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Confident AI vs Arize AI: Head-to-Head Comparison

Written by humansLast edited on Feb 1, 2026

Choosing the right LLM observability and evaluation platform comes down to what matters most to your team.

Arize AI's strength lies in its ML monitoring heritage—if you're already using Arize for traditional ML models, adding LLM observability to the same platform has obvious appeal. But that heritage also shapes its limitations: the platform is built for engineers running technical analysis, not cross-functional teams iterating on AI quality together.

Confident AI takes a different approach. It's built for teams where PMs run evaluation cycles without engineering bottlenecks, where QA teams own regression testing, and where domain experts provide feedback directly on production traces—all without writing code.

In this guide, we'll break down the differences across features, pricing, and use cases to help you decide.

How is Confident AI Different?

1. Non-technical teams can run evaluations without engineering

In most AI teams, every evaluation cycle requires engineering involvement—setting up test scripts, configuring endpoints, running code. This makes engineers the bottleneck for every AI quality decision.

Confident AI removes this bottleneck with AI connections. PMs, QA teams, and domain experts can evaluate your actual AI application directly from the platform—no code, no engineering tickets, no waiting.

PMs run full evaluation cycles on your production app independently
QA teams trigger regression tests against real endpoints on their own schedule
Domain experts validate behavior without asking engineering to "run a quick test"

When the people closest to your users can test the real thing themselves, AI quality stops being blocked on engineering capacity.

2. Simulations turn hours of manual testing into minutes

Evaluating chatbots and conversational agents means generating conversations to test. Without automation, that's 2-3 hours of manual prompting per evaluation—just to create the data you'll actually score.

Arize AI doesn't offer multi-turn simulations. Confident AI does.

Define a scenario, and the platform generates realistic multi-turn conversations automatically. What took hours now takes minutes—easily 30x time saved per evaluation cycle for teams testing conversational AI at scale.

3. Built for the whole team, not just engineers

Arize AI's ML monitoring roots show in its engineering-centric design. The UX assumes technical comfort, and workflows are built for data science personas.

This creates friction the moment anyone outside engineering needs to participate:

Product managers reviewing evaluation results
Domain experts flagging problematic outputs
QA teams uploading test datasets

Confident AI is designed so these teams own their part of the AI quality process—upload CSVs, run evaluations, annotate traces, curate datasets—all from a UI built for clarity, not technical gatekeeping.

Features and Functionalities

Confident AI and Arize AI offer overlapping features, but Arize lacks evaluation depth and is harder for non-technical teams to navigate.

Confident AI

Arize AI

LLM Observability _{Trace AI agents, track latency and cost, and more}

LLM Metrics _{Metrics for quality assurance, LLM-as-a-judge, and custom metrics}

Research-backed & open-source

Limited + heavy setup required

Simulations _{For multi-turn conversational agents}

AI analytics _{Determine user activity, retention, most active use cases}

Dataset management _{Supports datasets for both single and multi-turn use cases}

Single-turn only

Regression testing _{Side-by-side performance comparison of LLM outputs}

Prompt versioning _{Manage single-text and message-prompts}

Human annotation _{Annotate monitored data, align annotation with evals, and API support}

API support _{Centralized API to manage evaluations}

Red teaming _{Safety and security testing}

LLM Observability

Both Confident AI and Arize AI offer extensive features for LLM observability. Arize has deep roots in ML monitoring which translates to solid observability capabilities.

Confident AI

Arize AI

Free tier _{Based on monthly usage}

Unlimited seats, 10k traces, 1 month data retention

25k spans/month, 1 GB ingestion, 7 days retention

Core Features

Integrations _{One-line code integration}

OTEL Instrumentation _{OTEL integration and context propagation for distributed tracing}

Graph Visualization _{A tree view of AI agent execution for debugging}

Metadata logging _{Log any custom metadata per trace}

Trace sampling _{Sample the proportion of traces logged}

Online evals _{Run live evals on incoming traces, spans, and threads/sessions}

Custom span types _{Customize span classification for better analysis on the UI}

PII masking _{Redact custom PII in trace data}

Dashboarding _{View trace-related data in graphs and charts}

Conversation tracing _{Group traces in the same session as a thread}

User feedback _{Allow users to leave feedback via APIs or on the platform}

Export traces _{Via API or bulk export}

Annotation _{Annotate traces, spans, and threads}

LLM Evals

Both Confident AI and Arize AI offer evals, but Confident AI delivers a noticeably stronger experience—in both capability and interface—for technical and non-technical users alike.

Under the hood, Confident AI's metrics are powered by DeepEval, an open-source evaluation framework trusted by leading AI teams at OpenAI, Google, and Microsoft.

Confident AI

Arize AI

Free tier _{Based on monthly usage}

Unlimited offline evals, online evals free for first 14-days

25k spans/month, 7 days retention

Core Features

Experimentation on multi-prompt AI apps _{100% no-code eval workflows on multiple versions of your AI app}

Only for single-prompts

Eval alignment _{Statistics for how well LLM metrics align with human annotation}

Eval on AI connections _{Reach any AI app through HTTP requests for experimentation}

Online and offline evals _{Run metrics on both production and development traces}

Multi-turn simulations _{Simulate user conversations with AI conversational agents}

Multi-turn dataset format _{Scenario-based datasets instead of input-output pairs}

Native multi-modal support _{Support images in datasets and metrics}

Limited

Testing reports & regression testing _{Allow regression testing and stakeholder sharable testing reports}

LLM Metrics _{Supports LLM-as-a-judge metrics for AI agents, RAG, multi-turn, and custom ones}

50+ metrics for all use cases, single and multi-turn, research-backed custom metrics, powered by DeepEval

Limited metrics, heavy setup required

Non-technical friendly test case format _{Upload CSVs as datasets that does not assume any technical knowledge}

AI app & Prompt Arena _{Compare different versions of prompts or AI apps side-by-side}

Only for single prompts

Human Annotations

Both Confident AI and Arize AI support human annotations. Confident AI is more opinionated in its design and is extremely generous to annotation teams.

Confident AI

Arize AI

Free tier _{Based on monthly usage}

Unlimited annotations and annotation queues, forever data retention

Included in free tier (25k spans, 7 days retention)

Core Features

Reviewer annotations _{Annotate on the platform}

Annotations via API _{Allow end users to send annotations}

Custom annotation criteria _{Allow annotations to be of any criteria}

Annotation on all data types _{Annotations on traces, spans, and threads}

Custom scoring system _{Allow users to define how annotations are scored}

Yes, either thumbs up/down or 5 star rating system

Yes, numerical and category-based

Curate dataset from annotations _{Use annotations to create new rows in datasets}

Only for single-turn

Export annotations _{Export via CSV or APIs}

Annotation queues _{A focused view on annotating test cases, traces, spans, and threads}

Prompt Engineering

Both Confident AI and Arize AI offer prompt management capabilities, with Confident AI offering more customizations in templating.

Confident AI

Arize AI

Free tier _{Based on monthly usage}

1 prompt, unlimited versions

Contact sales for details

Core Features

Text and message prompt format _{Strings and list of messages in OpenAI format}

Custom prompt variables _{Support variables that can be interpolated at runtime}

Advance conditional logic _{Support if-else statements, for-loops}

Yes, supported via Jinja formats

Limited

Prompt versioning _{Manage different versions of the same prompt}

Manage prompts in code _{Use, upload, and edit prompts via APIs}

Run prompts in playground _{Compare prompts side-by-side}

Link prompts to traces _{Find which prompt version was used in production}

AI Red Teaming

Confident AI offers red teaming for AI applications—Arize AI does not. With red teaming, you can automatically scan for security and safety vulnerabilities in your AI system in under 10 minutes.

Confident AI

Arize AI

Free tier _{Based on monthly usage}

Red teaming on enterprise-only

Not supported

Core Features

LLM Vulnerabilities _{Library of prebuilt vulnerabilities such as bias, PII leakage, etc.}

Adversarial Attack Simulations _{Simulate single and multi-turn attacks to expose vulnerabilities}

Industry frameworks and guidelines _{OWASP Top 10, NIST AI, etc.}

Customizations _{Custom vulnerabilities, frameworks, and attacks}

Red team any AI app _{Reach AI apps through the internet to red team}

Purpose-specific red teaming _{Get use case tailored attacks based on AI purpose}

Risk assessments _{Generate risk assessments that contains things like CVSS scores}

Pricing

Both platforms offer paid tiers, but with different pricing philosophies.

Confident AI uses a transparent pricing model based on usage and user seats. Costs are predictable—measured by trace count rather than tokens or storage—so you can forecast spend before you scale.

Arize AI's pricing reflects its enterprise ML monitoring roots, with custom pricing for most plans. For teams prioritizing budget transparency, this can make cost planning difficult.

But pricing tells only part of the story. Confident AI's pricing reflects what you're getting:

Multi-turn simulations for testing conversational agents, bringing hours down to minutes per multi-turn evaluation, easily 30x in time saved
Features for cross-functional teams—non-technical teams can easily test multi-prompt AI systems, instead of making engineers a bottleneck in the AI quality assurance process
Red teaming—secure testing is something everyone eventually needs, don't double-pay for vendors on this
Enterprise support—work sessions to come up with the optimal evals strategy with the authors of DeepEval ensures you get the most ROI out of observability

The trade-off is straightforward: Arize AI has deep ML monitoring roots. Confident AI has deeper LLM evaluation capabilities. Choose based on whether you need traditional ML observability or comprehensive AI quality infrastructure.

Security and Compliance

Both Confident AI and Arize AI are enterprise ready.

Confident AI

Arize AI

Data residency _{For users that want to be all over the place}

US and EU

US, EU, and CA

SOC II _{For customers with a security guy}

HIPAA _{For customers in the healthcare domain}

GDPR _{For customers with a focus in EU}

2FA _{For users that want extra security}

Social Auth (e.g. Google) _{For users that don't want to remember their passwords}

Custom RBAC _{For organizations that need fine-grained data access}

Team plan or above

1 organization on free/pro, space-level RBAC on enterprise

SSO _{For organizations that want to standardize authentication}

Team plan or above

Enterprise only

InfoSec Review _{For customers with a security questionnaire}

Team plan or above

Enterprise only

On-Prem Deployment _{For customers with strict data requirements}

Enterprise only

Why Confident AI is the Best Arize AI Alternative

Although both are feature-rich LLM observability platforms, Confident AI stands out because it centralizes everything related to AI quality—observability, evaluations, simulations, and red teaming—while offering a UI intuitive enough for non-technical teams to use.

On paper, the two platforms may look similar. In practice, Confident AI unlocks more ROI by:

Empowering non-technical team members to run an end-to-end, AI app iteration cycle without touching a line of code, instead of single-prompt testing
Including multi-turn simulations that save hours of manual testing for conversational use cases
Offering red teaming out of the box—security testing for AI apps that every production system eventually needs
Delivering more functionality across the board for teams serious about AI quality

Arize AI is a strong choice if deep ML model monitoring and technical analysis are your priorities. But if you want industry-standard evals baked into your observability stack, don't want to stitch together separate tools for simulations and red teaming, and need a platform accessible to your entire team—Confident AI delivers more value.

Getting started is easy, and the best way to see the difference is to try it yourself for free.

When Arize AI Might Be a Better Fit

Arize AI excels in specific scenarios where Confident AI may not be the optimal fit:

Traditional ML model monitoring: If your organization has existing ML models beyond LLMs that need monitoring, Arize's heritage in ML observability means you get a unified platform for both traditional ML and LLM monitoring.
Engineering-only workflows: If your AI quality process is purely engineering-driven with no need for cross-functional collaboration, Arize's technical-first interface may suit your team's preferences.
Deep technical analysis: For data science teams comfortable with technical concepts and optimizing for deep analysis over quick iteration, Arize's engineering-centric design may feel more natural.

The bottom line: Both platforms solve real LLM observability problems. Choose Arize AI if you need unified ML monitoring across traditional and LLM models, or if your workflow is purely engineering-driven. Choose Confident AI if you need evaluation depth, multi-turn support, or a platform designed for your entire team—not just engineers.

The best way to decide? Try both on your actual use case.