‼️ Top 5 Arize AI Competitors & Alternatives, Compared 💥⚖️

TL;DR 📚

Arize AI is great for LLM observability. Depending on what you need though, its feature set might not always be ideal for every use case. If you care more about evaluating the performance of your LLM apps, you should be using something like Confident AI or Giskard, while for tracing and observability, there are other cheaper options such as Langsmith.

Let's begin!

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What Do People Like & Don't Like About Arize AI?

Arize AI is a platform to monitor and evaluate LLM applications. It's main product, Phoenix, is great for debugging LLM applications such as AI agents (for customer support, for example), and can be used to evaluate their performances as well. Originally built for more ML focused workflows, they have since pivoted into focusing on LLMs since 2023.

However, depending on your use case (and budget) 🚩, you may find that Arize AI may or may not be the right fit for your use case. In this article, we'll list out the top 5 alternatives that you must consider in 2025 before deciding whether Arize is right for you.

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1. Confident AI - The DeepEval LLM Evaluation Platform

Confident AI is the cloud platform for DeepEval, one of the world's most popular and adopted open-source LLM evaluation framework. It is well known for unit-testing LLM applications ✅

Key differences

As the name suggest, it is most known for its laser focus on LLM evaluation. While Arize AI offers evaluations in its spans and traces during LLM observability through one-off debugging, Confident AI focuses on the custom benchmarking of LLM applications instead.

This means:

1. More controllable and customizable metrics
2. Evaluation results are more accurate
3. Easier for entire organizations to collaborate on testing LLMs
4. Scales to LLM safety testing

With Confident AI, you're able to easily A|B test different iterations of your LLM application with a side-by-side, GitHub like diff view of all regressions and improvements. Arize AI, on the other hand, focuses more on one-off debugging.

They also target slightly differently in the LLM development lifecycle. Arize is more for production monitoring while Confident AI for LLM evaluation before deployment. They both do the other part well as well however.

Side by side comparison summary

We'll go down the feature list so you can make a more informed decision on which is best for you.

Metrics

Feature	Confident AI	Arize AI
Out-of-the-box metrics	50+	10+
RAG metrics	✅	✅
Conversation (chatbot) metrics	✅	❌
Agent metrics	✅	✅
Research-backed custom metrics	✅	❌
Deterministic LLM-as-a-judge metrics	✅	❌
Open-source	✅	✅
Integrates with any LLM	✅	❌
Can be run locally in code	✅	❌
Can be run on the cloud	✅	✅
Auto improves	✅	❌

For open-source users, Confident AI allows you to use literally any LLM for evaluation metrics, whereas Arize AI metrics are limited to the LLMs available on their platform.

Platform

Feature	Confident AI	Arize AI
Evaluation	50+	10+
Dataset Management	✅	✅
Prompt Management	✅	✅
Metric Alignment	✅	❌
Human Feedback	✅	✅
LLM Observability	✅	✅

From afar, no big differences here. Let's dive deeper into each feature on the platform.

Evaluation

Feature	Confident AI	Arize AI
Testing Report	✅	✅
A/B Experimentation	✅	🚧
Regression Testing	✅	❌
Side-by-side evaluation comparisons	✅	❌
Statistical metric scores analysis	✅	❌
Publicly sharable testing report	✅	❌
Advanced filtering for metrics/test cases	✅	❌
Human labelling for metrics	✅	✅
Metric score accuracy validation (confusion matrix)	✅	❌
Scales to safety testing	✅	✅

Although Arize supports LLM evaluation features, there's a lot of things that doesn't scale well into >100s of test cases. This means it will be harder to benchmark LLM applications that is required for experimentation and to satisfy external stakeholders through publicly sharable testing reports.

🌟 Visit Confident AI Website

Dataset management

Feature	Confident AI	Arize AI
100% DeepEval integration	✅	❌
Dataset editor	✅	✅
Uploading datasets from CSV	✅	✅
Push/pull datasets in code	✅	✅
Create datasets from production data	✅	✅
Create Datasets from testing reports	✅	❌
Comment on datasets	✅	❌
PIT recovery	✅	❌
Dataset backup	✅	✅
Revision history	✅	✅
Custom columns	✅	✅
RAG support	✅	❌
"Finalized" flag	✅	❌

Arize and Confident is mainly the same here. Confident does have a little bit of an edge in terms of dataset collaboration where comments can be left by domain experts while engineers can focus on building to ensure these test cases pass.

Prompt management

Feature	Confident AI	Arize AI
100% DeepEval integration	✅	❌
Prompt editor	✅	✅
Prompt auto versioning	✅	✅
Dynamic prompt variables	✅	✅
Can be used for evaluation	✅	❌
Can be used for observability	✅	❌

Arize has prompt management support but not as integrated for evaluation and observability.

LLM observability

Feature	Confident AI	Arize AI
LLM output monitoring	✅	✅
Integrated LLM tracing	✅	✅
Custom LLM tracing	✅	✅
Has chatbot specific monitoring	✅	❌
Real-time evaluations	✅	✅
Human feedback leaving	✅	✅
Advance filtering for prompts and models	✅	❌
Advance filtering for custom properties	✅	❌

Arize AI focuses more on deep, detailed debugging while Confident AI's observability is for monitoring the output of each LLM interaction, with tracing included.

Support, Security & Others

Feature	Confident AI (Premium)	Arize AI (Pro)
Pricing	Monthly	Monthly
User roles & permissions	✅	❌
SOC2 Type II	✅	❌
HIPAA	✅	❌
Data Retention	1 year	6 months
Support	Dedicated	Community + email

Both providers are compliant however for their enterprise tier.

Which One Should You Chooose?

Arize AI's great for debugging, while Confident AI is great for LLM evaluation and benchmarking. Both has their own strengths and weaknesses, and has overlap in features, but it ultimately depends whether you care more about evaluation or observability.

If you want to do both, go for Confident AI, since LLM observability is the same for most providers anyway.

🌟 Visit Confident AI Website

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2. Giskard - Secure your LLM Agents

• Primary Use Case: Testing and debugging LLMs before deployment
• Features:
  • Focuses on pre-deployment testing and model validation.
  • Helps identify biases, vulnerabilities, and errors in LLMs before production.
  • Provides automated testing and explainability tools.
  • Can be used for unit testing LLMs, similar to software testing frameworks.
  • Helps ensure compliance with AI safety and fairness guidelines.
• Ideal for: LLM teams who want to debug models, ensure robustness, and prevent issues before deployment.

Key Differences

Feature	Arize AI	Giskard
Focus Area	Production monitoring & observability	Pre-deployment testing & debugging
Data Drift Detection	✅	❌
Bias & Fairness Testing	❌	✅
Root Cause Analysis	✅	❌
Explainability	✅	✅
Automated LLM Testing	❌	✅
Compliance & Safety Checks	❌	✅

Which One Should You Choose?

• If you need to monitor production LLMs for drift and performance degradation, go with Arize AI.
• If you need to test and debug LLMs before deployment, go with Giskard.
• If you need both testing and monitoring, you might consider using both together.

Visit Giskard Website

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3. Lunary - AI Developer Platform

• Primary Use Case: LLM chatbot observability, evaluation, and debugging
• Features:
  • Provides logging, monitoring, and analytics for LLM chatbots.
  • Tracks conversation history, user feedback, and model performance.
  • Supports prompt versioning, management, and collaboration.
  • Measures cost, latency, token usage, and model performance metrics.
  • Offers both cloud-hosted and self-hosted deployment options.
• Ideal for: Teams developing and deploying LLM chatbots who need monitoring, evaluation, and debugging capabilities.

Key Differences

Feature	Arize AI	Lunary
Focus Area	LLM agents	LLM chatbots
Root Cause Analysis	✅	❌
Logging and Tracing	✅	✅
Prompt Versioning	❌	✅
Cost and Token Tracking	✅	✅
Automated LLM Testing	❌	❌
Compliance and Security	✅	✅ (SOC 2, ISO 27001)

Which One Should You Choose?

• If you need to track, debug, and evaluate LLM applications with logging, analytics, and user feedback, choose Lunary. It helps teams iterate on prompts, detect hallucinations, and analyze costs before and after deployment.
• If you need a solution focused on production monitoring with real-time performance tracking and drift detection, choose Arize AI. It is designed for LLM observability at scale, ensuring models remain reliable in deployment.

Visit Lunary Website

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4. Datadog - Modern monitoring & security

Datadog is not LLM specific, but it does offer some good features compared to Arize AI.

• Primary Use Case: General application monitoring, logging, and infrastructure observability
• Features:
  • Provides monitoring for servers, databases, and cloud services with real-time dashboards.
  • Supports log management, distributed tracing, and security monitoring across applications.
  • Detects anomalies and performance bottlenecks in system infrastructure.
  • Offers alerting and automated incident response for system failures.
  • Integrates with various cloud providers, DevOps tools, and microservices architectures.
  • Focuses on infrastructure observability rather than model-specific insights.
  • Weaker than Arize AI when it comes to LLM evaluation, as it lacks built-in model performance tracking, data drift detection, and detailed LLM-specific analytics.

Key differences

Feature	Datadog	Arize AI
Focus Area	Infrastructure and application monitoring	LLM observability and performance tracking
Model Performance Monitoring	❌	✅
LLM Drift Detection	🚧	✅
Logging and Tracing	✅	✅
Root Cause Analysis	🚧	✅
Security and Compliance	✅	❌
Application Performance Monitoring	✅	❌
LLM Evaluation and Debugging	🚧	✅

Which one should you choose?

• If you need to monitor system infrastructure, application performance, and security events, choose Datadog. It is best suited for DevOps and cloud-native applications that require end-to-end observability.
• If you need to monitor LLMs in production, detect model drift, and analyze performance issues, choose Arize AI. Arize is significantly stronger in LLM evaluation, providing model-specific insights, drift detection, and performance tracking that Datadog lacks.

Visit Datadog Website

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5. MLFlow - ML and GenAI made simple

As the name suggest, MLFlow is undecided on whether to focus on traditional ML or GenAI. I would not recommend MLFlow if you don't have traditional ML workflows to satisfy for this reason.

• Primary Use Case: Experiment tracking, model management, and deployment
• Features:
  • Tracks and logs ML experiments, including parameters, metrics, and artifacts.
  • Provides a central model registry for versioning and managing models.
  • Supports model packaging for deployment in multiple environments.
  • Enables reproducibility by logging code, dependencies, and environment configurations.
  • Integrates with various ML frameworks, including TensorFlow, PyTorch, and Scikit-learn.
  • Allows deployment of models to cloud services, on-premises, and edge devices.
  • Offers APIs and a UI for tracking and managing experiments.
  • Supports collaborative workflows for ML teams.
  • Provides lifecycle management for ML models, from development to production.

Which One Should You Choose?

• If you need to track experiments, manage model versions, and handle deployments, choose MLflow. It is best suited for the early stages of the LLM lifecycle, helping teams develop, iterate, and manage models before deployment.
• If you need to monitor LLMs in production, detect performance issues, and analyze model drift, choose Arize AI. It is specifically designed for LLM observability, helping teams detect data drift, hallucinations, and degradation over time.
• If your workflow involves both training and production monitoring, consider using MLflow for experiment tracking and Arize AI for post-deployment monitoring.

Visit MLFlow Website

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Conclusion

So there you have it, the list of the top 5 Arize AI alternatives in 2025. Think there's something I've missed? Comment below to let me know!

Thank you for reading, and till next time 😊

Comments

[Avid Reader]

Thank you for the informative post

[John Gilhuly]

Good breakdown! But I think a good chunk of this is already out of date.

E.g. Arize does have prompt management: docs.arize.com/phoenix/prompt-engi...

[Jeffrey Ip]

Thanks, just updated it!

[Artisan]

What about Langsmith?

[Jeffrey Ip]

Langsmith didn't make the list