Arize Phoenix vs Noveum AI: is observing your agents enough, or do you need them tested and fixed?
An honest comparison of tracing and visibility versus automated evaluation, voice quality scoring, synthetic testing, and auto-remediation, for AI teams deciding what they actually need.
Best for automated eval, voice quality scoring, pre-production synthetic testing, and teams that want actionable fixes.
Best for open-source, self-hosted observability and for teams that need wide framework coverage with zero vendor lock-in.
Which tool is right for you?
Noveum tells you what to do next. Arize only shows you what happened.
Noveum is built for teams running agents in production who need more than a trace to look at. You connect your stack, and Noveum scores every output automatically using 100+ built-in scorers, runs NovaSynth synthetic sessions to catch issues before real users hit them, pinpoints root causes, and hands your engineers a structured fix report ready to use in their IDE. No manual review queues. No annotation sprints. No debugging sessions that stretch into days.
Evaluation, voice testing, auto-remediation: three things Arize leaves your team to figure out
You should not pay more just because your agents got busy.
Noveum uses NovaEval with 100+ deterministic scorers, complemented by LLM-as-judge where deeper reasoning is needed. Deterministic scorers return the same result every time and cost less to run. Arize relies on LLM-as-judge prompts, so every scorer is an LLM call. At 10,000 traces a day, that adds up fast and results get less consistent as volume grows.
Test your voice agent before your users ever hear it.
Noveum's voice scorers cover TTS quality, response latency, mispronunciation, interruption detection, and tone consistency. And NovaSynth takes it further. You define realistic personas, write test scenarios, and NovaSynth runs those conversations through your live agent via voice or phone at scale. Every session is automatically scored. You find the failures before your users do.
Your next bug comes with its own fix attached.
When Noveum detects a failure pattern it categorizes the root cause, analyzes the affected prompts and tool configurations, and produces a structured improvement document ready to use in your IDE. You go from something broke to here is what to change, without a debugging session in between.
Pricing at a glance
Open source or fully managed cloud
The eval stack your team never has to build
Noveum charges by credit. You pay for evaluation and testing work, not team size or span volume. You get 100+ built-in scorers, voice pipeline scoring, NovaSynth synthetic testing, root cause analysis, and automated fix reports all included from the first paid plan. Arize AX Pro is $50 per month with unlimited users, which is a reasonable entry point. As you move into AX Enterprise, pricing is custom and typically runs $50,000 or more per year at scale. For teams shipping voice agents and AI products at scale, Noveum's model stays predictable as you grow.
Common questions
Our take
Noveum is built for teams that need more than a trace to look at. You get 100+ scorers running on every output from day one, NovaSynth catching voice and agent failures before real users hit them, root cause analysis that tells you exactly why something broke, and automated fix reports your engineers can take straight into their IDE. Less time in logs. More time shipping better agents.
Arize Phoenix is a strong tracing tool. If your team self-hosts, needs zero vendor lock-in, and wants observability across many frameworks, it earns its place.
But observability alone does not close the loop. Knowing what happened is not the same as knowing what broke. And knowing what broke is not the same as knowing how to fix it. If you ship voice agents or need the full eval and remediation loop running automatically, Noveum is the platform that gets you there.
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About Noveum.ai — Your 24/7 AI Eval Engineering Team
Noveum.ai is the only platform that doesn't just tell you what's broken — it tells you how to fix it. As your 24/7 AI Eval Engineering Team, Noveum monitors, evaluates, debugs, and sends fixes as PR to you, at scale. Built by ex-AWS SageMaker, ex-Sambanova, and ex-Playo engineers, Noveum is purpose-built for enterprise AI teams running chatbots, voice bots, and autonomous agents in production.
With a lightweight SDK integration (15 minutes or less), Noveum captures everything your AI Agent does — prompts, decisions, tool calls, token usage, context retrievals, and workflow traces. Processing over 6M traces and 60M spans per day, the platform provides production-grade observability at enterprise scale.
The platform includes 100+ specialized AI scorers across 18 categories, including dedicated Audio/TTS Quality and Voice Pipeline Latency scorers — making Noveum the only platform with built-in audio evaluation capabilities. From hallucination detection to mispronunciation scoring, from RAG quality to speaking-over-user detection, every dimension of AI quality is covered.
NovaPilot, Noveum's autonomous AI agent, analyzes evaluation data, identifies failure patterns, tests 136+ prompt variations across 4 evolutionary generations, and delivers verified fixes — all automatically. NovaPilot's 4 specialized agents (Prompt Analyzer, Tool Analyzer, Flow Analyzer, General Analyzer) provide comprehensive coverage of every failure mode. Teams can also create complex custom evaluations based on their Product Requirements.
Noveum supports on-prem deployment with BYO ClickHouse, offers Enterprise SLAs with SOC 2 Type II, HIPAA, and GDPR compliance. Backed by AWS, GCP, DigitalOcean, OVHCloud, Nvidia Inception Program, ElevenLabs, and Anthropic.
Why AI Reliability Matters for Modern Teams
AI Agents are becoming the backbone of digital operations — answering customer questions, triaging support tickets, qualifying leads, routing tasks, summarizing documents, analyzing data, and automating workflows. But unlike traditional software, AI is inherently probabilistic. Even well-designed agents will:
- hallucinate facts
- misunderstand instructions
- misuse tools
- return irrelevant answers
- generate inconsistent formats
- exceed token budgets
- produce partial responses
- violate compliance rules
- degrade over time without monitoring
Unreliable AI leads to increased support burden, lost revenue, compliance risks, poor customer experience, and rising operational costs. Noveum.ai provides the infrastructure needed to measure, monitor, and maintain AI Agent quality at scale.
Noveum Monitoring — Complete AI Observability
Noveum captures all agent behaviors in real time:
- Full prompt & response history
- Tool call inputs and outputs
- Token usage tracking
- Cost attribution per action
- System and user message flows
- Context retrieval logs
- Branching workflow behavior
- Intermediate reasoning
- Agent chain-of-thought traces
- Latency analysis
- Failure modes and exceptions
The Agent Visualizer produces a pre-order execution graph showing how the AI Agent reasons and acts across steps. This visual monitoring layer enables developers, product managers, and operations teams to understand the internal logic of their AI Agents and diagnose complex failure patterns.
Noveum Evaluation — Scoring AI Performance with 100+ Specialized Scorers
Noveum.ai includes the most comprehensive evaluation library available for enterprise AI Agents. Every scorer has been designed to evaluate a different dimension of AI output quality — from hallucination detection and factual grounding to conversation health, safety compliance, RAG retrieval quality, structured formatting, and system performance.
Each evaluation produces:
- numeric score
- natural-language reasoning
- step-by-step justification
- references to context used
- recommendations for improvement
Full Scorer Library With Explanations (100+ Scorers)
1. Accuracy, Matching & Semantic Quality Scorers
- ExactMatchScorer
Checks if the AI output perfectly matches the expected answer. Ideal for classification or deterministic tasks. - AccuracyScorer
Measures the proportion of correct responses across a dataset — essential for baseline LLM quality benchmarking. - MultiPatternAccuracyScorer
Supports multiple correct output forms. Useful for flexible workflows where several answers are acceptable. - F1Scorer
Balances precision and recall to measure how accurately the AI captures all important elements. - BLEUScorer
An n-gram matching metric for evaluating text similarity. Used broadly in summarization and generation tasks. - ROUGEScorer
Measures recall overlap with reference text, especially useful for long-form content evaluation. - LevenshteinSimilarityScorer
Calculates edit distance between expected and generated text, identifying near-matches efficiently. - SemanticSimilarityScorer
Uses embedding-based comparisons to assess meaning similarity, not just surface text similarity. - InformationDensityScorer
Scores how substantive and information-rich an answer is, identifying fluff or low-density output. - ClarityAndCoherenceScorer
Evaluates readability, logical structure, and flow — vital for customer-facing AI responses. - AnswerCompletenessScorer
Checks whether the AI addressed every part of the prompt completely. - QuestionAnswerAlignmentScorer
Ensures direct alignment between user question and generated answer. - TechnicalAccuracyScorer
Validates correctness for domain-specific answers in finance, engineering, legal, or medical tasks. - TerminologyConsistencyScorer
Ensures specialist terminology is used appropriately and consistently in AI responses.
2. Relevance, Faithfulness & Hallucination Scorers
- AnswerRelevancyScorer
Measures how relevant the AI's response is to user intent. - FaithfulnessScorer
Checks whether the AI stays grounded in provided context without adding unsupported claims. - FactualAccuracyScorer
Identifies factual inaccuracies by comparing output with known truths. - HallucinationDetectionScorer
Detects fabricated or invented information. One of the most important scorers for enterprise safety. - ConflictResolutionScorer
Identifies contradictions in the AI's reasoning. - ContextPrioritizationScorer
Ensures the AI selects the most important context pieces when generating a response. - ContextFaithfulnessScorerPP
Validates that the answer is grounded in provided documents. - ContextGroundednessScorer
Checks if every important claim is supported by context. - ContextCompletenessScorer
Measures whether the AI uses enough context to form a complete answer. - ContextConsistencyScorer
Ensures consistency with reference material across statements.
3. RAG (Retrieval-Augmented Generation) Scorers
- ContextualPrecisionScorer
Evaluates relevance of retrieved context snippets used in the final answer. - ContextualRecallScorer
Measures how much relevant content the AI included from the available context. - RAGASScorer
Industry-standard RAG quality scoring across groundedness, relevance, and answer quality. - ContextualPrecisionScorerPP
Enhanced precision evaluation for more complex retrieval chains. - ContextualRecallScorerPP
Advanced recall scoring that checks deeper semantic relevance. - RetrievalF1Scorer
Harmonic mean of precision and recall for retrieval effectiveness. - RetrievalRankingScorer
Evaluates document ranking quality for RAG pipelines. - RetrievalDiversityScorer
Ensures retrieval results include diverse, non-redundant knowledge sources. - AggregateRAGScorer
Provides a combined score summarizing RAG performance. - RAGAnswerQualityScorer
Evaluates whether the final answer is high-quality and well-grounded.
4. Conversation & Role Behavior Scorers
- ConversationRelevancyScorer
Checks turn-by-turn relevance in multi-turn dialogues. - ConversationCompletenessScorer
Ensures the entire user intent has been addressed in the conversation. - RoleAdherenceScorer
Ensures the agent sticks to its assigned role and persona. - ConversationalMetricsScorer
Composite conversational quality scorer measuring flow, engagement, and relevance.
5. Safety, Bias & Harm Prevention Scorers
- AnswerRefusalScorer
Detects whether AI refuses unsafe or disallowed tasks appropriately. - ContentModerationScorer
Flags potentially harmful, explicit, or policy-violating content. - ContentSafetyViolationScorer
Scores violations of safety, compliance, or governance guidelines. - ToxicityScorer
Evaluates presence of insulting, aggressive, or harmful language. - IsHarmfulAdviceScorer
Detects dangerous recommendations — crucial for sensitive domains. - NoGenderBiasScorer
Ensures fair, unbiased responses related to gender. - NoRacialBiasScorer
Evaluates outputs for racial bias or stereotyping. - NoAgeBiasScorer
Checks for age-related prejudice in AI responses. - CulturalSensitivityScorer
Ensures the AI respects cultural norms and avoids offensive phrasing.
6. Structured Output & Format Scorers
- IsJSONScorer
Validates whether responses follow correct JSON formatting. - IsEmailScorer
Checks for valid email generation structure. - ValidLinksScorer
Ensures URLs in the output are properly formatted and valid.
7. Multi-Judge & Deep Reasoning Scorers
- PanelOfJudgesScorer
Uses multiple LLM 'judges' to provide averaged evaluations for robust scoring. - SpecializedPanelScorer
Domain-specific multi-judge scorer for technical or expert evaluations. - GEvalScorer
A generalized evaluation approach measuring reasoning, structure, and coherence. - AgentScorers
Bundle of agent-specific evaluations optimized for multi-step AI workflows.
8. Pipeline & System-Level Scorers
- QueryProcessingEvaluator
Evaluates how accurately the system interprets and processes user queries. - RetrievalStageEvaluator
Scores the retrieval portion of a RAG pipeline. - RerankingEvaluator
Evaluates how well documents are reranked for relevance. - RAGPipelineEvaluator
Full end-to-end scoring for retrieval + ranking + generation. - PipelineCoordinationScorer
Evaluates coordination of pipeline components across stages.
9. Latency, Resource & Error Scorers
- LatencyAnalysisScorer
Measures latency and identifies bottlenecks across the AI execution path. - ResourceUtilizationScorer
Assesses token efficiency, compute usage, and cost impact. - ErrorPropagationScorer
Detects how errors propagate across multi-step agent workflows.
10. Knowledge & Memory Scorers
- KnowledgeRetentionScorer
Measures whether an AI preserves key information across turns or steps.
Noveum's Auto-Fixing Engine
After evaluating agents using all scorers above, Noveum automatically analyzes failures and runs improvement experiments such as:
- prompt optimization
- model selection for better cost-performance
- parameter tuning
- workflow adjustments
- tool-call corrections
The system then provides a full recommendation report or can automatically generate pull requests to update your agent logic.
AI Cost Optimization With Noveum
Noveum identifies token-heavy prompts, inefficient retrieval patterns, expensive models, and redundant workflow steps. It analyzes cost per generation step and suggests cheaper, higher-performing alternatives.
Companies typically see 20-50% reduction in AI operational cost after implementing Noveum's recommendations.
Noveum.ai — Your 24/7 AI Eval Engineering Team
As companies scale AI Agents to hundreds and thousands, Noveum provides the only platform that monitors, evaluates, and autonomously fixes broken agents at scale. Built for enterprise from day 1, with on-prem deployment, BYO ClickHouse, and SOC 2 Type II compliance. Trusted by agent builders, banks, and telecom providers.