Arize AX or Braintrust for tracing, testing and evaluating an LLM app

One path: bad answer, test case, evaluation, review

Braintrust describes tracing prompts, responses and tool calls, then running evals on versioned datasets scored by LLMs, code or humans. Its homepage lists a one-click action that turns production traces into eval datasets, and annotation views that teams shape to their own review workflow.

Arize frames the same loop as observe, evaluate, learn: span, trace and session evals, plus a playground to test prompts before deployment. Its pricing table lists datasets, experiments, prompt versioning and trace replay in the playground. Booking.com says it builds experiment datasets directly from production data. So both products have datasets; what the pages cannot tell you is how smooth that step feels in daily use.

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What the customer workflows show, and what they do not

Dropbox describes three tiers in Braintrust: 150 smoke tests before merge, a suite of more than 10,000 tests after merge, and an LLM judge scoring sampled production queries against the same rubric, with dashboards and alerts. Thumbs up or down on a response becomes a trace that feeds new test sets. Box describes product managers writing test cases and rubrics, nightly programmatic runs, and a rule that the agent had to score above 80% on a set of roughly eighty questions before internal release; a golden set of about 330 questions then defined general availability.

PagerDuty describes the production side in Arize: Slack feedback is tied to the span ID of the response, LLM judges score tool choice, parameters and relevance, and a breached quality threshold opens a low-urgency page and a Jira ticket. Booking.com routes traces through a collector that samples them and removes sensitive passages before export to Arize; there, lower scores on later chatbot turns led the team to long attraction links fed back into the context, which it fixed before launch.

The pattern is a difference of emphasis, not of capability: the Braintrust stories centre on pre-release thresholds owned by product and engineering, the Arize stories on live monitoring tied to on-call routines. Coursera also describes online monitoring with alerting in Braintrust, so neither tool is limited to one phase. These are different companies reporting their own results; they do not rank the tools.

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Traces and integrations: check your own stack

The lists overlap on LangChain, LangGraph, LlamaIndex, CrewAI, DSPy and Pydantic AI. Braintrust's list adds Vercel AI SDK, Mastra, OpenTelemetry and Pytest, and its documentation counts about 90 integrations plus SDKs for Python, TypeScript, Go, Ruby and C#. Arize's list adds Haystack, Dify, Flowise, Apache Airflow, Claude Code and Cursor; its documentation also covers Claude Agent SDK and OpenAI Agents SDK, and notes that you instrument both the agent framework and the model provider.

Arize builds on OpenInference, which it presents as an open standard on top of OpenTelemetry, and offers Phoenix, an open-source local-first tool. Braintrust also lists OpenTelemetry support, so trace portability does not separate them on paper.

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Human review and plan units are not the same currency

Arize lists human annotations and labeling queues as unlimited on Free, Pro and Enterprise. Its limits sit elsewhere: 25k spans per month on Free and 50k on Pro, 1 GB and 10 GB of ingestion, 15 and 30 days of retention, with Pro at $50 per month.

Braintrust Starter includes one human review score per project; Pro makes them unlimited. It bills by processed data (1 GB on Starter, 5 GB on Pro), scores (10k and 50k per month), model credits and retention (14 and 30 days), with Pro at $249 per month. Spans and gigabytes are different meters, so no price comparison is possible from the pages: estimate both with your own traffic and sampling rate.

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Hosting and data handling

Arize runs Free and Pro as SaaS and offers SaaS or self-hosted on Enterprise; its table shows data regions in the US, EU or Canada on every plan. For Braintrust, the pricing page puts on-prem or hosted deployment on Enterprise, and the homepage describes a hybrid mode where the data plane runs on your infrastructure.

Braintrust states SOC 2 Type II and makes a data processing agreement available. Its agreement says Pro customers can place the data plane in the EU, while control-plane identity data may go to the US, and that the optional Topics feature may send selected eval data to subprocessor models. Its trust center lists OpenAI, Anthropic and Google as optional models for Loop. For Arize, check its trust center for what applies to your plan.

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A test to run before you decide (editorial suggestion)

Take a few real bad answers from your app and push them through both free plans. Time how long it takes to go from the trace to a saved test case, whether a non-engineer reviewer can label without help, whether your framework's tool calls appear in the trace, and which meter you hit first at your volume. The pages cannot answer these questions.

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