Agnost AI vs Arize Phoenix

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-18
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At a glance

DimensionAgnost AIArize Phoenix
PricingContact for pricingFree (open source, self-hostable); cloud with free tier
Core FocusProduction agent failure detectionFull LLM agent observability + evals
DeploymentCloud (not specified)Self-host (local, Docker, K8s) or cloud
Key FeatureCatches failures evals missLLM-as-judge, experiments, PXI
IntegrationsNone listedOpenTelemetry, LlamaIndex, LangChain, OpenAI, K8s, Docker

If you need to catch production edge cases that standard evals miss and want a quick, focused monitoring solution, Agnost AI is your pick. But if you want a comprehensive, open-source, self-hostable platform for tracing, evaluating, and iterating on agent performance, Arize Phoenix is the clear winner.

Try Agnost AITry Arize Phoenix
Agnost AI
Agnost AI

Agent analytics that catches silent AI agent failures in live production conversations, then turns them into reviewed prompt fixes.

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Arize Phoenix
Arize Phoenix

Arize Phoenix is open-source LLM observability: trace agent steps, run evals, and self-host your traces.

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Pricing
Freemium
Freemium
Plans
$0/mo
$49/mo
$499/mo
Custom
$0/mo
$50/mo
Custom
Popularity
3 views
7.3k views
Skill Level
Intermediate
Intermediate
API Available
Platforms
Web
WebAPICLI
Categories
📡 LLM Observability & Evals
📡 LLM Observability & Evals
Features
Detect silent agent failures from live production conversations
Catch user corrections, rephrased questions and abandonment signals
Group similar failures into clusters ranked by users affected
Track which failure clusters are growing over time
Drill into flagged messages behind each discovered intent
Inspect nested tool calls and traces behind a specific message
Generate regression evals automatically from affected traces
Approve suggested prompt or workflow fixes to apply automatically
Replay results shown before a fix is approved
Automatic intent and sentiment discovery across conversations
Quality, policy and compliance violation detection
Slack alerts for silent failures and rising friction with copyable fixes
Voice agent silence and interruption analysis
Session-style replay for agent conversations
OpenTelemetry-based instrumentation for Vercel AI SDK and VoltAgent
End-to-end tracing of agent prompts, retrievals, tool calls, and outputs
OpenTelemetry-native instrumentation with no proprietary lock-in
LLM-as-judge evaluations on traces, spans, datasets, and experiments
Decision Judge: provider-neutral typed boolean, choice, or score evaluators
Session evals: score multi-turn conversations, not just single spans
Human annotations and labeling queues with review workflows
Queue full multi-turn sessions and complete traces for annotation
Annotation queues via app, REST API, and Python, TypeScript, and Go SDKs
Build datasets directly from production traces
Run experiments to benchmark changes under identical conditions
Prompt IDE with multi-prompt comparison and versioning
PXI: AI engineering agent that investigates, annotates, and experiments via chat
Alyx with long-term memory, plus dashboard building and widget maintenance
Multi-modal tracing and evaluation for image, voice, and PDF data
Agent trajectory visualizations as path and graph views
Integrations
Slack
OpenTelemetry
LlamaIndex
LangChain
OpenAI
Kubernetes
Docker

What real users say: Agnost AI vs Arize Phoenix

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Agnost AI

56 mentions across 4 sources · 55% positive — mixed (averaged across 4 sources)

Hacker News, YouTube, Product Hunt, Lemmy

What users praise

  • • Catches real behavioral failures like rageprompting and repeated rephrasing that evals miss
  • • Observes production conversations, not just test assertions
  • • Provides detailed traces, logs, and decision-path visualization
  • • Pitch is directly validated by founders' own outreach to user teams

What frustrates them

  • • Pricing is 'contact us' — only vague 'pennies per million messages' claim
  • • No independent reviews or critical evaluations exist yet
  • • Real user feedback outside launch comments is essentially absent
  • • Doesn't directly answer whether it cuts model inference costs

Researched Aug 26, 2026

Arize Phoenix

27 mentions across 3 sources · 63% positive — mixed (weighted across 3 sources)

Hacker News, YouTube, Lemmy

What users praise

  • • OpenTelemetry-native tracing means spans land in existing pipelines with no proprietary backend lock-in.
  • • No model, framework, or language lock-in — integrations span LlamaIndex, LangChain, OpenAI, and beyond.
  • • Five-step observe-annotate-hypothesize-experiment-measure loop gives evals a structured workflow, not just a dashboard.
  • • Self-hosting via uvx, Docker, or Helm on Kubernetes keeps sensitive traces inside your own infrastructure.

What frustrates them

  • • Requires manual instrumentation and quality criteria before eval scores carry any meaning.
  • • Autonomous features like Alyx and dashboard-building agents sit on the paid AX side, not OSS.
  • • Getting Claude Code and other tools to emit spans into Phoenix still takes proxy or custom work.
  • • Community discussion is concentrated in HN and tutorial videos — thinner elsewhere than Langfuse or LangSmith.

Researched Oct 14, 2026

Who should pick which

  • AI engineer debugging complex agent workflows
    Pick: Arize Phoenix

    Phoenix provides end-to-end tracing, LLM-as-judge, and experiment tools to systematically debug and improve multi-step agents.

  • Enterprise needing self-hosted observability for compliance
    Pick: Arize Phoenix

    Phoenix can be self-hosted on your own infrastructure, ensuring data stays in-house — a critical feature for regulated industries.

  • ML team focused on production agent failure detection
    Pick: Agnost AI

    Agnost specializes in catching failures that standard evals miss, giving you targeted insights into edge cases and loops.

  • Open-source enthusiast seeking full control
    Pick: Arize Phoenix

    Phoenix is open-source, free, and self-hostable, offering complete control over your AI observability stack.

Frequently Asked Questions

Agnost AI vs Arize Phoenix: which should you choose?

If you need to catch production edge cases that standard evals miss and want a quick, focused monitoring solution, Agnost AI is your pick. But if you want a comprehensive, open-source, self-hostable platform for tracing, evaluating, and iterating on agent performance, Arize Phoenix is the clear winner.

Does Agnost AI offer a free trial?

No public pricing or free trial is listed; you need to contact their sales for a demo or quote.

Can I use Arize Phoenix with my own LLM?

Yes, Phoenix is model-agnostic and works with any LLM, including OpenAI, via its tracing and evaluation capabilities.

Is Arize Phoenix affected by the Dynatrace acquisition?

Yes, Arize announced an acquisition agreement with Dynatrace, but the open-source nature of Phoenix suggests continuity, though specifics are under the news.

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Explore each tool further

Agnost AI
View Agnost AI reviewAgnost AI alternatives
Arize Phoenix
View Arize Phoenix reviewArize Phoenix alternatives

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Last reviewed: August 26, 2026