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Arize Signals

What teams building AI features on Arize report, with every finding tied to its evidence.

Read the findings

What the evidence says

Whose sources count

Practitioner sentiment, teams and leaders

+26 Positive from 27 sources that take a side

How this figure is made

Positive overall signal, net 26 points

Each from a different author and a different discussion. Small sample. Run a pilot on one production app to measure which failures it surfaces and how long tracing them takes.

Analyzed
78 total sources
Top praise
Trace-Level Debugging (10 of 11 sources) See praise evidence
Top concern
Scale & Self-Host Operations (6 of 6 sources) See concern evidence

Feb 2025 to Oct 2026.

64 sources from practitioners, by month and sentiment.

Team accounts lean positive, with 11 positive, 12 mixed and 4 negative among 27 counted. PagerDuty, Wayfair, Booking.com and Uber run Arize AX in production, while Elastic, Apify and bbv left Phoenix in 2026. All 6 discussing self-hosting praise it. All 6 discussing volume report problems. Six compare Langfuse. Platform decisions split between 9 keeping or choosing Arize and 6 moving to Langfuse, Elasticsearch, or no tracer.

Positive, mixed and negative sources

The score counts each source once, as the split does. See How the numbers work.

See the evidence

Below: 3 praise themes, 1 concern, 1 point of agreement and 1 disagreement.

Findings

Counted for Teams and technical leaders, from 29 sources. Change group

What practitioners praise

Every capability practitioners describe positively, ranked by how many sources say so. Open one to read who said it.

  1. Trace-Level Debugging10 of 11 sources on it praise itArize shows each model and tool call, retrieval, and agent step. Engineers use inputs and outputs, latency, and token counts to locate failures.

    Counted for Teams and technical leaders.

    Positive, 10

    1. Positive

      Moran Beladev, Booking.com (with Hadas Harush and Chana Ross)

      Introducing AgentHub: Booking.com Platform for Modular Travel AI AgentsEngineering blog post, Booking.com ML & DS Blog

      Booking.com logs every AgentHub interaction with OpenTelemetry, covering prompts, context, tool calls, and agent transitions. It stores the traces in its internal systems and in Arize, which the team uses for deep debugging and monitoring.

      Published Oct 2, 2026self-described: AgentHub, Booking.com's platform that gives product teams configurable travel agents with shared monitoring

    2. Positive

      Jai Chopra, Sr. Product Manager, Uber (with Soumya Gupta, Sr. Software Engineer)

      Building Closed-Loop Evals for a Multimodal Agent at Uber ScaleConference talk with transcript, AI Engineer World's Fair 2026

      Uber logs every stage of its food-photo agent as one flat record per case, so technical and non-technical staff can diagnose single cases and roll results up in aggregate. Chopra names Arize as the observability platform the team uses.

      Published Jul 24, 2026Uber's computer-vision team, running an agent that edits Uber Eats merchants' food photos

    3. Positive

      Amir Bitaraf, Booking.com (with Shahaf Veber)

      From Traditional ML to AI Agents: How Booking.com Scales AI Observability With Arize AIEngineering blog post, Booking.com ML & DS Blog

      Booking.com sends traces from its agents and prediction logs from its ML models into Arize through a central OpenTelemetry collector that redacts PII. An Arize latency monitor caught an agent running without a service tier, and comparing token counts in Arize exposed context bloat in an itinerary agent before it reached production.

      Published Jul 10, 2026self-described: Booking.com's shared observability platform for traditional ML models and LLM agents; the authors describe a partnership with Arize

    4. Positive

      Peihan Huo, Hanyue He, and Ioana Boier of NVIDIA, with Yang Shen of Huatai Securities

      Automating and Optimizing Financial Signal Discovery with Multi-Agent SystemsEngineering blog post, NVIDIA Technical Blog

      The team traces its long-running signal discovery agents in Phoenix to watch LLM reasoning and token use in real time and to find whether a bottleneck sits in the model's logic.

      Published May 21, 2026Multi-agent signal discovery workflow on NeMo Agent Toolkit for quantitative research

    5. Mixed

      Valentin Velev, Data Scientist, Codify

      Evaluating AI Agents with DeepEval and Arize Phoenix: Lessons from Our Integration JourneyEngineering blog post, Codify blog

      Codify traces its agents in Phoenix and scores them with DeepEval. Phoenix helps diagnose failures, but past experiment results needed manual database queries, concurrency above 1 errored, and docs were thin or outdated.

      Published Oct 6, 2025Codify AG, Swiss software and AI consultancy, evaluating its own agents

    6. Positive

      Jeronim Morina, AI and MLOps Engineer, AXA

      Optimizing LLMs in Insurance with DSPy: Beyond Manual TuningConference talk with transcript, AI Engineer World's Fair 2024, video published February 2025

      The AXA team logged no traces at first and could not see how its LLM calls fit together. Adding open-source Phoenix helped a lot in showing which calls were bundled together and what ran behind the framework's abstractions.

      Published Feb 16, 2025AXA Germany's data team, building a DSPy pipeline on its internal Secure GPT platform

    7. Positive

      PagerDuty Engineering team

      PagerDuty for AI: How the SRE Agent Triages AI IncidentsEngineering blog post, PagerDuty engineering blog

      PagerDuty's Insights Agent has several Arize monitors that open a PagerDuty incident when breached. During triage the team's SRE Agent filters the Arize project for the offending traces and summarizes the eval explanations before it proposes prompt changes. The team notes that judge evals can be noisy when few traces are scored.

      Published Jul 21, 2026self-described: PagerDuty's Insights Agent, a chat assistant for operational questions, watched by Arize monitors

    8. Mixed

      robbert-gijsels, Developer (issue author)

      [ENHANCEMENT]: Support attaching images to non-LLM spans (TOOL/CHAIN) without faking LLM message attributesGitHub issue

      Their team traces an OCR and vision pipeline in Phoenix, but images render only on LLM spans, so they fake LLM spans to see them.

      Published Jul 15, 2026self-described: Team tracing a computer-vision pipeline with OpenTelemetry and OpenInference

    9. Mixed

      Total_Listen_4289, Reddit user, r/LLMDevs

      Spent 3 months evaluating Galileo, Arize, Langfuse and LangSmith for production LLM monitoring. Notes for anyone doing the same.Reddit post

      After three months comparing Galileo, Arize, Langfuse, and LangSmith, the team chose Langfuse. It found Phoenix good for tracing and easy to run locally, but the full Arize platform aimed at larger ML teams.

      Published May 30, 2026self-described: Small team shipping agents, evaluating observability for production

    10. Positive

      Farshid Bahrami, Wayfair (with Atharva Deo, Roger Bock, Ying Jiang, and Luming Wang)

      Flag Early, Fix Faster: Real-Time Intervention for Supplier Transfers with WilmaEngineering blog post, Wayfair tech blog

      Wayfair's Wilma clears about 65% of supplier-transfer tickets; besides human feedback and offline evals, Arize gives trace-level visibility, rapid debugging, and alerts on metric shifts.

      Published Jan 15, 2026self-described: Wayfair's Wilma, a LangGraph agent that flags supplier transfer tickets for associates

    Mixed or negative, 1

    1. Mixed

      Hamel Husain, former machine learning engineer at Airbnb and GitHub, guest author

      A pragmatic guide to LLM evals for devsNewsletter guest post, The Pragmatic Engineer

      Shows a NurtureBoss agent trace in Arize Phoenix. Calls generic tools such as Arize a decent start for reviewing data, but they proved cumbersome for the team's context, so it spent a few hours building its own viewer.

      Published Dec 2, 2025self-described: NurtureBoss, an apartment leasing assistant, reviewing agent traces

    Open them in the record
  2. Free, Simple Self-HostingAll 6 sources on it praise itTeams run Phoenix from one container without a license fee. Trace data stays in their infrastructure.

    Counted for Teams and technical leaders.

    1. Mixed

      bathon, Reddit commenter on r/mlops

      Self-hosted Phoenix and Langfuse side by side over OTLPReddit comment, r/mlops

      The commenter's team runs self-hosted Phoenix and Langfuse in its projects, publishing every call, piece of feedback, tool call and agent-to-agent call over OTLP gRPC, with custom model pricing on both. Langfuse is better for evals, dataset building and prompt versioning; Phoenix is about as capable, and choosing it is personal preference.

      Published Oct 1, 2026self-described: Team projects with self-hosted models, tracing every call, tool call and agent-to-agent call

    2. Mixed

      ToWorld, Developer (discussion author)

      Can the image dependencies for an offline/air-gapped Phoenix deployment be simplified?GitHub discussion

      Phoenix's container build pulls four external images and a WASM binary that an air-gapped site must mirror; their team found the PyPI wheel works and asked if that is supported.

      Published Sep 9, 2026self-described: Team deploying Phoenix in a fully air-gapped environment

    3. Mixed

      mashkovd, Developer on mctl's platform team

      spike(observability): compare Traceway, Tempo, Langfuse, Phoenix, SigNoz and Grafana Agent ObservabilityGitHub issue

      Rates Phoenix as self-hostable with strong datasets, evals, and experiments, but flags its source-available license and fit as an eval specialist beside a general trace store.

      Published Aug 26, 2026mctl agent platform choosing an OTLP backend

    4. Mixed

      Total_Listen_4289, Reddit user, r/LLMDevs

      Spent 3 months evaluating Galileo, Arize, Langfuse and LangSmith for production LLM monitoring. Notes for anyone doing the same.Reddit post

      After three months comparing Galileo, Arize, Langfuse, and LangSmith, the team chose Langfuse. It found Phoenix good for tracing and easy to run locally, but the full Arize platform aimed at larger ML teams.

      Published May 30, 2026self-described: Small team shipping agents, evaluating observability for production

    5. Mixed

      oginskis, Developer (discussion author)

      Slow trace and span search performance when searching by spans.attributesGitHub discussion

      At about 100,000 LLM interactions a day on Kubernetes and Cloud SQL, filtering traces by metadata such as app or team is slow over a 24-hour window.

      Published Feb 4, 2026self-described: Team self-hosting Phoenix on Kubernetes with Cloud SQL PostgreSQL, about 100,000 LLM interactions a day

    6. Positive

      jkisiel, Hacker News commenter

      Comment on: LLM Observability in the Wild, Why OpenTelemetry Should Be the StandardHacker News comment

      Evaluated many tools for a small startup in early 2025; Phoenix was the only one that worked out of the box and used OpenTelemetry the right way.

      Published Sep 28, 2025self-described: Small startup choosing its LLM observability stack

    GILD's fact checks, which don't count as sources

    Checked Oct 12, 2026.Phoenix documents self-hosting with no license fees, usage limits, or feature gates. It runs on Docker, Kubernetes, or Helm, with SQLite by default and PostgreSQL 14 or later recommended for production. Phoenix: Self-hosting

    Open them in the record
  3. OpenTelemetry & OpenInference5 of 6 sources on it praise itOpenInference conventions and auto-instrumentation produce portable OpenTelemetry spans. Critical reports describe problems with those conventions or instrumentors.

    Counted for Teams and technical leaders.

    Positive, 5

    1. Mixed

      bathon, Reddit commenter on r/mlops

      Self-hosted Phoenix and Langfuse side by side over OTLPReddit comment, r/mlops

      The commenter's team runs self-hosted Phoenix and Langfuse in its projects, publishing every call, piece of feedback, tool call and agent-to-agent call over OTLP gRPC, with custom model pricing on both. Langfuse is better for evals, dataset building and prompt versioning; Phoenix is about as capable, and choosing it is personal preference.

      Published Oct 1, 2026self-described: Team projects with self-hosted models, tracing every call, tool call and agent-to-agent call

    2. Positive

      jkisiel, Hacker News commenter

      Comment on: LLM Observability in the Wild, Why OpenTelemetry Should Be the StandardHacker News comment

      Evaluated many tools for a small startup in early 2025; Phoenix was the only one that worked out of the box and used OpenTelemetry the right way.

      Published Sep 28, 2025self-described: Small startup choosing its LLM observability stack

    3. Positive

      Jérémie Doucy (jdoucy), Jérémie Doucy, contributor to the Bayes Impact platform

      feat(observability): export traces over OTLP and link them to Phoenix instead of LangfuseGitHub pull request

      Moved Bayes Impact's platform from a 673-line Langfuse exporter to plain OTLP, with an OpenTelemetry Collector mapping spans to OpenInference for Phoenix. Plans to keep Langfuse read-only for 30 days, since Phoenix deletes traces after 30 days.

      Published Sep 29, 2026self-described: Bayes Impact platform, staging and production

    4. Positive

      Amir Bitaraf, Booking.com (with Shahaf Veber)

      From Traditional ML to AI Agents: How Booking.com Scales AI Observability With Arize AIEngineering blog post, Booking.com ML & DS Blog

      Booking.com sends traces from its agents and prediction logs from its ML models into Arize through a central OpenTelemetry collector that redacts PII. An Arize latency monitor caught an agent running without a service tier, and comparing token counts in Arize exposed context bloat in an itinerary agent before it reached production.

      Published Jul 10, 2026self-described: Booking.com's shared observability platform for traditional ML models and LLM agents; the authors describe a partnership with Arize

    5. Positive

      yurif-freitas, Open WebUI user (issue author)

      Add support for Arize Phoenix for LLM observability and evaluationGitHub issue

      Their team runs Phoenix alongside Open WebUI and asked for a native Phoenix pipeline, since both speak OpenTelemetry.

      Published May 16, 2025self-described: Team running Phoenix alongside Open WebUI

    Mixed or negative, 1

    1. Mixed

      robbert-gijsels, Developer (issue author)

      [ENHANCEMENT]: Support attaching images to non-LLM spans (TOOL/CHAIN) without faking LLM message attributesGitHub issue

      Their team traces an OCR and vision pipeline in Phoenix, but images render only on LLM spans, so they fake LLM spans to see them.

      Published Jul 15, 2026self-described: Team tracing a computer-vision pipeline with OpenTelemetry and OpenInference

    Open them in the record

What concerns them

Every concern practitioners raise, most raised first.

  1. Scale & Self-Host OperationsAll 6 sources on it raise it as a concernHigher traffic exposes slow queries and missing traces, plus database deadlocks. Teams also own Phoenix upgrades and operations.

    Counted for Teams and technical leaders.

    1. Mixed

      ToWorld, Developer (discussion author)

      Can the image dependencies for an offline/air-gapped Phoenix deployment be simplified?GitHub discussion

      Phoenix's container build pulls four external images and a WASM binary that an air-gapped site must mirror; their team found the PyPI wheel works and asked if that is supported.

      Published Sep 9, 2026self-described: Team deploying Phoenix in a fully air-gapped environment

    2. Mixed

      David O'Connell (doconnell2020), David O'Connell, GitHub profile lists Telnyx

      BulkInserter opens up to 7 DB sessions per loop iteration, exhausting connection pool under loadGitHub issue

      Under sustained ingest on two self-hosted replicas, Phoenix's bulk inserter opened up to 7 database sessions every 100 ms and exhausted the connection pool. The author offered a fix that uses one session per loop.

      Published Mar 25, 2026Self-hosted Phoenix on PostgreSQL with 2 replicas

    3. Mixed

      oginskis, Developer (discussion author)

      Slow trace and span search performance when searching by spans.attributesGitHub discussion

      At about 100,000 LLM interactions a day on Kubernetes and Cloud SQL, filtering traces by metadata such as app or team is slow over a 24-hour window.

      Published Feb 4, 2026self-described: Team self-hosting Phoenix on Kubernetes with Cloud SQL PostgreSQL, about 100,000 LLM interactions a day

    4. Mixed

      vignesh-arivazhagan, Developer (discussion author)

      Old logs are getting deleted when the number of log request is highGitHub discussion

      Logging thousands of LLM calls from a high-throughput app, their team sees older traces disappear from Phoenix even after tuning batch export settings.

      Published Jul 17, 2025self-described: Team building a high-throughput LLM application

    5. Negative

      zac-wang-nv, Developer on NVIDIA's Video Search and Summarization blueprint

      chore: remove Arize Phoenix (telemetry / observability) from VSSGitHub pull request

      NVIDIA's blueprint team removed Phoenix, an optional tracing exporter, because it added a container and dependencies they did not want to maintain; OpenTelemetry stays for future exporters.

      Published May 20, 2026NVIDIA AI Blueprint shipped with Docker Compose and Helm

    6. Mixed

      Valentin Velev, Data Scientist, Codify

      Evaluating AI Agents with DeepEval and Arize Phoenix: Lessons from Our Integration JourneyEngineering blog post, Codify blog

      Codify traces its agents in Phoenix and scores them with DeepEval. Phoenix helps diagnose failures, but past experiment results needed manual database queries, concurrency above 1 errored, and docs were thin or outdated.

      Published Oct 6, 2025Codify AG, Swiss software and AI consultancy, evaluating its own agents

    GILD's fact checks, which don't count as sources

    Checked Oct 12, 2026.Phoenix stores data in SQLite or PostgreSQL with one tenant per instance. Additional instances share one database. Arize directs high-volume ingestion and OLAP work to AX and its adb database. Phoenix: Architecture

    Open them in the record

Where the evidence agrees

Statements this group's sources agree on, then the ones close to agreement.

  1. Recurring concern

    Practitioners compare Phoenix with Langfuse most often. Several choose Langfuse.

    7 supporting sources

Where it disagrees

Questions the sources split on, with the count on each side.

  • Evidence remains split

    When practitioners weigh Arize against another platform, which do they keep?

    • Chose or kept Arize, 9 sources

      Some chose Phoenix or Arize AX for simple self-hosting, OpenTelemetry fit, or production evals and alerts, including PagerDuty for its agents and Bayes Impact after leaving Langfuse.

    • Chose another tool, 6 sources

      Others picked or moved to Langfuse, Elasticsearch, or no tracer, citing a confusing first look, instrumentation effort, the Elastic License, or one more service to maintain.

The evidence

Whose experience this is

The three groups, then every practitioner by the context they work in, and how emphasis changes with scale.

Three groups drawn from the same sources. Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. Signals open on Teams and technical leaders.

  • Teams and technical leaders

    29 sources. Leaders' accounts cover how named companies and small product teams run Phoenix or Arize AX, what they trace and evaluate, and why some moved to another platform.

  • Individual builders

    35 sources. Builders' accounts cover setup, self-hosting, OpenTelemetry, and day-to-day reliability for developers running Phoenix on their own.

  • All practitioners

    64 sources. These accounts combine named product teams with individual builders. Builders' GitHub issues emphasize problems.

64 sources from practitioners, by the context they work in. Filled rows count for this group.

  1. Individual builderDevelopers building on the platform on their own35 sources

    Emphasizes Self-hosting, OpenTelemetry, and reliability

  2. Small product team2 to 10 engineers, or an unnamed startup team13 sources, counted here

    Emphasizes Scale, access control, and platform choice

  3. Product or platform team11 to 50 engineers, or a named company's team14 sources, counted here

    Emphasizes Trace debugging, evaluation, and production alerts

  4. Technical leadershipLeaders setting model and platform choices across teamsNo sources from this setting yet
  5. Organization-wideEnterprise or company-wide AI platform2 sources, counted here

    Emphasizes Booking.com routes traces from its ML models and every AgentHub agent into Arize as one shared platform

What changes with scale. Individual builders talk most about how easily Phoenix self-hosts and how well it fits OpenTelemetry, and file most of the reliability reports, including a project page at 3.5 seconds or more per load on 2.4 million root spans in October 2026. Small teams running one Phoenix instance for several groups raise access control and query speed at volume. Named companies describe tracing, evaluation, and alerts on AX at PagerDuty, Wayfair, Booking.com, and Uber, and moving onto or off Phoenix at AXA, Bayes Impact, Apify, bbv, NVIDIA, and Elastic. Parlance Labs, a two-person evaluation practice, measured Alyx's precision against Braintrust Loop and LangSmith on 100 production traces.

Where the sources come from

Where this group's sources were published.

Counted for Teams and technical leaders, from 29 sources. Change group

43 sources for this group, 14 of them documentation

  • Developer Communities17, 40% of 43 sources
  • Product Documentation14, 33% of 43 sources
  • Engineering Blogs & Reviews10, 23% of 43 sources
  • Talks & Videos2, 5% of 43 sources

Older accounts remain when applicable. Accounts tied to old Phoenix versions or features since added carry Needs a recheck.

See sources by origin

The record

Who each source is, how it knows, whether it still applies and what it counts toward.

Newest first, on its own page. Every count above opens its sources there.

Read all 78 sources

About this read

What changed, and how we count

What changed

  • Each source links to a public page and names its author and publication date.
  • A finding needs at least 5 sources from 3 different authors for each group. A disagreement also needs at least 2 sources on each side.
  • Keeps Arize's own blog posts, customer stories, and comparison pages out of the source count, and excludes posts by competing vendors.
  • Checks the self-hosting, license, access control, and plan findings against Arize's current AX and Phoenix documentation.
  • Counts only sources published on or after January 1, 2025, and caps GitHub issues and discussions at about a third of the sources. Set aside 13 thin issue threads for pull requests in which teams such as Apify, Bayes Impact, and bbv adopted, moved, or removed Phoenix. Two pull requests from the same bbv team count as one decision.
  • Oct 25, 2026: added Stability, drawn from accounts already on the page. Of these, 1 says Phoenix worked when other tools did not boot, and 2 report it destabilizing production or failing to load a large project.
  • Oct 25, 2026: adds 5 engineering blog, conference talk, independent evaluation, and newsletter sources.
  • Oct 26, 2026: adds team accounts from Uber and AXA talks at AI Engineer and from the Booking.com and PagerDuty engineering blogs, archived the same way.
  • Oct 28, 2026: adds 9 Operator Insights from named teams (Parlance Labs' evaluation of Alyx against Braintrust Loop and LangSmith, Elastic's, Apify's and bbv's exits from Phoenix, Bayes Impact's switch to it, Booking.com, Handshake, NonBioS and AXA), ledger quotes on 6 insights, and 13 records from GitHub, X, Reddit, Hacker News and Parlance Labs. The 8 records from GILD's targeted search for problem reports carry no stance and count toward no figure. An X post and two Reddit comments found the same day take positions and count. Parlance Labs' record is listed and not counted, since Hamel Husain already has two counted records here.
  • Oct 30, 2026: a second-model check removed Booking.com's Operator Insight, whose page GILD could not store, and an X post that names Phoenix with no outcome. Parlance Labs' record no longer reads as a vote for Arize, since its authors say ranking vendors was not the goal. It corrected Bayes Impact's switch, which kept Langfuse read-only for 30 days as a plan, bbv's exit, which its pull request ties to experiments and cost tracking, NonBioS's takeaway, a Reddit cost-tracing sketch and Braintrust's ease of use in the Braintrust comparison. 64 practitioner accounts remain; the counted figures do not move.

Method

A running review of what teams building AI features on Arize report, from individual builders to company-wide platforms. Each finding links to its sources, and each conflict shows both sides.

Counts cover only the sources GILD reviewed.

Who counts

Teams and technical leaders
Small product team, Product or platform team, Technical leadership, Organization-wide
Individual builders
Individual builder
All practitioners
Everyone in the teams and leaders and individual builders groups

Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. GILD files each source in a group from how the account describes its author. A role or employer marked self-described is what the source states about themselves. Documentation checks facts for every group and never counts toward sentiment.

How the numbers work

  • Overall signal. Positive votes minus negative ones, per 100 votes from the sources that take a side. Each author counts once, with their latest view, whoever they are and wherever they posted. The older weighted reading, where accounts GILD matched to a named person count three times when GILD lists the author among technical leaders and twice otherwise, is shown beside it as a second view. Where an author wrote more than one source, the line beside the split gives the votes, one per author, so the score follows from it. When one author's sources from the same day disagree, that author counts as mixed, and a half point rounds away from zero. Sources with no opinion are listed and not counted. Strong positive is +50 or higher, positive +20 to +49, mixed −19 to +19, negative −20 to −49, strong negative −50 or lower.
  • Weighted reading, second view. Counted the older way, where an account GILD matched to a named person counts two or three times, teams and leaders read +24 positive (method sentiment/S1). GILD printed that number as "GILD score" until October 29, 2026; most of those matches rest on a name or a self-described role.
  • Method change, Oct 30, 2026. GILD changed how practitioner sentiment counts, to one vote per author. Weighted, it read +33 Positive; with one vote per author it reads +24 Positive. No evidence changed.
  • Unscored. A group gets a score only with 20 sources from 15 authors. Below that, one source moves the score about 5 points, so the read says "Unscored until 20 sources from 15 authors" and shows its counts.
  • Independence. A source whose author works for the vendor, for one of its partners or programs, or for a competitor, and a page the vendor published or ran, is listed with that tie and counts toward nothing. One author counts on at most two records per product. The line under the score says how many authors and how many separate discussions the counted sources come from: comments in one thread are one discussion.
  • Praise and concerns. How many of the sources that discuss a theme praise it or raise it.
  • Consensus. A statement joins when 20% of the group's sources support it and fewer contradict it. Contradicting sources are counted separately. Below that line a statement shows as building. At 40% or more it reads Strong agreement; below, Recurring point.
  • Disagreements. The count on each side. GILD names no winner and gives no share of all users.
  • Minimum evidence. A finding needs 5 sources from 3 authors; a disagreement also needs 2 on each side. Smaller counts stay in the table below, marked under the minimum.

Labels on a source

A source's date and whether it still applies are separate: a recent post can describe an old version, and an old one can still hold.

Documented
Stated in official documentation.
Reported
A practitioner's account of their own use.
Measured
Backed by the source's own measurements, such as a comparative test or tracked team metrics.
GILD analysis
GILD's check of a claim against official documentation.
Current
Describes the product as it is now.
Still applicable
Older, and the behavior or lesson still holds.
Needs a recheck
May depend on a plan, version or setting that changed since it was written. It keeps counting until GILD rechecks it, since the rest of the account usually holds, and its card says so.
Out of date
No longer accurate. Kept in the record; counts toward nothing current.

Flagged for a recheck, of the sources that take a side: teams and leaders 0 of 27, individual builders 1 of 26, all practitioners 1 of 53.

Limits

  • The figures count reviewed public sources. Bug reports lean negative, so GILD caps GitHub issues and discussions at about a third, 16 of 52, and counts an issue as mixed unless its author rejects or abandons Phoenix. Another 14 sources are pull requests documenting Phoenix adoption, migration, or removal. All 4 AX accounts describe production use, and the other accounts concern Phoenix. Booking.com and PagerDuty each describe a partnership with Arize. PagerDuty co-wrote its account with an Arize solutions architect. On Oct 25, 2026, GILD added NVIDIA's and Codify's engineering blog posts, an independent tool evaluation, a Pragmatic Engineer newsletter guest post, and an AI Engineer World's Fair talk. Hacker News returned 6 qualifying comments from 2025 onward. Arctic Shift returned 2 qualifying r/LLMDevs posts. Searches of r/LangChain and r/mlops, plus r/devops, timed out.
  • Most accounts describe Phoenix. PagerDuty, Wayfair, Booking.com, and Uber describe AX. Sources do not consistently name Phoenix versions. Since early 2025, Phoenix has added authentication and retention policies, plus dataset upserts and fixes. Accounts tied to old versions or missing features carry Needs a recheck. Requests that Arize has since shipped carry Out of date.

Every count, and the sources behind it

16 findings for 3 groups

Each row opens its sources for the group you are reading.

FindingTeams and technical leadersIndividual buildersAll practitionersSources
Current readCurrent sentiment11 positive, 12 mixed, 4 negative14 positive, 10 mixed, 2 negative25 positive, 22 mixed, 6 negativeSources
PraiseTrace-Level Debugging10 of 11 positive3 of 3 positive (under the minimum)13 of 14 positiveSources
PraiseDatasets & Evaluations2 of 3 positive (under the minimum)4 of 5 positive6 of 8 positiveSources
PraiseFree, Simple Self-Hosting6 of 6 positive12 of 13 positive18 of 19 positiveSources
PraiseOpenTelemetry & OpenInference5 of 6 positive7 of 10 positive12 of 16 positiveSources
PraiseProduction Monitoring4 of 4 positive (under the minimum)0 of 1 positive (under the minimum)4 of 5 positiveSources
PraiseStability1 of 1 positive (under the minimum)0 of 2 positive (under the minimum)1 of 3 positive (under the minimum)Sources
ConcernScale & Self-Host Operations6 of 6 raise it5 of 5 raise it11 of 11 raise itSources
ConcernUI, Features & Reliability3 of 3 raise it (under the minimum)5 of 5 raise it8 of 8 raise itSources
ConcernAccess Control & Multi-Team Use1 of 1 raise it (under the minimum)3 of 4 raise it (under the minimum)4 of 5 raise itSources
ConcernSource-Available License2 of 2 raise it (under the minimum)1 of 3 raise it (under the minimum)3 of 5 raise itSources
ConsensusSelf-hosting keeps traces in-house1 supporting (under the minimum)4 supporting (under the minimum)5 supporting (below threshold)Sources
ConsensusOpenTelemetry keeps the backend replaceable4 supporting (under the minimum)4 supporting (under the minimum)8 supporting (below threshold)Sources
ConsensusLangfuse is the tool weighed against Phoenix7 supporting4 supporting (under the minimum)11 supporting (below threshold)Sources
DisagreementWhen practitioners weigh Arize against another platform, which do they keep?Chose or kept Arize 9, Chose another tool 6Chose or kept Arize 5, Chose another tool 1 (under the minimum)Chose or kept Arize 14, Chose another tool 7Sources
Current readEvidence mix43 sources49 sources78 sourcesSources

Fact checks

GILD's checks of claims against official documentation, kept apart from the source counts.

  • Datasets & Evaluations. AX continuously scores selected production traces with LLM judges, code evaluators, agent-as-a-judge, and remote evaluators. Your configured sampling rate sets volume. Judge calls use your AI provider, so your team pays for tokens. Arize AX: Run online evals on traces, checked Oct 12, 2026
  • Free, Simple Self-Hosting. Phoenix documents self-hosting with no license fees, usage limits, or feature gates. It runs on Docker, Kubernetes, or Helm, with SQLite by default and PostgreSQL 14 or later recommended for production. Phoenix: Self-hosting, checked Oct 12, 2026
  • Scale & Self-Host Operations. Phoenix stores data in SQLite or PostgreSQL with one tenant per instance. Additional instances share one database. Arize directs high-volume ingestion and OLAP work to AX and its adb database. Phoenix: Architecture, checked Oct 12, 2026
  • Access Control & Multi-Team Use. Phoenix supports OAuth2 and LDAP sign-in. Its admin, member, and viewer roles apply instance-wide. Project-scoped access is planned for later in 2026. AX offers SAML SSO and organizations with spaces and custom roles. Phoenix: Access control (RBAC), checked Oct 12, 2026
  • Source-Available License. Phoenix is released under the Elastic License 2.0, which Arize documents as free to self-host with no feature gates; it is not an OSI-approved open-source license. Phoenix: License, checked Oct 12, 2026