5 LLM Evaluation Tools You Should Know in 2025

Large Language Models (LLMs) are quickly becoming a core piece of almost all software applications, from code generation, to customer support automation and agentic tasks. But with outputs that can be unpredictable, how do you prevent your LLM from making costly mistakes?

Looking ahead to 2025, as enterprises deploy LLMs to high-stakes workflows and applications, robust evaluation and testing of models is crucial. This guide covers how to evaluate LLMs effectively, spotlighting leading LLM evaluation software and comparing each LLM evaluation platform based on features and enterprise readiness.

1. Humanloop

Humanloop is an LLM evaluations platform for enterprises. Humanloop’s end-to-end approach ensures teams can perform rigorous LLM testing without compromising on security or compliance. Humanloop enables teams to run LLM Evaluations in their user-interface or in code, by leveraging pre-set or fully customizable evaluators, which can be AI, code or human based. For example, enterprises like Gusto and Filevine use Humanloop to evaluate the accuracy of their agents or to assess AI apps for objective metrics like cost and latency as well as more subjective metrics like tone of voice.

Humanloop is designed to be collaborative, flexible and scalable — making it a leading choice for enterprises who aim to foster and bring technical and non-technical teams together to build AI products and agents that will work reliably at scale.

Additionally, Humanloop offers best-in-class Prompt Management features—essential for iterating on prompts outside of the codebase—and robust LLM Observability to continuously track user interactions, model behavior and system health. For enterprises, Humanloop also offers enterprise-grade security, including role-based access controls (RBAC), SOC 2 Type II compliance, and self-hosting deployment options.

2. OpenAI Evals

OpenAI Evals is an LLM evaluation framework that enables developers to design and execute custom tests for Large Language Models, placing flexibility at the forefront. Its open-source nature fosters a community-driven approach, with contributors actively sharing new evaluations and insights on how to evaluate LLMs across varied domains.

In practice, OpenAI Evals functions as an LLM evaluation platform by integrating user-defined prompts, scoring logic, and post-processing scripts. It supports the collaborative development of standardized LLM evaluation metrics, allowing teams to replicate or adapt tests as their models evolve. This modular design helps enterprises incorporate security checks, policy compliance, and advanced analytics directly into their model development pipeline.

For teams that thrive on code-centric workflows, OpenAI Evals can be a powerful LLM evaluation software option. While it may require more engineering effort to maintain and extend, the upside is full customization.

3. Deepchecks

Deepchecks offers a multi-layered LLM evaluation platform that aims to simplify LLM testing with automated checks and actionable insights. By applying a library of pre-built tests for text coherence, factual accuracy, and even distribution shifts, Deepchecks helps teams quickly identify potential performance bottlenecks. On top of these automated tests, the platform also places emphasis on bias detection—a vital element when you’re examining how to evaluate LLMs for fairness and representativeness across diverse user segments.

One of Deepchecks’ strengths is its seamless integration with existing machine learning stacks, making it relatively easy to embed into CI/CD workflows or data pipelines. Enterprises can set up triggers that run LLM evaluation metrics on each new model version or dataset update, helping maintain consistent performance as the model evolves. Additionally, Deepchecks’ interface is designed to clarify complex results, allowing engineering and product teams to interpret performance insights without wading through excessive complexity. While not as specialized in advanced prompt management features, Deepchecks excels as a focused LLM evaluation framework.

4. ML Flow

ML Flow provides a unified platform that caters to both traditional machine learning and generative AI workflows. Its core functionality revolves around experiment tracking—allowing teams to log hyperparameters, code versions, and LLM evaluation metrics for each run. This streamlined tracking system translates naturally to LLM testing, ensuring that model changes, data updates, and prompt refinements are all documented under a single roof.

Beyond experiment logging, ML Flow’s model registry makes it easier to track model provenance and manage multiple versions—an essential feature when you’re determining how to evaluate LLMs for ever-evolving performance benchmarks. As an LLM evaluation software, it also integrates well with existing CI/CD pipelines, letting data scientists push updates, run tests, and revert changes through a reproducible, automated process. Though ML Flow doesn’t specialize exclusively in Large Language Models, it remains flexible enough to incorporate any custom LLM evaluation framework your team adopts.

5. DeepEval

DeepEval distinguishes itself by offering a rich suite of LLM evaluation metrics—more than a dozen—covering everything from relevance and faithfulness to bias detection and toxicity. Each metric is designed to be self-explanatory, giving direct feedback about how and why a particular score can’t be higher. This approach can be a game-changer in LLM testing, since teams can quickly pinpoint exactly where a model’s outputs are falling short.

A key strength lies in its modular architecture, making it straightforward to pair metrics or create new ones tailored to your application. DeepEval even treats evaluations like unit tests, providing an integration for Pytest so developers can embed LLM evaluation framework checks into familiar workflows. Additionally, the platform enables synthetic dataset creation for testing edge cases, while also letting teams load data from CSVs, JSON files, or Hugging Face repositories. For enterprises that need real-time evaluations, DeepEval’s hosted platform comes with a free tier, making it an accessible LLM evaluation software option.

Conclusion

Enterprises investing in Large Language Models should recognize that LLM evaluation metrics are no longer optional—they’re essential for reliable performance and robust compliance. Whether you opt for a specialized LLM evaluation software like Humanloop or a community-driven LLM evaluation framework like OpenAI Evals, comprehensive LLM testing helps you detect bias, maintain accuracy, and iterate quickly. Tools such as Deepchecks, ML Flow, and DeepEval each offer unique value in how to evaluate LLMs, from real-time analytics to deep experimentation tracking.

By combining these platforms with clear evaluation goals, enterprises can ensure their models remain trustworthy and adaptable—especially as LLMs expand into more business-critical functions. Ultimately, embracing an LLM evaluation platform built for 2025’s challenges will keep your AI initiatives ahead of the curve.

Learn more about LLM evaluations

Evaluations are a critical area in the development cycle of AI applications. At Humanloop, we’ve been working on providing best-in-class tooling for developing and evaluating applications with large language models since 2020. If you’re interested in learning how our LLM evaluation suite can accelerate your AI product development in a reliable and scalable manner, book a demo with our team.