Confident AI vs OpenLayer: Head-to-Head Comparison

Confident AI and OpenLayer both helps you do AI evaluation and observability, but here are some important key differences:

How is Confident AI Different?

1. We’re product and engineering focused

Because we’re product- and engineering-led, setup is fast and intuitive.

• You can go from zero to running real evaluations in hours, not weeks.

• The platform feels simple out of the box — no endless training calls required.

• We have more support for emerging use cases — e.g. MCP, something OpenLayer have yet to add.

• Support comes from engineers who actually build the product, so issues are solved quickly and with real context.

The result: less overhead, faster time-to-value, and a smoother path to shipping better AI apps.

2. We support all forms of LLM evals

Because we support every type of evaluation, we can grow with your use cases.

• Single-turn prompts, multi-turn conversations, retrieval-augmented queries, and agent workflows are all supported.

• You can measure everything from accuracy and reliability to safety and bias, all in one place.

• DeepEval, our open-source framework, ensures flexibility and extensibility without vendor lock-in.

The result: no matter how your AI use cases evolve, your evaluation strategy evolves with them — seamlessly.

3. Cutting-edge evaluation, more feature-rich observability

Because Confident AI is built for observability, you don’t just get scores — you get clarity.

• Track performance over time to prevent broken models from slipping into production.

• Run evaluations on a component-level, not just the end-to-end or conversation-level.

• Add custom metrics that tie directly to your business goals, not just generic benchmarks.

The result: faster iteration cycles, fewer blind spots, and greater confidence in every deployment.

4. We support technical and non-technical workflows equally

Because engineers no longer write tests, we built Confident AI with PMs and domain experts in mind.

• No code required to edit datasets, prompts

• Trigger evaluations directly on the platform

• “Intuitive dashboard design” — Not just for engineers

5. We’re transparent and work in the open (no pun intended)

Because we work in the open, you can trust how evaluations are run and how the product evolves.

• DeepEval is open-source, actively maintained, and trusted by hunreds of thousands of engineers.

• Metric algorithms, data generations, all open-source.

• Community feedback turns directly into product improvements.

The result: you’re not just using a platform, you’re shaping it — with full visibility and no black boxes.

Product Comparison

Confident AI offers more comprehensive metrics, covering single and multi-turn use cases, as well as multi-modality, for agents, RAG, and chatbots alike.

AI evaluation & testing

While both offers evaluation, Confident AI stands out in a few ways:

Open-source metrics, battle-tested with minimal setup

Confident AI’s evaluations are powered by our proprietary open-source LLM evaluation framework, one of the most adopted if not already the most adopted in the world, used by hundreds and thousands of developers at organizations such as BCG, Aztrazenca, Stellantis, and Google.

OpenLayer’s on the other hand, are only tested by those who would pay them an annual contract. OpenLayer’s custom metrics take at least 100 lines of code to setup, while this literally all it takes for Confident AI:

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCaseParams

correctness_metric = GEval(
    name="Correctness",
    criteria="Determine whether the actual output is factually correct based on the expected output.",
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT],
)

Apart from adoption and ease of use, Confident AI simply offers more variety of metrics. Single-turn for one-shot prompts, multi-turn ones for conversation, across safety, RAG, agentic workflows, and chatbot categories. OpenLayer does use some frameworks like RAGAs under the hood for RAG evaluation, which DeepEval also recently beat in terms of adoption.

Code driven, flexible workflows, integrates with Pytest:

To make Openlayer part of your pipeline, you must set up a way to push your artifacts to the Openlayer platform after each development cycle. - OpenLayer Docs

In Confident AI, things aren’t as complicated. You can either run tests as a stande-alone script, or as part of your CI/CD pipeline, with first-class integrations via Pytest.

Tests are entirely code driven, which allows for customizations and flexibility. Instead of having to learn about how to setup an openlayer.json file, or be tied up to Git commits to trigger tests in OpenLayer, using Confident AI you simply need to call your LLM app with the metrics you wish to test for, and you’re good to go.

from deepeval.metrics import AnswerRelevancy
from deepeval import evaluate

evaluate(test_cases=dataset.test_cases, metrics=[AnswerRelevancyMetric()])

That means LLM evaluation in any environment, anytime, without leaving your codebase, best for cross-team collaborations.

Built-in testing reports, A|B regression tests:

Confident AI has broader support of features in the testing report, designed for collaboration on both testing workflows and validating AI apps.

For example, one can easily debug metric scores (even for LLM-as-a-judge powered evaluation algorithms), while finding a quick summary of all test cases (which can be in the thousands) via our test run summary. Each testing report is also publicly sharable for external stakeholders, and has a built in comparison functionality to run regression tests between different versions of your AI app.

We counted, and it takes exactly 3 clicks to find all regressions in your AI app:

Oh and we also include built in observability for testing reports, which means LLM traces for debugging failed test cases.

Off-the-shelf models and prompts comparison

Confident AI treats prompts and models as first-class citizens, primitives to optimize for. Each testing report contains a built-in log of which models and prompts were used, and with our insights functionality you can quickly detect which model and prompt combination works best for each use case.

As far as we know, there’s no such thing in OpenLayer.

AI observability

Although both offers observability, Confident AI has far greater support for LLM tracing and online evals, including:

When it comes to evaluation for tracking performance in production over time, Confident AI allows you to bring the same DeepEval metrics that you already use in development to production.

These metrics does not require code — meaning PMs, QAs, and even domain experts that have never seen a line of code in their lives can modify it without requiring engineers to deploy a new version of the AI app.

Dataset management

Datasets are the cornerstone of AI evaluation, and in Confident AI this is managed in one centralized dataset editor. Our dataset editor allows non-technical users to:

• Annotate “goldens”, and mark them as ready or not ready for evaluation as they see fit

• Assign team members to review or annotate goldens

• Manage both single-turn or multi-turn goldens

• Custom columns, that can also be used for evaluation

These datasets integrates with an engineer’s workflow as they could simply pull it from the cloud, with all the type safety features you would expect.

OpenLayer relies on pandas datasets, and does not handle the non-technical workflow in this regard, and instead delegates to Big Query, S3 buckets, etc.

Prompt versioning

Confident AI allows non-technical users to edit and version prompts directly on the platform, while OpenLayer does not support this.

Platform Comparison

API Support

Integrations

Confident AI’s observability integrates with 10+ frameworks and LLM gateways, and is Python, Typescript, and OpenTelemetry native, and 100% open-source. OpenLayer while also integrates with OpenTelemetry, is more focused on just the model layer instead of the entire application.

For example, although both has a LangChain integration, OpenLayer only integrates with LangChain’s chat models, which is a wrapper around model providers. Confident AI is able to trace entire LangChain apps, and not just its model abstractions.

Security and compliance

Price Comparison

Confident AI offers more transparent and granular pricing, while OpenLayer is either trial or contract:

FAQs

What does working with Confident AI look like?

Working with us involves anywhere from 2-5 weeks, where you will get first-class support with one of the maintainers/authors of DeepEval (yours truly), where we will be tailoring metrics and figuring out what works with your use case. After the initial alignment phase, we'll move onto building metrics, and make sure your dataset has enough test coverage, and bringing those metrics to production where appropriate.

Why go for enterprise instead of self-served?

Both are great options. Typically self-served are for users that are OK with the basic feature set, don't need support, and aren't looking for a trusted partner to scale out their AI evaluation pipeline. However, for those exploring ways to build a robust system and best practices for the decade to come, enterprise is the best solution.

Apart from custom metric development, dataset auditing, enterprise customers get priority support, feature requests, including those for emerging use cases, new model releases, as well as framework integrations no matter how niche they are.