Arize Phoenix vs. Braintrust: Which stack fits your LLM evaluation & observability needs?

TLDR: Why teams choose Braintrust over Arize

Teams building production AI choose Braintrust because it's the only platform that connects the complete development loop -- from production traces to evals and back. While Arize Phoenix focuses on observability after the fact, Braintrust turns every production trace into a test case, catches regressions before users do, and lets PMs and engineers ship iterations in minutes, not days.

Ship AI products as fast as the industry evolves. Braintrust is an AI development platform that makes AI measurable, continuous, and fast.

Arize Phoenix is a strong observability tool, but it doesn't close the loop. Self-hosting requires infrastructure overhead, and Phoenix Cloud still treats evals as disconnected from production data -- forcing teams to build custom pipelines to improve from real-world usage.

The core philosophy difference

Braintrust is an AI development platform built on the foundational belief that evals are product design, not an afterthought. We turn production data into better AI products through the complete development loop -- automatically converting traces to test cases, running CI/CD quality gates, and enabling PMs and engineers to iterate together in one platform.

From vibes to verified.

Arize Phoenix focuses on observability and tracing. Phoenix is available as open-source (requiring self-hosting) or as Arize AI's managed cloud service. It excels at showing you what happened in production, but doesn't connect those insights back into your development workflow.

While Arize helps you understand your AI system, Braintrust helps you continuously improve it.

Why teams choose Braintrust over Arize AI

Proven results from leading companies

Notion: Increased from fixing 3 issues per day to 30 issues per day. A 10x productivity improvement.

Zapier: Improved AI products from sub-50% accuracy to 90%+ within 2-3 months.

Coursera: 90% learner satisfaction rating with their AI Coach and 45× more feedback than manual processes.

The complete loop: production → evals → production

Here's the difference: When an AI interaction fails in production with Braintrust, it automatically becomes a test case. Your next eval run catches whether you fixed it. With Phoenix, you see the failure in traces, then manually export data, set up custom eval infrastructure, and build your own pipeline to close the loop.

Braintrust's complete development loop means:

1. Production traces are automatically logged with full context
2. One-click dataset creation turns any trace into a test case
3. CI/CD quality gates prevent regressions before deployment
4. Every deployment shows exactly what improved or regressed and why

Phoenix excels at step 1 -- showing you what happened. But without steps 2-4, teams build on vibes, hoping their changes improved quality without systematic verification.

Model-agnostic experimentation with the AI proxy

Experimenting with new models shouldn't mean rewriting code. Braintrust's AI proxy gives you access to 100+ models from OpenAI, Anthropic, Google, and more through a single OpenAI-compatible API. Switch providers instantly, automatically cache results to reduce costs, and log everything for analysis -- all without changing your application code.

# Drop-in replacement for OpenAI API with instant A/B testing
import openai

client = openai.OpenAI(base_url="https://api.braintrust.dev/v1/proxy", api_key=YOUR_BRAINTRUST_API_KEY)

# All calls automatically traced and ready for comparison
response = client.chat.completions.create(model="gpt-4", messages=[{"role": "user", "content": "Hello world"}])

Phoenix doesn't provide a proxy layer -- it's purely an observability tool. You handle model routing yourself, then analyze the results in Phoenix's tracing UI.

Ship iterations in minutes with the Playground

When you need to test a prompt change or compare model outputs, waiting for CI/CD pipelines or deployment cycles kills momentum. Braintrust Playgrounds let you run evals in real-time, compare variations side-by-side with diff mode, and share results via URL -- all without writing code.

The difference: PMs can iterate independently without waiting on engineering. When they find a winning prompt, developers use npx braintrust push to sync it straight into code. This code-to-UI pipeline means product and engineering work in the same environment, accelerating decision-making across the team.

Phoenix has added playground functionality, but it's disconnected from the complete loop. You can test in their UI, but there's no systematic way to convert those experiments into production improvements or regression tests.

Know exactly what changed with quality scores on every deployment

The worst feeling in AI development: deploying a change and wondering if it actually improved quality or just shifted failure modes around. Braintrust's CI/CD integration means every pull request shows quality scores before merge, and every deployment shows exactly what improved or regressed.

Catch regressions before users do. Set quality gates that prevent degraded prompts from reaching production. When something does slip through, the complete loop means that production failure is already a test case for your next iteration.

Phoenix shows you traces after deployment. Braintrust prevents bad deployments in the first place.

Performance that scales with your team

When evaluating thousands of runs or analyzing extensive production datasets, platform responsiveness becomes critical to maintain development velocity. Braintrust is architected for sub-second response times even with large-scale evaluation histories, ensuring teams can iterate without waiting for UI updates or saves to complete.

Phoenix's web interface can experience performance degradation with larger datasets, particularly when working with extensive trace histories or running large-scale evaluations. Teams report UI responsiveness issues that create friction during critical debugging sessions or comprehensive testing cycles.

Additionally, Braintrust automatically generates monitoring dashboards for all scorers without configuration overhead. Phoenix requires setting up individual monitors for each scoring function, adding administrative steps that slow down the evaluation workflow -- especially when iterating rapidly on multiple custom scorers.

No artificial limits on experimentation

Braintrust playgrounds support unlimited evaluation rows, letting teams test comprehensive datasets without arbitrary constraints. Phoenix limits playground testing to 100 rows, forcing teams to run abbreviated tests that may miss edge cases or require additional tooling for full validation.

When you're debugging a production issue or running regression tests across your entire evaluation suite, these limits create real friction. Braintrust removes these constraints so you can test at the scale your application requires.

The complete development loop in action

Here's what the loop looks like with Braintrust:

1. Production: Your AI app runs in production with automatic logging
2. Capture failures: Users report issues, or you spot concerning patterns in monitoring
3. One-click test cases: Convert those production traces to dataset rows instantly
4. Rapid iteration: Test fixes in the Playground, compare side-by-side with the original
5. Quality gates: Run evals in CI/CD to verify the fix doesn't break other cases
6. Deploy with confidence: See quality scores on every commit, know exactly what improved
7. Continuous monitoring: Production traces feed back into your eval suite automatically

With Phoenix, you're manually stitching these steps together across multiple tools. That means slower iteration, more room for gaps, and teams building on vibes instead of verified quality improvements.

Feature comparison: Arize Phoenix vs Braintrust

Decision framework: Arize vs Braintrust

Choose Braintrust when you:

✅ Ship AI products to real users and need to know exactly what changed with every deployment

✅ Want the complete loop -- production traces automatically become test cases

✅ Need PMs and engineers working together in one platform instead of siloed tools

✅ Value speed -- ship iterations in minutes, not days

✅ Want to catch regressions before users do with CI/CD quality gates

✅ Need model flexibility without vendor lock-in or code rewrites

✅ Are building on production data and want to turn every trace into continuous improvement

✅ Don't want to build custom eval infrastructure -- you want to ship product, not maintain pipelines

Choose Arize Phoenix when you:

✅ Only need observability and are comfortable building your own eval-to-production pipeline

✅ Have DevOps resources to self-host and maintain infrastructure (Phoenix OSS)

✅ Don't need PM/eng collaboration -- engineering owns the entire AI workflow

✅ Are primarily research-focused where production deployment isn't immediate

✅ Prefer building custom tooling over using an integrated platform

✅ Don't need CI/CD integration for AI quality gates

Why Braintrust is the best choice for shipping AI products

Braintrust = The complete development loop. From vibes to verified.

Arize Phoenix = Observability after the fact.

Here's the fundamental difference: Phoenix shows you what happened in production. Braintrust turns what happened into systematic improvement.

Most production AI teams choose Braintrust because we're the only platform connecting evals to production and back. Every trace becomes a test case. Every deployment shows quality scores. PMs and engineers work together instead of throwing requirements over the wall.

Companies like Notion (10x faster issue resolution), Zapier (sub-50% to 90%+ accuracy), and Coursera (90% satisfaction ratings) prove that closing the loop drives measurable business results. They're not building on vibes -- they're shipping verified quality improvements, fast.

Frequently asked questions: Arize Phoenix vs Braintrust

Q: Which is better Braintrust or Arize AI for LLM evaluation?

Braintrust is the only platform that connects the complete development loop, turning production traces into test cases automatically and catching regressions before users do. Companies like Notion, Zapier, and Coursera have achieved 10×-45× productivity improvements because Braintrust enables systematic improvement from real-world usage.

Phoenix focuses on observability -- showing you what happened in production. But without the loop back to evals and forward to deployment gates, teams are building on vibes.

Q: What makes Braintrust superior to Arize AI for rapid iteration?

Ship iterations in minutes, not days. With Braintrust, PMs test prompts in the Playground, developers sync changes with npx braintrust push, and CI/CD quality gates catch regressions before merge. Phoenix requires manual export of traces, custom eval infrastructure, and engineering effort to stitch together the workflow.

Q: How does Braintrust solve the dataset management problems of Arize AI?

When something goes wrong in production with Braintrust, one click turns that trace into a dataset row. Your next eval run verifies the fix. Datasets stay in sync with production automatically through the complete loop. With Phoenix, you manually export traces and build custom pipelines to convert observability into actionable evals.

Q: Why do teams prefer Braintrust over Arize AI for production AI at scale?

Braintrust ties evaluation, monitoring, and dataset updates into one loop, so real-world failures immediately inform the next round of testing. Phoenix provides strong tracing but requires separate engineering steps to act on those insights. The complete loop means every deployment shows quality scores and you catch regressions before users do.

Q: Is Braintrust's managed platform better than Arize's open-source and cloud approaches?

Yes. Braintrust's hosted platform minimizes infrastructure overhead while enabling the complete development loop. Phoenix OSS offers flexibility but demands DevOps effort to self-host, and Arize Cloud focuses on observability without connecting production data back to evals and forward to CI/CD gates.

Q: What automated capabilities make Braintrust a better choice than Arize AI?

Braintrust automates the complete loop: production traces automatically become test cases, CI/CD integration prevents regressions before merge, and monitoring dashboards generate for all scorers without configuration. Phoenix requires custom engineering to build these pipelines yourself.

Q: How does Braintrust's production monitoring surpass Arize AI?

Braintrust unifies monitoring and evaluation in the complete loop. The same evals that run in CI/CD run continuously against live traffic. When something fails in production, it's already a test case for your next iteration. Phoenix surfaces observability, but translating insights into actionable improvements requires separate engineering work.

Q: How easy is it to migrate from Arize AI to Braintrust?

Braintrust's SDK integrates with existing code in minutes. Production traces start flowing immediately, and you can create your first eval from those traces with one click. Most teams see productivity gains within days because the complete loop is straightforward to set up as one platform, not disconnected tools.

Q: How does Braintrust handle custom evaluation logic compared to Arize Phoenix?

Braintrust executes custom Python and Node.js code directly within the evaluation pipeline, enabling rapid iteration without deployment overhead. This connects directly to the complete loop -- your custom scorers run in CI/CD gates and production monitoring automatically. Phoenix requires running evaluation code in your own environment via SDK, adding infrastructure complexity.

Q: Why does platform performance matter for LLM evaluation?

When running comprehensive evals or analyzing production datasets, UI responsiveness directly impacts how fast you can iterate. Braintrust maintains sub-second performance regardless of dataset size. Phoenix's UI can experience slowdowns with larger datasets that create friction during critical workflows, slowing down the development loop.

Q: Can Braintrust integrate with our existing annotation tools?

Yes. Braintrust supports embedding custom HTML and iframe-based tools directly in the evaluation interface. This keeps domain experts working within the complete development loop instead of switching between multiple tools to convert annotations into systematic improvements.

Ready to ship AI products with confidence?

Sign up for Braintrust to experience the complete development loop -- from production traces to evals and back. Stop building on vibes. Start shipping verified quality improvements, fast.