10 Best LLM Evaluation Tools, Platforms and Software

Have you ever wanted to know how we can be sure that LLMs are not just spinning text but actually understand our prompts? 

What standards can show where they succeed or fail?

The techniques and metrics used to compare a model’s outputs to standards for quality, accuracy, and safety are referred to as LLM evaluation. 

It covers everything from evaluating how models manage challenging reasoning tasks to verifying the accuracy and bias of facts. 

Since models are now more common and larger in 2025, variations in performance may have a greater effect on products and services.   

Strong testing tools are now used by businesses to identify biases and regressions before they affect users.

So, we have got best LLM evaluation tools in one place. 

1. Qpik

Opik tracks every prompt, answer, and internal span to help you find where your LLM stumbles during development.

You define evaluation tasks, select datasets, and execute automated tests to verify accuracy, bias, hallucinations, and other factors.

Opik tracks live data in production to find data changes, unsafe outputs, or performance drift.

Common applications include CI/CD model comparisons, pre-deployment testing, and ongoing monitoring to maintain dependability of user experiences. 

Key Features

• End-to- End Tracing: Maintains a record of prompts, responses, and spans through the Opik SDK, enabling you to observe each action your application takes.
• Pre-Built and Custom Metrics: Outputs are scored based on relevance, coherence, bias, toxicity, and hallucinations, with the option of using built-in evaluators or your own definitions.
• CI/CD Integration: Integrate LLM unit tests into PyTest to detect regressions during each deployment.
• Annotation Tools: Use the Python SDK or UI to add feedback scores or labels to answers. This will make human review and dataset building go faster.
• Production Monitoring: Receive real-time notifications of anomalies and model drift by streaming live traces to dashboards.
• Open-Source & Self-Hostable: Under a permissive license, access the whole feature set on GitHub and personalize for business needs.  

Pricing

You can start using it for free and premium pricing starts at $39/month.

2. DeepEval

DeepEval is an open-source, user-friendly LLM evaluation framework that applies the same level of rigor as unit testing to Python model outputs.

You can choose the metrics that are most important for your application, such as G-Eval, hallucination recognition, answer quality, and RAG data.

It connects easily with LangChain, LlamaIndex, Hugging Face processes, and CI/CD systems to find regressions before they reach production.

DeepEval is used by teams to test new prompts and models while they are being built and to keep an eye on live broadcasts for drift, unsafe outputs, or data changes.

Key Features

• Unit Tests in the Pytest Style: Develop LLM evaluation tests that mirror the syntax and reporting of software unit tests.
• 30+ Built-In Metrics: Such as G-Eval, DAG, hallucination checks, faithfulness, contextual precision/recall, tool correctness, and more.
• Synthetic Dataset Generation: Automatically generate gold-standard examples that are rooted in documents or context to enhance your test suites.
• CI/CD Integration: Integrate LLM tests into PyTest pipelines to detect regressions in every commit or pull request.
• Metric Support Customization: Establish and implement your own evaluation criteria or expand existing ones to accommodate specific needs.
• Red-Teaming & Safety Scans: Conduct automated reviews for security vulnerabilities, bias, and toxicity prior to deployment.
• Production Drift Monitoring: Use Confident AI’s interface to alert on performance shifts and trace model interactions in real time.

Pricing

You can start using it for free and premium pricing starts at $29.99 per user per month.

3. Deepchecks

Deepchecks is a comprehensive AI evaluation solution that ensures the high quality of your models and algorithms at every stage, from research to production.

Its LLM Evaluation service is mainly about testing applications that use large language models all the time and being able to see what they are doing.

The system monitors critical metrics, including relevance, coherence, context grounding, bias, and hallucinations, to identify performance deficiencies and safety risks.  

Some common uses are pre-deployment testing to find bugs early, CI/CD version comparisons to help make changes, and live tracking to find outputs that are drifting or unsafe in production.

Key Features

• Automated Metric Scoring: Performs metrics such as context grounding, completeness, and relevance without the need for external API queries. 
• Version Comparison: Compares multiple model versions side by side to identify gains or regressions.
• Customizable Properties: Enables the identification and measurement of properties such as fluency, bias, toxicity, and response length. 
• Auto-Annotation: Accelerates manual review workflows by supplying estimated labels for model outputs. 
• Real-Time Monitoring: Identifies performance drift and anomalies in production to prevent user-facing issues. 
• Simple Integration: Three lines of code are all you need to add evaluation to your research or CI/CD setup. 
• Advanced Hallucination Detection: Uses retrieval-based methods and NLI models to identify unsupported claims with high F1 scores.

Pricing

You can start using it for free and premium pricing starts at $1000/month for 3 seats.

4. Prompt Flow

Promptflow is a collection of development tools in Azure Machine Learning that simplifies the development, testing, and deployment of LLM-based applications by using a visual graph and notebook-style scripting.

It allows you to debug and iteratively interactively link prompts, LLM calls, Python code, other tools, into executable “flows”.

The platform includes an Evaluation flow type that is intended to incorporate previous outputs into metric-driven evaluations.

This allows for the measurement of hallucinations, bias, coherence, and relevance at a large scale.

Teams use Promptflow for large-scale prompt A/B testing, fast prototyping, and live endpoint monitoring to instantly find drift or dangerous outputs.

Key Features

• Visual & Code Hybrid Authoring: Provides a drag-and-drop graph builder and embedded Python cells to allow flexible flow design and debugging.
• Built-In Evaluation Flows: It has an evaluation mode that instantly runs your prompts over datasets and gives you performance numbers.
• CI/CD Integration: Integrates prompt unit tests into popular pipelines (e.g., Azure Pipelines) to detect regressions on each commit.
• Real-Time Monitoring and Alerts: Generates alerts for anomalies, drift, or safety violations by streaming live LLM calls and metrics to dashboards.
• RAG & Custom Logic Orchestration: Manages retrieval-augmented generation pipelines, which encompass document chunking, embedding stores, and downstream LLM calls.
• Team Collaboration & Versioning: Allows consistent collaboration by enabling multi-user editing, version history, and sharing through the Azure Machine Learning workstation.

Pricing

You can start using it for free.

5. Promptfoo

Promptfoo is a robust open-source CLI and library that enables the evaluation and redeployment of LLM applications through the use of simple commands or code.

It lets you build prompts and RAG pipelines that work by running tests on custom datasets and assertions and then seeing comparisons of outputs in the form of matrices.

Promptfoo can integrate into any CI/CD system like GitHub Actions, Jenkins, GitLab and automate tests to find quality flaws before they are used.

Teams test it before putting it into production, run automatic vulnerability scans, and keep an eye on model drift and dangerous outputs in real time.

Key Features

• Declarative Test Cases: Clearly define quick assessments in YAML without coding, therefore ensuring easy maintenance and reading of tests.
• Multi-Model Support: In the same workflow, evaluate outputs from Llama, HuggingFace, Google, Azure, Anthropic, OpenAI, and custom providers.
• Caching & Live Reload: Use built-in caching and auto-reloads to expedite iterations, ensuring that tests are only re-run when data or code is updated.
• Side-by-Side Comparison: Generate matrix views to promptly identify regressions and quality variances among model versions, inputs, and prompts.
• CI/CD Integration: Integrate Promptfoo eval commands into pipelines to enforce test gating and monitor quality over time.
• Open-Source and extensible: GitHub offers extension points for custom metrics, modules, and enterprise features, and is licensed under MIT.

Pricing

You can start using it for free. 

6. Ragas

Ragas offers tools that accelerate the evaluation of complex language model applications, simplifying the process of defining, running, and analyzing tests in code or via CLI.

It allows a lot of different measures, such as faithfulness, answer relevance, and context precision, as well as bias, toxicity, and hallucination detection. Both LLM-based judges and standard score methods can be used.

This tool’s test set generation module and token-usage parsers let you automatically create production-aligned test sets that cover edge cases and corner scenarios without having to mark them by hand.

Common use cases include pre-deployment benchmarking, RAG pipeline assessment, and production trace analysis via connections with Langfuse’s evaluation dashboards and Datadog’s observability suite.

Key Features

• Objective Metrics: Use traditional IR scores, as well as built-in metrics for coherence, relevance, and faithfulness, to accurately assess your LLM outputs.
• Test Data Generation: Generate exhaustive test datasets that are based on your documents or prompts to account for a wide range of scenarios during the automated synthesis process.
• Seamless Integrations: With minimal setup, log spans and metrics by plugging into LangChain, LlamaIndex, Hugging Face, Datadog, and Langfuse.
• Establish Feedback Loops: Use user interactions and production traces to continuously improve prompts and models without the need for manual intervention.
• Evaluation Without Reference: Perfect for live drift detection, run model-based scoring straight on production traces without ground-truth labeling.
• CI/CD & Pipeline Testing: Integrate Ragas tests into PyTest or GitHub Actions to detect regressions on each commit and ensure consistently high quality.

Pricing

You can start using it for free. 

7. ChainForge

ChainForge provides a drag-and-drop interface when used with code cells, enabling the creation of data-flow diagrams that invoke prompts, models, and custom logic in a single location.

It automatically records every LLM call and answer, so you don’t have to. This frees you up to focus on making scoring rules instead of tracking calls by hand.

It keeps track of all the details for you, whether you need to do quick “smoke tests” on new prompt ideas or full A/B comparisons across dozens of model versions.

It’s used by teams for both exploring prompt engineering and thorough testing before changes are sent to production.

Key Features

• Visual Flow Builder: Without the need to compose boilerplate code, users can chain together Python code, model queries, and prompts by dragging nodes onto a canvas.
• Systematic Response Scoring: Establish straightforward tests, incorporate custom evaluation code, or establish an LLM-based scorer with your own rubric to evaluate outputs.
• Multi-Model & Prompt Variant Comparison: You can query multiple LLMs or run different prompt combinations at the same time, and then compare their quality data side by side in one table.
• Custom Evaluation Nodes: You can add Python code fragments to make your own measures, hooks, or conditional logic for specific testing needs.
• Multi-Round Dialogue Assessment: Set up chains that cover multiple back-and-forth turns, which are optimal for testing conversational agents over extended interactions.
• Data Export & Reporting: Enable deeper analysis with external tools or integration into dashboards by exporting your evaluation results to Excel or JSON.

Pricing

You can start using it for free. 

8. Evidently AI

Evidently AI handles the whole testing lifecycle, from creating test cases to producing audit-ready reports, to ensure the dependability of your models and data pipelines.

It includes LLM review methods that use both deterministic measures and LLM-as-a-judge templates to rate results on things like bias, toxicity, hallucinations, relevance, and consistency.

In CI/CD pipelines, you can run automatic tests, see side-by-side comparisons of model versions, and set up real-time dashboards with alerts to show any speed drift or safety risks in production.

Common uses include RAG accuracy checks, adversarial testing for PII breaches and jailbreaks, AI agent scenario validations, and conventional ML monitoring for classification or regression systems.

Key Features

• 100+ LLM Judges and Built-In Metrics You can quickly check for factuality, helpfulness, relevance, bias, poison, and more with a library of standard and chain-of-thought models.
• Synthetic and Adversarial Data Generation: Automatically generate test inputs that are context-specific, hostile, or edge-case to analyze model failure modes without the need for manual labeling.
• Continuous Testing and Monitoring: Use live interfaces to monitor AI performance for each update, receiving notifications for data drift, regressions, or safety breaches.
• Custom Evaluation Flows: Put together rules, classifiers, and external LLMs to make test packages that are specific to your risk profile.
• Support for Multiple Use Cases: Test both generative AI systems (like chatbots, RAG pipelines, and multi-agent processes) and prediction models (like classification, ranking, and recommendation) on the same platform.
• Easy CI/CD Integration: Use GitHub Actions, Azure Pipelines, or PyTest to add tests to every pull request and release to make sure they pass quality gates.

Pricing

It offers a free developer plan and a Pro plan at $50/month.

9. Open AI Evals

OpenAI Evals is an open-source framework and registry of benchmarks that allows users to test large language models (LLMs) and LLM systems.

It provides pre-built templates and the capability to create custom evals in Python or YAML.

It gives an organized way to check how well LLM works in terms of accuracy, bias, hallucinations, and reasoning, using both model-graded and deterministic methods.

It includes a repository of ready-made eval templates covering typical benchmarks without any coding: match, fuzzy match, JSON match, and model-graded classifiers.

You can use custom Python eval logic or YAML-driven model-graded evals to have the model grade its own results in complex scenarios.

You can run evals by installing them with pip install evals or setting them up directly in the OpenAI Dashboard, which makes development and testing go more smoothly.

Key Features

• Pre-Built Registry of Benchmarks: With a single command, access dozens of community-contributed evaluations (e.g., HumanEval, GSM8K, MMLU).
• Custom Eval Support: Build Python or YAML-based evaluations to evaluate any use case, such as multi-turn dialogues and agents that use tools.
• Model-Graded Eval Templates: Prompt a model to grade responses in a parsable format, so automating open-ended output scoring.
• Private Data & Private Evaluations: Perform evaluations on your own datasets without showing them to the public.
• Integration of CI/CD and Dashboard: You can embed evaluation runs in Azure Pipelines, GitHub Actions, or observe the results live in the OpenAI Dashboard.
• External logging: Integrate with Weights & Biases to enable traceability and dashboards, or stream evaluation results to Snowflake.

Pricing

You can start using it for free. 

10. MLFlow

MLflow is an open-source platform that is intended to optimize each phase of the machine learning lifecycle, including experiment management, reproducibility, model bundling, and deployment.

Its Tracking component documents each experiment run, capturing hyperparameters, training metrics, and output artifacts to help with the iteration of LLM fine-tuning and prompt-engineering workflows.

Its Models module standardizes how models are packaged into “flavors,” such as a Transformers flavor, that wrap inference processes as Python functions to make them easy to serve.

The Model Registry adds version control, stage changes (like staging to production), and notes so you can check which version of LLM runs on each node.

Some common uses are comparing different LLM designs, running large-scale fine-tuning tests, automating prompt evaluations, and putting chatbots or RAG services into production.

Key Features

• Experiment Tracking & Autologging: Use the mlflow.autolog() function to automatically capture metrics, parameters, and anomalies during LLM training or evaluation sessions.
• Model Registry: Centralize model versioning, registration, and approval procedures to facilitate the transition of LLMs from development to production, ensuring that audit trails are maintained.
• Transformers Flavor Integration: Log using mlflow.transformers.log_model() and mlflow.transformers.save_model().Hugging Face models and pipelines as MLflow artifacts that load without any problems using mlflow.push_models().
• Projects for Reproducibility: Package code, dependencies, and entry points in MLproject files to conduct consistent LLM experiments on local or remote clusters.
• Built-in Model Serving: Integrate with SageMaker, Azure ML, and Docker; deploy any logged LLM as a REST API or batch inference task with a single CLI command.
• CI/CD & Monitoring Hooks: Integrate MLflow evaluations into Azure Pipelines or GitHub Actions and transmit live metrics and drift notifications to dashboards for LLM applications.

Pricing

You can start using it for free. 

Conclusion

As LLMs grow quickly, they need clear evaluate standards to make sure that models give correct, fair, and trustworthy results.

Teams can detect errors prior to deployment and preserve user confidence by evaluating metrics such as factuality, bias, and coherence.

The integration of these tests into CI/CD pipelines and the monitoring of live inference traces helps to maintain quality gates in both development and production.

As LLMs are used in high-stakes situations in healthcare, banking, and the law, they need to be evaluated in a way that makes them clear and safe.

In the end, the cornerstone of the deployment of AI that scales responsibly and confidently is accurate LLM evaluation.