DeepEval vs Arize

TL;DR: Arize is great for tracing LLM apps, especially for monitoring and debugging, but lacks key evaluation features like conversational metrics, test control, and safety checks. DeepEval offers a full evaluation stack—built for production, CI/CD, custom metrics, and Confident AI integration for collaboration and reporting. The right fit depends on whether you're focused solely on observability or also care about building scalable LLM testing into your LLM stack.

How is DeepEval Different?

1. Evaluation laser-focused

While Arize AI offers evaluations through spans and traces for one-off debugging during LLM observability, DeepEval focuses on custom benchmarking for LLM applications. We place a strong emphasis on high-quality metrics and robust evaluation features.

This means:

• More accurate evaluation results, powered by research-backed metrics
• Highly controllable, customizable metrics to fit any evaluation use case
• Robust A/B testing tools to find the best-performing LLM iterations
• Powerful statistical analyzers to uncover deep insights from your test runs
• Comprehensive dataset editing to help you curate and scale evaluations
• Scalable LLM safety testing to help you safeguard your LLM—not just optimize it
• Organization-wide collaboration between engineers, domain experts, and stakeholders

2. We obsess over your team's experience

We obsess over a great developer experience. From better error handling to spinning off entire repos (like breaking red teaming into DeepTeam), we iterate based on what you ask for and what you need. Every Discord question is a chance to improve DeepEval—and if the docs don’t have the answer, that’s on us to build more.

But DeepEval isn’t just optimized for DX. It's also built for teams—engineers, domain experts, and stakeholders. That’s why the platform is baked-in with collaborative features like shared dataset editing and publicly sharable test report links.

LLM evaluation isn’t a solo task—it’s a team effort.

3. We ship at lightning speed

We’re always active on DeepEval's Discord—whether it’s bug reports, feature ideas, or just a quick question, we’re on it. Most updates ship in under 3 days, and even the more ambitious ones rarely take more than a week.

But we don’t just react—we obsess over how to make DeepEval better. The LLM space moves fast, and we stay ahead so you don’t have to. If something clearly improves the product, we don’t wait. We build.

Take the DAG metric, for example, which took less than a week from idea to docs. Prior to DAG, there was no way to define custom metrics with full control and ease of use—but our users needed it, so we made one.

4. We're always here for you... literally

We’re always in Discord and live in a voice channel. Most of the time, we’re muted and heads-down, but our presence means you can jump in, ask questions, and get help, whenever you want.

DeepEval is where it is today because of our community—your feedback has shaped the product at every step. And with fast, direct support, we can make DeepEval better, faster.

5. We offer more features with less bugs

We built DeepEval as engineers from Google and AI researchers from Princeton—so we move fast, ship a lot, and don’t break things.

Every feature we ship is deliberate. No fluff, no bloat—just what’s necessary to make your evals better. We’ll break them down in the next sections with clear comparison tables.

Because we ship more and fix faster (most bugs are resolved in under 3 days), you’ll have a smoother dev experience—and ship your own features at lightning speed.

6. We scale with your evaluation needs

DeepEval and Confident AI are two separate products built by the same team — not the same thing.

• DeepEval is the open-source LLM evaluation framework: metrics, test cases, datasets, synthetic data generation, benchmarks, and CI/CD evals. It runs locally, requires no account, and works fully standalone.
• Confident AI is an all-in-one enterprise platform for LLM evaluation, observability, and red teaming. It adds shared regression reports, online evals on production traces, monitoring, cloud-hosted datasets, prompt and model experimentation, red teaming campaigns, and team collaboration.

Confident AI open-sourced many of its metrics through DeepEval. That does not make them the same product, and Confident AI is not a UI layer on top of DeepEval.

Use DeepEval on its own for fast, code-first local evaluation and CI gates. Use DeepEval with Confident AI when your team needs:

• Shared dashboards for metric distributions, averages, and trends across runs
• Test reports to share internally or with external stakeholders
• Centralized cloud datasets and golden management
• Regression gates and side-by-side prompt and model experiments
• Production trace observability and online evaluation of live traffic
• Red teaming campaigns and safety testing at organization scale

The integration is built into DeepEval — connect once and every DeepEval run syncs to Confident AI without extra code.

DeepEval also pairs with DeepTeam, our open-source red teaming framework, which Confident AI's red teaming features build on the same way they build on DeepEval.

Comparing DeepEval and Arize

Arize AI’s main product, Phoenix, is a tool for debugging LLM applications and running evaluations. Originally built for traditional ML workflows (which it still supports), the company pivoted in 2023 to focus primarily on LLM observability.

While Phoenix’s strong emphasis on tracing makes it a solid choice for observability, its evaluation capabilities are limited in several key areas:

• Metrics are only available as prompt templates
• No support for A/B regression testing
• No statistical analysis of metric scores
• No ability to experiment with prompts or models

Prompt template-based metrics means they aren’t research-backed, offer little control, and rely on one-off LLM generations. That might be fine for early-stage debugging, but it quickly becomes a bottleneck when you need to run structured experiments, compare prompts and models, or communicate performance clearly to stakeholders.

Metrics

Arize supports a few types of metrics like RAG, agentic, and use-case-specific ones. But these are all based on prompt templates and not backed by research.

This also means you can only create custom metrics using prompt templates. DeepEval, on the other hand, lets you build your own metrics from scratch or use flexible tools to customize them.

Dataset Generation

Arize offers a simplistic dataset generation interface, which requires supplying an entire prompt template to generate synthetic queries from your knowledge base contexts.

In DeepEval, you can create your dataset from research-backed data generation with just your documents.

Red teaming

We built DeepTeam—our second open-source package—as the easiest way to scale LLM red teaming without leaving the DeepEval ecosystem. Safety testing shouldn’t require switching tools or learning a new setup.

Arize doesn't offer red-teaming.

Using DeepTeam for LLM red teaming means you get the same experience from DeepEval, even for LLM safety and security testing.

Checkout DeepTeam's documentation, which powers DeepEval's red teaming capabilities, for more detail.

Benchmarks

DeepEval is the first framework to make LLM benchmarks easy and accessible. Before, benchmarking models meant digging through isolated repos, dealing with heavy compute, and setting up complex systems.

With DeepEval, you can set up a model once and run all your benchmarks in under 10 lines of code.

This is not the entire list (DeepEval has 15 benchmarks and counting), and Arize offers no benchmarks at all.

Integrations

Both tools offer integrations—but DeepEval goes further. While Arize mainly integrates with LLM frameworks like LangChain and LlamaIndex for tracing, DeepEval also supports evaluation integrations on top of observability.

That means teams can evaluate their LLM apps—no matter what stack they’re using—not just trace them.

DeepEval also integrates directly with LLM providers to power its metrics—since DeepEval metrics are LLM agnostic.

Platform

DeepEval integrates natively with Confident AI, a separate all-in-one enterprise platform for LLM evaluation, observability, and red teaming, built by the same team. Arize's platform is called Phoenix.

Confident AI is built for powerful, customizable evaluation and benchmarking, with observability and red teaming in the same platform. Phoenix, on the other hand, is more focused on observability.

Confident AI is also self-served, meaning you don't have to talk to us to try it out. Sign up here.

Conclusion

If there’s one thing to remember: Arize is great for observability and debugging, while DeepEval is built for LLM evaluation and benchmarking.

Both have some feature overlap — but it really comes down to what you care about more: evaluation or observability.

If you want evaluation plus an enterprise platform that also covers observability and red teaming, pair DeepEval with Confident AI, a separate all-in-one enterprise platform for LLM evaluation, observability, and red teaming, built by the same team. DeepEval and Confident AI are not the same product: DeepEval is an open-source framework, Confident AI is an enterprise platform you can graduate into when team scale demands it. That should be more than enough to get started with DeepEval.