop a bad answer before the customer sees it, cheaply enough to run on every request," Galileo's Luna and guardrails are aimed at that. If the question is "give me tracing and evals without a license fee to start," Phoenix is the free entry point. One caution on Luna: the cost and latency numbers come from Galileo's own docs, and the docs also say Luna-2 is available only on Galileo's Enterprise tier, while Splunk's pricing page lists Luna tokens with the entry price. Confirm which applies to you before you build a cost model on it.

Tracing and OpenTelemetry

OpenTelemetry support is the lock-in question: if traces come in as standard OTLP, you can change backends without re-instrumenting your app.

All three accept OpenTelemetry traces, so none of them is a dead end on instrumentation. Arize is the only one where OpenTelemetry and OpenInference are the founding design rather than an ingestion path added later, which matters if you want a free local backend while developing.

Pricing: What Each Vendor Publishes

Prices below come from each vendor's own pricing page. The billed unit differs for every vendor, so the dollar figures aren't directly comparable.

Braintrust's page shows monthly prices and states no annual option. Arize's pricing page lists AX Pro as $50 per month. Splunk bills its entry price annually.

To make the units concrete, take a team running 100,000 scores and 8 GB of processed data a month on Braintrust Pro. That's the $249 plan fee, plus 50,000 extra scores at $1.50 per 1,000 ($75), plus 3 extra GB at $3 per GB ($9), for $333 a month before any model usage beyond the $100 credit. You can't build the same calculation for Arize AX or Splunk past their entry volumes, because neither page publishes a rate beyond what's included. That gap is itself a buying signal: at real volume, expect a sales conversation with both.

Self-Hosting and Licensing

Phoenix's Elastic License 2.0 lets you read, run, and modify the code, but it is not the same legal position as Apache or MIT. Have legal read the LICENSE file before you build a product on top of it. If keeping prompts inside your own network is the requirement, the free path is Phoenix, and the commercial paths are enterprise conversations on all three. An LLM trace usually contains the full prompt and completion, so a trace store is a data-governance decision, not just a tooling one.

When Arize Is the Right Call

Pick Arize when you want to start free with real tracing and evals, keep the option to self-host, and eventually have a commercial upgrade path under the same brand. It also suits teams that already run Dynatrace, since Dynatrace says it plans to connect Arize's workflows into its platform.

When Galileo Is the Right Call

Pick Galileo when the judge is the bottleneck: you need to score a large share of live traffic, or enforce guardrails in the request path, and a frontier-model judge is too slow or too costly. It is also the natural fit for organizations already buying Splunk or Cisco products.

When Braintrust Is the Right Call

Pick Braintrust when your pain is shipping regressions. If prompt and model changes happen weekly and nobody can say whether the new version is better, an experiment-first tool pays for itself fast. It also helps that its plans have unlimited users, projects, datasets, and experiments.

Decision Framework

What to Do Next

Take one LLM app that already has real traffic and instrument it with two of these three for two weeks. Collect 50 to 100 real cases, including some that failed, and build one regression eval from them. Then compare what each tool made easy: seeing the failing step, scoring it, and rerunning the test after a fix. Price the result at your own monthly spans or scores, not the free tier.

If your app is a retrieval pipeline, the RAG tools roundup covers what those traces are recording, and if the app keeps state between sessions, agent memory tools covers that layer. If requests pass through a gateway before the model, MCP gateways is worth reading alongside this one.