Comparison
Winnow vs Arize
Arize is the closest conceptual competitor, the other platform that takes agent and LLM observability seriously and speaks OpenTelemetry natively. We interoperate on their on-ramp, then add the three things a pure observability layer structurally does not: allocation (multi-armed bandits), a connection to your business metrics, and the compounding offline-to-online loop.
This matrix includes the rows where Arize leads. If we matched their observability depth and stopped there, the comparison would not be worth writing.
The full matrix
Green check: full support. Yellow dash: partial or competing approach. Blank: not available.
| Capability | Winnow | Arize |
|---|---|---|
| Tracing and observability, the shared ground | ||
OTLP / OpenTelemetry-native ingest Both accept standard OTLP spans. Point the exporter you already run at either backend. | ||
LLM-as-judge / online scorers | ||
Agent trajectory and tool-use metrics | ||
Embedding and drift visualization (ML-observability heritage) Arize leads here: deep, mature embedding-drift and multi-modal monitoring from their ML-observability roots. | ||
| Statistical honesty, the throughline | ||
Confidence intervals on every comparison, anytime-valid, plus advanced Bayesian, CUPED, and SRM detection | ||
Explicit "no data" states, never a fabricated zero | ||
| Online experimentation and allocation, what a pure observability layer doesn't do | ||
Online A/B tests on prompts, models, configs | ||
mSPRT (anytime-valid), Welch / Bayesian / CUPED, SRM detection | ||
Multi-armed bandits that shift traffic to the winning arm Winnow shifts traffic to the winning arm under a cost-adjusted reward; Arize reports the numbers. | ||
| The compounding loop and business connection | ||
Offline-to-online delta with a CI on the difference | ||
Production failures promote to golden evals (human-gated) The loop keeps going: every incident widens the offline set, and Winnow carries the chart back into your evals so the library compounds. | ||
Connect quality to your business metrics (revenue, CSAT) via segments and retro enrichment | ||
| Deploy and portability | ||
Self-hosted option | ||
Open by default: cancel and keep the data you collected | ||
Already sending OTLP to Arize? Fan it out to Winnow too.
Winnow shares the OTLP on-ramp. There is no rip-and-replace to evaluate us: add a second exporter endpoint and the same spans land in both. Keep Arize's observability where it is strong, and let Winnow add allocation, stats, and the closed loop on the exact same event stream.
# OpenTelemetry collector: dual export, zero app changes
exporters:
otlp/arize: endpoint: "otlp.arize.com:443"
otlp/winnow: endpoint: "ingest.justwinnow.com:443"
service:
pipelines: { traces: { exporters: [otlp/arize, otlp/winnow] } }Honest positioning
Pick Winnow when
- You want to act on observability by allocating traffic with bandits
- Online experiments with real sequential stats are part of how you ship
- You want production failures to compound into your golden eval set
- Connecting model quality to business metrics matters to your team
Pick Arize when
- Deep embedding / drift visualization from mature ML-observability heritage is load-bearing
- Broad multi-modal model monitoring across classic ML + LLM is the whole job
- You need their established enterprise observability catalog today
- You are not running online experiments or allocation and do not plan to
Observe, allocate, and prove it on the traffic you already have.
Interoperate on OTLP, put a CI on the delta, and let cost-per-correct-answer pick the winner.