Production scale from day one. Stable architecture with the benchmarks to prove it. Agent-first workflows. An AI engineering agent that closes the loop.
Architecture that matches the claim.
Braintrust
Marketed for production. Built for dev.
Strong prompt iteration. Real dev-loop value. But when you’re running multiple agents at scale, costs climb fast and stability matters. That’s where the architecture shows its limits.
What AI builders are saying
from field interviews
Deeper observability
“The granularity of the agent traces and the way we are able to see each step – like the agent graph and the agent timeline, the decision path and all agent tool-calling paths – that’s exciting and stands out.”
Anonymous Engineer
Large global financial institution
Proven architecture
“Arize is just further ahead across the board. We had 200 people on Braintrust with 16 LLM applications. We moved away due to runaway costs and server stability issues at scale.”
Anonymous Engineer
Productivity SaaS platform
Built for production agents
“We needed a solution that works across experimental workflows straight through to production – Arize has a much more robust production suite once you’re running multiple agents.”
Anonymous Engineer
Large ecommerce platform
Where the architecture diverges
Shallow roots show at scale
performance
Retrofitted for production
Braintrust’s schema is fixed: input, output, metadata. All your trace and span context gets dumped into those buckets. Query something specific? Parse it out yourself.
A purpose-built datastore for agent eval telemetry is a no-brainer. They built Brainstore to catch up. We built adb to handle the schema complexity of real agent production traces.
If it’s useful, we can run a quick ingestion + query test on one of your largest datasets.
Agent evals
Surface-level agent evaluation
Braintrust traces agent runs, but Arize AX goes deeper.
Path evals measure if your agent took the optimal route. Convergence evals catch loops and unnecessary backtracking. Session evals track coherence across multi-turn interactions.
Braintrust captures what happened; for teams debugging why an agent made a specific choice, the eval depth isn’t there.
ENTERPRISE
The VPC compromise
The whole point of VPC is control. Braintrust’s hybrid model defeats that: control plane and auth live in their cloud, and version conflicts force you onto their update schedule.
Fall behind and your deployment breaks. Arize AX deploys to one Kubernetes cluster. No cloud dependencies. No forced cadence. Your data, your rules.
AGENT EXPERIENCE
Following at a distance
Braintrust’s Loop iterates only on what you point it at. We shipped the first AI engineering agent for observability, then wrote the guide on how to build one.
Alyx finds (and fixes) what you didn’t know to look for.
When Braintrust is the right call
If your team is focused on prompt iteration and offline evaluation (building loops), and you’re not yet running agents in production at scale,
Braintrust’s dev-loop tooling is solid.
Where Arize AX stands out
Infrastructure at Scale
Trillions of data points. No tradeoffs.
Arize's purpose-built AI database (adb) handles trillions of data points with up to 100x cost advantage over traditional observability platforms. Open formats, no vendor lock-in.
Production Monitoring
First trace to full production visibility
Continuous real-time monitoring with automated alerting. Surface regressions before users notice. One system from first trace through enterprise scale.
Alyx
Find what you didn't know to look for
Alyx is a Cursor-like AI engineering agent that surfaces failure clusters, drift signals, and anomalous reasoning paths automatically. Closes the loop before you know it's open.
Enterprise VPC Deployment
One deployment. Total control.
One Kubernetes cluster. No outbound calls to third-party servers. Predictable K8s-native costs - no Lambda surprises. Your data, your infrastructure, your rules.
No split-brain risk
Unified data and control plane within your VPC. No outbound calls to third-party servers required.
Predictable costs
K8s-native architecture means fixed, plannable infrastructure costs. No Lambda invocation surprises as you scale.
Simpler operations
One Kubernetes cluster to manage. No split infrastructure, no multi-cloud coordination.
AI evolved from ML. So did we.
We’re Jason and Aparna.
We built the foundational ML infrastructure at Uber, Apple, and TubeMogul.
Before LLMs existed, we watched models break in production with nothing to fix them. So we started Arize to fix it.
Our mission since 2020: make AI work.
ML first. Then LLMs. We shipped the first open-source library for LLM evaluation: Phoenix.
Now agents.
That’s Arize AX — the Agent Experience.
