Top 5 Arize AI Alternatives and Competitors, Compared

Arize AI started off as an ML model monitoring platform for data science teams—and that heritage shows in its LLM observability offering. The UX assumes you're comfortable with technical concepts, the workflows prioritize engineering personas, and the interface is optimized for deep technical analysis over quick collaborative iteration.

This isn't a flaw if your AI quality process is purely engineering-driven. But when product managers need to review evaluation results, domain experts want to flag problematic outputs, or QA teams need to upload test datasets without writing Python, the engineering-centric design creates friction.

In this guide, we'll examine the top Arize AI alternatives, comparing how different platforms balance observability depth with cross-functional accessibility—because your platform choice determines whether AI testing stays siloed in engineering or becomes a true team effort.

Why AI Observability Alone Isn't Enough

If your engineering team doesn't already use Datadog, Honeycomb, or New Relic for infrastructure observability, you have bigger problems than choosing an AI platform. The reality is that most organizations are well-covered on tracing and monitoring — what they're missing is a systematic way to measure and improve AI quality. That means evaluation workflows that catch regressions before deployment, multi-turn simulations that stress-test conversational AI at scale, red teaming that surfaces safety vulnerabilities, annotation systems that turn domain expertise into measurable improvements, and production monitoring that alerts on AI quality degradation — not just latency spikes or error rates, but actual drops in output faithfulness, relevance, and safety.

Several platforms in this guide emphasize observability as their core value proposition, but logging what happened is fundamentally different from preventing what shouldn't happen. As you compare the alternatives below, pay attention to which platforms treat evaluation as the product and observability as the infrastructure — not the other way around.

Our Evaluation Criteria

Choosing the right LLM observability tool requires balancing technical capabilities with business needs. Based on our experience, the most critical factors include:

• Evaluation maturity: Some platforms bolt evaluation onto observability as an afterthought. Others build observability around evaluation. The distinction matters: are the metrics widely validated by research? Can you create custom evaluators easily? Does the platform prioritize testing quality or just logging what happened?

• Observability breadth and depth: Beyond supporting popular frameworks (OpenTelemetry, LangChain, OpenAI), can you drill down into specific agent components? Can you filter thousands of traces efficiently? Can you evaluate directly on production traffic?

• End-to-end non-technical workflows: This is the critical question: Can a product manager or domain expert run a complete iteration cycle independently? Upload a test dataset, trigger your production AI application for evaluation, review the results, and make decisions—all without asking engineering for help? Or does the workflow require engineer intervention at multiple steps?

• Setup friction and integration complexity: Does your engineering team spend two days configuring SDKs, or two hours? Enterprise deployments need smooth integration with existing infrastructure, while developers need SDKs that work out of the box without extensive documentation diving.

• Data portability and platform flexibility: If you decide to switch platforms in 18 months, how painful is the migration? API access to your trace data, custom dashboard building capabilities, and standard export formats determine whether you own your data or your platform owns you.

• Annotation and feedback loops: When domain experts spot issues in production traces, can they annotate them inline? Do those annotations feed back into your evaluation datasets? Can you export annotations for fine-tuning workflows?

With these criteria in mind, let's examine how the top Arize AI alternatives stack up across these dimensions.

1. Confident AI

• Founded: 2023

• Most similar to: LangSmith, Langfuse, Arize AI

• Typical users: Engineers, product, and QA teams

• Typical customers: Mid-market B2Bs and enterprises

What is Confident AI?

Confident AI is an LLM evals and observability platform that combines evals, tracing, A|B testing, dataset management, human-in-the-loop annotations, and prompt versioning to test AI apps in one collaborative platform.

It is built for engineering, product, and QA teams, and is native to DeepEval, a popular open-source LLM evaluation framework.

Key features

• • 🧮 Comprehensive eval coverage spanning 50+ single-turn metrics, 15+ multi-turn metrics, built-in multimodal support, LLM-as-a-judge capabilities, and extensible custom metrics like G-Eval. All evaluation logic is open-source through DeepEval.

• 🧪 Complete evaluation workflows from code-driven testing to no-code interfaces, supporting shareable test reports, side-by-side A/B comparisons, performance analytics across prompts and models, conversational testing, and custom dashboard creation for stakeholder visibility.

• 🌐 Unified observability and tracing connecting development testing to production monitoring via OpenTelemetry and 10+ framework integrations (OpenAI, LangChain, Pydantic AI). Run evaluations in real-time as traces are captured or retrospectively on historical data.

• 🗂️ Collaborative dataset workflows handling both single-turn and conversational test data, with annotation task distribution, full version history, and automated backup protection.

• 📌 Prompt lifecycle management supporting simple text templates and complex message-based prompts, with variable substitution and one-click deployment to production environments.

• ✍️ Expert-driven annotation system enabling domain specialists to flag issues directly on production traces, individual spans, or entire conversation threads, with annotations exportable back into test datasets for continuous improvement.

Who uses Confident AI?

Confident AI is also the only platform offering end-to-end evaluation workflows that actually trigger your production AI application, meaning Confident AI serves organizations where AI quality assurance extends beyond the engineering department:

• Development teams running automated testing pipelines and CI/CD integration for continuous validation

• Product leaders coordinating evaluation workflows with domain experts who annotate and validate AI outputs

• Dedicated AI QA functions modernizing testing infrastructure beyond traditional software QA approaches

• Cross-functional teams monitoring model performance degradation and behavioral drift in production environments

The customer base ranges from Series A startups through enterprise deployments, with customers including Panasonic, Syngenta, Phreesia, CircleCI, and Humach.

How does Confident AI compare to Arize AI?

Confident AI's evals-first approach ensures your entire organization are able to collaborate on AI quality and reliability, extending beyond standard LLM observability:

Confident AI is the only platform offering end-to-end evaluation workflows that actually trigger your production AI application for testing. It is as simple as calling your AI app in Postman —not just analyze logged traces retrospectively.

This eliminates the typical bottleneck where product managers draft prompt changes but need engineers to instrument, deploy, and test them. Non-technical team members can upload datasets, call production apps, run evaluations, and review results independently, saving 20+ hours of engineering time per week previously spent on manual testing support.

For conversational AI, automated multi-turn simulations compress 2-3 hours of manual testing per experiment into under 5 minutes. Built-in red teaming consolidates security testing without requiring separate tool licensing.

Arize AI focuses on analyzing existing traces rather than triggering live application calls, meaning teams still need manual processes for controlled pre-deployment testing. The engineering-centric interface maintains rather than eliminates the bottleneck.

How popular is Confident AI?

Confident AI is an AI observability and evals platform powered by DeepEval, and as of December 2025, DeepEval has become the world’s most popular and fastest growing LLM evaluation framework in terms of downloads (3million+ monthly), and 2nd in terms of GitHub stars (runner-up to OpenAI’s open-source evals repo).

More than half of DeepEval users end up using Confident AI within 2 months of adoption.

Why do companies use Confident AI?

Companies use Confident AI because:

• True cross-functional collaboration: Engineers handle the initial SDK integration, then product managers and domain experts can run complete evaluation cycles independently—upload datasets, trigger tests, review results—without touching code or requesting engineering support.

• No-code evaluation workflows: Non-technical team members can create test cases, run A/B comparisons, and analyze results through the UI while engineers maintain full API access for programmatic control when needed.

• Multi-turn conversation testing: Automated simulations eliminate the manual effort of testing conversational AI, compressing hours of manual prompting into minutes of automated testing across dozens of conversation scenarios.

• Evaluation-first architecture: Rather than bolting evaluation onto observability as an afterthought, the platform is built around systematic testing with observability as the supporting layer.

Bottom line: Confident AI is the best Arize AI alternative for organizations where AI testing involves more than just your engineering department. The combination of no-code workflows for non-technical users and comprehensive multi-turn evaluation eliminates the typical pattern of maintaining separate platforms for different team personas or use cases.

2. Helicone

• Founded: 2023

• Most similar to: Langfuse, Arize AI

• Typical users: Engineers and product

• Typical customers: Startups from early to growth stage

What is Helicone?

Helicone is an open-source platform that offers observability on top of an unified AI gateway for teams to build reliable AI apps.

Key Features

• ⛩️ AI gateway providing access to 100+ large language model providers through OpenAI's SDK interface

• 📷 Model observability for monitoring and evaluating API calls by usage costs, failure rates, and applying metadata tags to LLM requests for granular filtering

• ✍️ Prompt management for building and optimizing prompts, then deploying them directly into your LLM applications via the AI Gateway

Who uses Helicone?

Common Helicone users include:

• Engineering teams requiring unified access to multiple LLM providers

• Startup companies that prioritize rapid deployment and precise expense monitoring

Helicone emphasizes its AI gateway capabilities, with observability features centered on API requests rather than full application tracing. Notable customers include QA Wolf, Duolingo, and Singapore Airlines.

How does Helicone compare to Arize AI?

Helicone focuses on LLM-specific observability at the API gateway level, while Arize AI operates across the broader AI application level with comprehensive tracing.

Helicone also provides an intuitive UI designed for non-technical teams, making it an ideal alternative for those prioritizing AI gateway functionality, cost-efficient observability, and simplified access to multiple LLM providers.

How popular is Helicone?

Helicone is less popular than Arize AI sitting at 4.4k GitHub stars. However, Helicone's focus on LLM-specific observability and developer-friendly pricing makes it increasingly popular among teams, especially startups, building GenAI applications.

Why do companies use Helicone?

• 100% Open-source: Arize AI is only open-source through Phoenix - being open-source means teams can try it out locally quickly before deciding if a cloud-hosted solution is right for them

• Works with multiple LLMs: Helicone's AI gateway capability enables unified access to 100+ LLM providers, a unique advantage for teams managing diverse model architectures

Bottom line: Helicone is the best alternative if you need lightweight LLM observability with AI gateway functionality rather than enterprise-wide ML monitoring. It's open-source and focused specifically on generative AI, making it faster to deploy and easier to navigate data security requirements.

For teams that are operating at the application layer, and need full-fledged LLM-tracing, and evaluations, there are other alternatives more suited.

3. Langfuse

• Founded: 2022

• Most similar to: Confident AI, Helicone, LangSmith

• Typical users: Engineers and product

• Typical customers: Startups to mid-market B2Bs

What is Langfuse?

Langfuse is a fully open-source platform built for LLM engineering. In practice, this means it helps teams observe, manage, and evaluate their LLM applications through capabilities like tracing, prompt versioning, and evaluations.

Key Features

• ⚙️ LLM tracing, similar to tools like LangSmith, but with broader integration support. It’s designed to be quick to set up and includes features such as data masking, sampling controls, multiple environments, and more.

• 📝 Prompt management allow teams to version and manage prompts outside of application code. This makes it easier to experiment, collaborate, and deploy changes without tightly coupling prompts to engineering releases.

• 📈 Evaluation allows users to score and evaluate traces over time, helping teams track model quality, performance trends, costs, and errors in a structured way.

Who uses Langfuse?

Langfuse is commonly used by:

• Engineering teams that require on-prem or self-hosted data control

• Teams that want full ownership of their prompts and observability stack within their own infrastructure

With a strong emphasis on open-source LLM observability, Langfuse is trusted by organizations such as Twilio, Samsara, and Khan Academy.

How does Langfuse compare to Arize AI?

Langfuse should not be confused as being part of the “LangChain” ecosystem. In practice, it overlaps closely with platforms like Arize AI across LLM observability, evaluations, and prompt management, and the core capabilities are largely comparable.

Arize AI is slightly stronger in evaluation depth and analysis, especially for teams that want more built-out evaluation workflows. That said, the gap is not large. Langfuse differentiates itself mainly through its fully open-source model and developer-friendly experience, along with self-hosting and unlimited users across all pricing tiers, which can reduce friction for teams getting started.

How popular is Langfuse?

Langfuse is widely adopted largely because it is fully open source, with strong visible developer usage, while equivalent adoption data for Arize AI’s commercial platform is not publicly available. Arize Phoenix, Arize AI’s open-source offering, has gained traction with roughly 8k GitHub stars, but it represents a more limited subset of the broader Arize AI platform rather than a full open-source LLMOps solution.

Why do companies use Langfuse?

• 100% open-source: Because Langfuse is fully open source, teams can self-host it and maintain full control over their data. This makes it easier to adopt in environments with stricter privacy or compliance requirements.

• Less expensive: Langfuse’s pricing model and self-hosted option reduce upfront costs and procurement friction, especially for engineering-led teams.

Bottom line: Langfuse offers functionality very similar to LangSmith, but in a fully open-source form with a slightly better developer experience. For companies that need an on-prem, self-hosted solution or want to avoid security and procurement hurdles, Langfuse is a strong option.

For teams without these constraints—especially those that need more non-technical workflows or more streamlined evaluation tooling—other platforms may offer better overall value.

4. LangSmith

• Founded: 2022

• Most similar to: Confident AI, Langfuse, Arize AI

• Typical users: Engineering teams

• Typical customers: Mid-market B2Bs to enterprises

What is LangSmith?

LangSmith is a closed-source alternative to for Langfuse. This means they offer LLM tracing, prompt management, evals, to most of which that are also offered by Langfuse, but being closed-source instead.

LangSmith is the only contender on this list without an open-source component.

Key Features

• ⚙️ LLM tracing: Similar to Langfuse's offering, though Langfuse supports more integrations and includes open-source features like data masking, sampling, environment management, and more.

• 📝 Prompt management: Version prompts and develop applications without hardcoding prompts into your codebase.

• 📈 Evaluation: Score traces and track performance over time, alongside cost and error monitoring.

Who uses LangSmith?

Typical LangSmith users are:

• Engineering teams that are already using other products in the "Lang" ecosystem (.e.g LangChain and LangServe)

• Teams that are technical and has a strong focus on observability

LangSmith puts a strong focus on observability. Customers include Workday, Rakuten, and Klarna.

How does LangSmith compare to Arize AI?

How popular is LangSmith?

LangSmith is one of the most popular LLMops platforms out there due to it being the enterprise platform for LangChain.

Why do companies use LangSmith?

• Tight LangChain integration: As the native observability solution from the LangChain team, LangSmith offers seamless integration with LangChain and LangGraph—ideal for teams already deeply invested in that ecosystem.

• Enterprise-grade support: LangSmith provides dedicated support and managed infrastructure, which can be valuable for organizations that prefer vendor-backed reliability over self-hosted open-source solutions.

Bottom line: LangSmith is basically Langfuse, but closed-source with slightly better experience for non-technical users. For companies wanting enterprise support LangSmith is a great alternative.

For teams that want to be able to self-host an LLMOps platform, or want more evals-focused features, there are other better-valued alternatives.

5. MLFlow

• Founded: 2018

• Most similar to: Arize AI, Weights & Biases

• Typical users: Engineering teams

• Typical customers: Mid-market B2Bs to enterprises

What is MLFlow?

MLflow is an open-source platform for managing the machine learning lifecycle, and has recently expanded to support GenAI and LLM workflows. It now offers LLM experiment tracking, prompt logging, and evaluation capabilities, overlapping with areas covered by Arize AI. Unlike Arize AI’s commercial platform, MLflow is fully open source, but similar to Arize AI was not originally designed as a production-first LLM observability tool.

Key Features

• ⚙️ Experiment tracking and tracing MLflow tracks runs, metrics, artifacts, and now LLM traces for GenAI workflows. However, observability is more experiment-centric and less focused on production monitoring compared to Arize AI.

• 📝 Prompt and artifact management Prompts, inputs, and outputs can be logged and versioned as artifacts, but MLflow does not offer dedicated prompt management workflows out of the box.

• 📈 Evaluation MLflow provides built-in support for LLM evaluations, including automated and LLM-as-judge metrics. These capabilities are solid, though generally less opinionated and less turnkey than Arize AI’s evaluation workflows.

Who uses MLFlow?

Typical MLflow users are:

• ML and data science teams already using MLflow for traditional ML workflows

• Teams that prefer an open-source, self-hosted solution and are willing to build custom LLM observability on top

MLFlow puts a strong focus on traditional ML workflows, and customers include Microsoft, PwC, and IBM.

How does MLFlow compare to Arize AI?

MLflow has recently expanded into the GenAI/LLM space with MLflow GenAI, bringing built-in support for tracking, prompt versioning, automated evaluation with LLM-as-judge metrics, and comprehensive observability through OpenTelemetry-compatible tracing. This means teams can both evaluate quality and inspect detailed execution traces of prompts, retrievals, and model responses within the same open-source platform.

Compared to Arize AI, MLflow’s evaluation features are competitive and designed to work end-to-end, but its observability experience is still evolving and can feel less production-optimized out of the box. In practice, the choice often comes down to how much you prioritize built-in, streamlined observability and monitoring (where Arize tends to feel stronger) versus an open, flexible ecosystem with experiment tracking, prompt/version management, and community extensibility (MLflow’s strength).

How popular is MLFlow?

MLflow is one of the most widely used open‑source platforms in the ML ecosystem, with over 45 million monthly downloads and a large, active community contributing to its development. It has around 17,000+ stars on GitHub, sits in the top fraction of PyPI packages by download count, and is used by thousands of companies globally across industries for experiment tracking, model management, and now GenAI workflows.

Why do companies use MLFlow?

• All-in-one MLOps platform: MLflow provides end-to-end lifecycle management for machine learning and GenAI projects, covering experiment tracking, model and artifact versioning, prompt logging, and LLM evaluation—all in a single open-source platform.

• Flexible and open-source: Being fully open source, MLflow lets teams self-host, maintain full control over their data, and integrate with existing ML workflows, making it a versatile choice for organizations of all sizes.

Bottom line MLflow is an all-in-one MLOps solution that balances experiment management, evaluation, and operational flexibility. Teams that want a self-hosted, customizable, evaluation-focused platform often choose MLflow, while those prioritizing turnkey production observability may consider other options like Arize AI.

Why Confident AI is the Best Arize AI Alternative

Confident AI provides a full end-to-end, no-code evaluation cycle for LLMs, enabling entire teams to iterate quickly without depending on engineers. Companies like CircleCI, Panasonic, and Amazon use it to let non-technical personas—product managers, QA teams, and domain experts—run evaluations independently. Product managers can upload datasets and run evals, domain experts annotate traces and align them with metrics, and QA teams set up regression tests in CI/CD—all through an intuitive no-code UI. Engineers keep full programmatic control but are no longer the bottleneck for every testing decision.

Arize AI offers strong ML ops and evaluation capabilities, and its open-source component (Arize Phoenix) exists, but its workflows are primarily engineer-focused. Non-technical users often feel out of place running annotations or evaluations in the same platform where ML engineers manage models, which can slow iteration.

This workflow difference drives measurable ROI. Multi-turn simulations compress hours of manual conversation testing into minutes, built-in red teaming removes the need for separate security vendors, and no-code evaluation cycles save teams 20+ hours per week when product, QA, and domain experts can run tests independently.

Confident AI’s metrics are research-backed and battle-tested at companies like OpenAI and Google. For teams already using DeepEval locally, Confident AI extends those workflows seamlessly to the cloud, empowering the whole team—not just engineers—to own LLM quality.

When Confident AI might not be the right fit

• If you need fully open-source: Confident AI is cloud-based with enterprise security standards. Confident AI can also be easily self-hosted, but this is not open-source, unlike Phoenix.

• If you don't need to support non-technical workflows: If supporting non-technical teams are not a strict requirement, then a tool like Arize AI can be a viable alternative.

Frequently Asked Questions

Does Arize AI support no-code evaluation workflows?

Arize AI's evaluation capabilities are primarily designed for technical users and require engineering involvement to set up and run. The platform does not offer end-to-end no-code evaluation workflows where non-technical team members can independently upload datasets, trigger tests against production AI applications, and review results. Confident AI is the primary alternative that provides complete no-code evaluation cycles, enabling product managers, QA teams, and domain experts to run evaluations without engineering support.

What are the limitations of Arize AI?

Arize AI's main limitations include its engineering-centric interface that creates friction for non-technical users, limited multi-turn conversation evaluation capabilities, and the lack of no-code workflows for cross-functional teams. Its evaluation features require significant setup compared to purpose-built evaluation platforms, and its open-source component (Arize Phoenix) covers only a subset of the full platform's functionality. Teams that need collaborative AI quality workflows across engineering, product, and QA often find these gaps slow down iteration cycles.

Is Arize AI suitable for non-technical teams?

Arize AI is optimized for engineering and data science personas. Non-technical users such as product managers, QA teams, and domain experts may find it difficult to independently run evaluations, annotate traces, or manage test datasets. For organizations where AI quality assurance involves cross-functional collaboration, Confident AI offers an accessible no-code interface for non-technical team members while engineers retain full programmatic control via API and SDK.

What is the best Arize AI alternative?

Confident AI is the best Arize AI alternative for teams that need cross-functional collaboration on AI quality. Unlike Arize AI's engineering-focused workflows, Confident AI provides end-to-end no-code evaluation cycles that allow product managers, QA teams, and domain experts to upload datasets, trigger evaluations against production AI applications, and review results independently—without requiring engineering support.

What is the best open-source alternative to Arize AI?

Langfuse is the best fully open-source alternative to Arize AI. It offers comparable LLM observability, prompt management, and evaluation capabilities, with the added benefit of self-hosting for teams with strict data privacy or compliance requirements. Arize AI's open-source offering, Arize Phoenix, covers a more limited subset of its full platform. For teams that need broader MLOps capabilities, MLflow is another strong open-source option. For teams that want open-source evaluation metrics paired with a cloud platform, Confident AI's DeepEval framework provides 50+ open-source metrics alongside its commercial offering.

How does Arize AI compare to Langfuse?

Arize AI and Langfuse offer similar core capabilities including LLM tracing, evaluations, and prompt management. Arize AI is slightly stronger in evaluation depth and built-in analysis workflows. Langfuse differentiates through its fully open-source model, self-hosting support, unlimited users across all pricing tiers, and a developer-friendly experience that reduces setup friction. For teams that need both strong observability and no-code evaluation workflows accessible to non-technical users, Confident AI covers both in a single platform.

How does Arize AI compare to LangSmith?

Arize AI and LangSmith both provide LLM observability, tracing, prompt management, and evaluation features. LangSmith's main advantage is tight integration with the LangChain and LangGraph ecosystem, making it ideal for teams already invested in those frameworks. Arize AI offers broader ML monitoring capabilities beyond just LLMs. Neither platform provides strong support for non-technical workflows or multi-turn conversation evaluation—areas where Confident AI differentiates.

How does Arize AI compare to Confident AI?

Arize AI focuses on analyzing existing traces and production monitoring with an engineering-centric interface. Confident AI focuses on end-to-end evaluation workflows that trigger live production AI applications for testing—similar to calling an API in Postman. Confident AI also offers multi-turn conversation simulation, built-in red teaming, no-code evaluation interfaces for non-technical users, and 50+ research-backed evaluation metrics through its open-source framework DeepEval.

Which Arize AI alternative supports multi-turn conversation evaluation?

Confident AI is the strongest alternative for multi-turn conversation evaluation. It supports automated multi-turn simulations that compress 2–3 hours of manual conversation testing into under 5 minutes, along with dedicated multi-turn datasets, conversation-level metrics, and the ability to test across dozens of conversation scenarios automatically. Most other platforms, including Arize AI, LangSmith, and Langfuse, have limited or no multi-turn evaluation support.

Which Arize AI alternative is best for startups?

Confident AI is the best Arize AI alternative for startups. It automatically generates evaluation datasets from production observability data, eliminating the time-consuming manual effort of building test sets from scratch—a major bottleneck for resource-constrained teams. Confident AI also offers more cost-effective pricing, starting at 15GB of included data compared to Arize AI's 10GB, with additional usage at $1 per GB-month versus Arize AI's $3 per additional GB stored. The GB-month model gives teams flexibility to allocate extra capacity toward either ingestion or retention based on their needs. For startups that primarily need lightweight LLM observability and multi-provider access without evaluation workflows, Helicone is another open-source option worth considering.

Which is the most affordable Arize AI alternative?

Confident AI offers the most flexible pricing among Arize AI alternatives. It uses a single GB-month unit at $1, which teams can allocate toward either ingestion or retention depending on their needs — rather than paying for separate ingestion and storage limits. By comparison, Arize AI charges $3 per additional GB of storage, and Langfuse's unit-based pricing can be difficult to forecast since traces and spans count equally regardless of payload size.