A detailed, practical reference comparing evaluation philosophies, data needs, integrations, outputs, and recommended workflows.
Useful for product teams, engineering leads, reliability engineers, and investors evaluating LLM & agent observability solutions.
Core Philosophy Difference
Trace-level evaluation
“Did the agent follow the process correctly?”
Open-source tracing, datasets, experiments, and evaluators
“How did the traced application and experiment perform?”
Experiments, scorers, production logs, and datasets
“How did this prompt, workflow, or model perform on a dataset?”
112
Noveum.ai
15
Dimension
0
Ground truth required
Executive Summary
Each platform excels in different areas. Understanding these differences will help you choose the right solution for your specific needs.
Connected LLM and agent observability, configured evaluation, synthetic testing, and recommendation reporting built around captured trace evidence.
Primary Focus
Evaluates how and why an agent behaved the way it did
Open-source Phoenix combines tracing, datasets, prompt experiments, and model- or code-based evaluators; Arize AX adds managed production workflows.
Primary Focus
Observability, datasets, experiments, and evaluation
Braintrust combines production logs, playgrounds, versioned datasets, UI or code experiments, and reusable scorers.
Primary Focus
Experimentation, evaluation, and production quality workflows
Core Difference
“Did the agent follow the process and use the system/tools/context correctly?”
“How did the traced application, prompt, model, or scorer perform across a dataset or production cohort?”
The products overlap substantially. Compare deployment, instrumentation, scorer fit, experiment workflow, and Noveum's distinct SIP or phone persona-scenario testing and NovaPilot reports.
Feature Comparison
Noveum currently publishes 112 scorers across 15 categories, including Agent, Conversational, RAG, Safety, Hallucination, Quality, Voice, and more.
| Dimension | Noveum.ai | Arize | Braintrust |
|---|---|---|---|
| Primary focus | Instrumented traces, configured dataset evaluation, synthetic testing, recommendation reports | Open-source tracing, datasets, experiments, prompt iteration, and evaluators | Experiments, scorers, production logs, playgrounds, and versioned datasets |
| Ground truth required | Varies by scorer; reference-free options are available | Varies by evaluator; references are optional for many checks | Varies by scorer; custom code and judge scorers need not use expected output |
| Uses production traces? | Yes: instrumented traces can feed reviewed ETL datasets | Yes: score traces or collect them into datasets and experiments | Yes: log production data and promote selected records into datasets |
| Hallucination detection | Selected compatible hallucination, faithfulness, and provenance checks | Pre-built or custom model evaluators, code checks, and human labels | Autoevals, LLM-as-judge, custom code scorers, and human review |
| Typical metrics | Prompt adherence, tool usage correctness, context leakage, step validity, drift | Accuracy, precision/recall, calibration, ROUGE/BLEU proxies, embeddings similarity | Experiment scores, factuality, similarity, custom business rules, latency, and cost |
| Best for | Managed evaluation, SIP or phone persona-scenario testing, and NovaPilot reports | Open-source observability, prompt iteration, datasets, and experiments | Code- or UI-driven experiments, scorers, playgrounds, and production evaluation |
| Integration complexity | SDK + trace schema + agent instrumentation | OpenTelemetry instrumentation plus optional dataset and evaluator configuration | SDK or UI setup for logs, datasets, experiments, prompts, and scorers |
Understanding the Fundamentals
Before comparing platforms, it's essential to understand these fundamental concepts that differentiate evaluation approaches.
Trajectory
The ordered sequence of captured events: available prompt context, tool calls and arguments, tool responses, retrievals, recorded reasoning fields, and final output.
Evaluating trajectory checks HOW the model arrived at an answer.
Output
Final text or structured response. Output evaluation can use a reference, rubric, model judge, or deterministic rule.
All three platforms can evaluate outputs and captured trace context. Compare the specific instrumentation, evaluator inputs, and experiment workflow you need.
When Ground Truth is Essential
When Ground Truth is Optional
Compatible rule- or model-based evaluators can assess these from captured evidence without an expected-answer field; this is possible in Noveum, Arize Phoenix, and Braintrust with different setup.
Arize & Braintrust
Both support model-based evaluators as well as deterministic or custom code checks. Judge-model results introduce noise and calibration needs regardless of platform.
Noveum's Approach
Noveum publishes a mixed library: 76 model-backed scorers plus deterministic, audio-model, and signal-based checks. Requirements, scale, and pass direction vary by scorer.
Deep Dive
Explore the specific differences across key dimensions to understand which platform best fits your needs.
Noveum
Practical Examples
See how each platform is typically used in practice with these real-world workflow examples.
Reference-free and reference-based checks
Ingest Traces
Integrate noveum-trace SDK to capture structured trace events from your AI agents in production
Configure ETL Mapping
Use AI Mapper to propose trace-to-dataset Python, then review, test, and save the mapper before enabling eligible trace processing
Run Evals & Scorers
Apply compatible scorers to evaluated items and inspect reasoning from model-backed judges
Review NovaPilot Analysis
NovaPilot analyzes evaluated dataset items and produces a report with issue patterns and recommendations
Implement and Validate
Engineers review the report, make any code or prompt changes, and validate them in a separate evaluation run
UI or SDK workflow
Instrument or Import Data
Send OpenTelemetry traces or create a dataset from code, CSV, or selected records
Create a Dataset
Define examples with input, output, optional reference, and metadata fields
Attach Evaluators
Configure pre-built or custom LLM evaluators and deterministic code checks with explicit field mapping
Run an Experiment
Execute from the Playground or SDK and record outputs, evaluator scores, traces, latency, and cost where available
Compare Results
Inspect low scores and associated traces, then compare prompt or model versions before a team-owned release decision
From logs or a versioned dataset
Collect Representative Data
Log production or staging records, upload a file, or create dataset rows in the UI or SDK
Configure the Experiment
Choose a prompt or workflow, a versioned dataset, and Autoeval, LLM-as-judge, or custom code scorers
Compare and Promote
Review experiment and per-row scores, then promote validated configurations through your own release process
Implementation
Understanding the integration requirements and ongoing effort needed for each platform.
Noveum.ai
Moderate
Effort Level
Requires tracing and dataset-field mapping; reference-free options can reduce labeling for compatible checks
Arize / Braintrust
Workflow-dependent
Requires tracing and dataset-field mapping; reference-free options can reduce labeling for compatible checks
Captures instrumented production operations; configured eval jobs process eligible dataset items separately
Effort varies with instrumentation, self-hosting, dataset design, evaluators, and review requirements
Both support production data and experiments; references and human annotation are optional inputs for some, not all, evaluator designs
Decision Guide
Use this framework to determine which platform is best for your specific needs.
If You need reusable persona-scenario testing over SIP or phone
plus a managed scorer catalog and NovaPilot recommendation reports
If You prioritize an open-source, self-hostable observability stack
with datasets, Playground experiments, and model or code evaluators
If You prioritize UI- or code-driven prompt and model experiments
with reusable scorers and versioned datasets
Start with one instrumented workflow, then choose compatible scorers. Reference-free checks can run without expected output when their required fields are present; other checks need reviewed references.
Honest Assessment
No evaluation approach is universally sufficient. Understanding where each platform excels and falls short helps make the right choice.
Compatible scorers can assess structural, provenance, and semantic signals from the fields captured in each dataset item. Cases requiring an external canonical answer, such as precise numeric solutions, legal statutes, or regulated medical facts, still need an authoritative reference and domain review.
For tasks demanding a single authoritative ground truth (exact math solutions, regulatory compliance assertions matching statute text), organizations often still maintain a gold dataset for auditability.
Noveum's strengths are proportional to trace fidelity. Well-instrumented traces (typed tool-call records, retrieval provenance, token-level context) enable rich scoring; poor traces reduce coverage.
If you cannot instrument the relevant operations, Noveum has less evidence to map and score. ETL can normalize captured fields into dataset items, but it cannot reconstruct events or provenance that were never recorded.
Phoenix provides an open-source, self-hostable tracing and evaluation stack with broad OpenTelemetry and framework integrations.
Phoenix supports datasets, Playground or SDK experiments, prompt and model comparison, evaluator traces, and dry runs.
Teams can use pre-built or custom model evaluators, deterministic code checks, and human labels across traces, datasets, and experiments.
Phoenix is a strong candidate when open-source self-hosting, broad OpenTelemetry instrumentation, and integrated datasets or experiments are primary requirements.
Braintrust supports UI- and code-driven experiments over prompts, workflows, versioned datasets, and reusable scorers.
Teams can collect data from production, staging, evaluations, uploads, or manual entry, then track row performance across experiments.
Autoevals, LLM-as-judge, and custom code scorers can run in experiments or against production logs.
Summary
A quick reference guide to help you choose the right platform for each scenario.
| Scenario | Best Choice |
|---|---|
| SIP or phone persona-scenario testing plus managed evaluation | Noveum.ai |
| Open-source, self-hostable tracing with broad instrumentation | Arize Phoenix |
| UI or SDK experiments across prompts and models | Compare Arize Phoenix and Braintrust |
| Published scorer catalog plus NovaPilot recommendation reports | Noveum.ai |
| Debugging captured model, retrieval, and tool operations | Pilot all three |
| Managed traditional ML and LLM monitoring | Arize |
| Versioned datasets connected to production logs and experiments | Compare Braintrust and Arize Phoenix |
| Publishing accuracy metrics with ground truth | All three with authoritative references |
| Human-reviewed correctness | All three; review process is team-owned |
Noveum can compare already-captured runs from candidate models when data mapping and scorer configuration are held constant; candidate execution stays in your application.
Noveum has 76 model-backed scorers, while Arize Phoenix and Braintrust also support LLM-as-judge evaluation. Compare scorer inputs, calibration, explanations, concurrency, latency, and cost on the same dataset; some checks still need references.
Expert Guidance
Structured traces (tool events + retrieval provenance) are the foundation for both behavioral checks and for creating labeled datasets later.
Choose a small set of checks whose required fields match the dataset, then measure false alarms and review effort before expanding.
Keep a representative, human-reviewed set for calibration of judge-model methods and repeatable regression testing.
Use automated checks to prioritize samples, then add qualified human review where an authoritative label or ambiguous judgment matters.
Make it simple to reproduce evaluations across model changes.
Appendix
Missing Required Tool Call
If a scenario requires a bank_api call but no matching tool event appears, return a failed check.
Fabricated Tool Output
If the final answer cites a document ID or snippet absent from retrieved context, return a failed check.
Context Leakage
If the answer includes user data absent from the current session context, return a failed check.
Premature Answer
If final_answer occurs before required validation or step events complete, return a failed check.
Accuracy Template
Use exact match or another deterministic code evaluator when a reference is authoritative, or a calibrated judge when correctness needs a rubric.
Hallucination Template
Prompt judge with input + model_output + retrievals; ask if any claims are unsupported.
Instrumented traces → reviewed ETL mapper → structured dataset items: Noveum ETL maps fields already present in captured traces into structured dataset items. AI Mapper can propose Python mapping code, but a user must review, test, and save it. The mapper cannot create missing provenance or authoritative expected answers; add reviewed references separately when a scorer or benchmark needs them.
Why this matters: The same mapped dataset can support configured evaluations and targeted review. A dataset becomes a trustworthy golden set only after the required references and labels have been validated by the responsible team.
Common Questions
Get answers to the most common questions about choosing between these platforms.
Treat scorer results as evaluation evidence, not automatically as ground-truth labels. Export or map the required fields, preserve source identifiers, and have a qualified reviewer decide what can seed a dataset or benchmark in another system.
All three can use model-based evaluators, references, captured retrieval context, custom rules, or human review depending on configuration. Compare recall, false alarms, required fields, explanations, latency, and cost on the same representative dataset.
Not necessarily. Their tracing, datasets, experiments, and evaluation capabilities overlap. Choose based on your instrumentation, deployment, scorer, and workflow requirements; Noveum's clearest distinct public workflow is SIP and phone-capable persona-scenario testing plus NovaPilot recommendation reports.
Noveum's library mixes deterministic, signal-based, audio-model, and 76 model-backed scorers. Judge-model checks still require calibration and review. Arize Phoenix and Braintrust also support model-based and deterministic or custom code evaluators.
Noveum's strengths are proportional to captured trace fidelity. Typed tool-call records, retrieval provenance, and relevant context enable richer mapping and scoring; missing fields reduce compatibility and coverage. ETL can normalize fields that exist, but it cannot recover evidence the application never captured.
No platform removes the need for ground truth in every task. Reference-free checks can assess compatible structural, behavioral, relevance, or provenance signals, while exact facts, regulated decisions, and other canonical outcomes may require authoritative references and domain review.
See how Noveum maps captured evidence into configured evaluation, with reference-free options where compatible and human review where required.
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