Backed by
Y Combinator

The LLM Evaluation & Observability Platform for DeepEval

Built by the creators of DeepEval, engineering teams use Confident AI to benchmark, safeguard, and improve LLM applications, with best-in-class metrics and tracing.

USE CASES

Build your AI moat.
Do evals the right way.

Confident AI provides an opinionated solution to curate dataset, align metrics, and automate LLM testing with tracing. Teams use it to safeguard AI systems to save hundreds of hours a week on fixing breaking changes, cut inference cost by 80%, and convince stakeholders that their AI is always better than the week before.

END-TO-END EVALUATION

Build in a weekend, validate in minutes.

Measure which prompts and models give the best end-to-end performance using Confident AI's evaluation suite.

REGRESSION TESTING

Make forward progress. Always.

Mitigate LLM regressions by running unit tests in CI/CD pipelines. Go ahead and deploy on Fridays.

COMPONENT-LEVEL EVALUATION

Dissect, debug, and iterate with tracing.

Evaluate and apply tailored metrics to individual components, to pinpoint weaknesses in your LLM pipeline.

DEEPEVAL AND PLATFORM

Built for developers.
Used by everyone to drive product decisions.

Easily integrate evals using DeepEval, with intuitive product analytic dashboards for non-technical team members.

observability.py
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from deepeval.tracing import observe
 
@observe()  
def llm_app(input: str):
res = openai.ChatCompletion.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": input}],
      ).choices[0].message["content"]
   return res
 
llm_app("Write me a poem.")
evaluate.py
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from deepeval.tracing import observe, update_current_span
from deepeval.dataset import Golden, LLMTestCase
from deepeval.metrics import AnswerRelevancyMetric
from deepeval import evaluate
 
@observe(metrics=[AnswerRelevancyMetric()])  # Define metrics
def llm_app(input: str):
res = openai.ChatCompletion.create(model="gpt-4o", messages=[{"role": "user", "content": input}])
      .choices[0].message["content"]
 
   # Set test case at runtime
update_current_span(test_case=LLMTestCase(input=input, actual_output=res))
   return res
 
# Call your LLM app to evaluate
evaluate(goldens=[Golden(input="Write me a poem."), observable_callback=llm_app)
dataset.py
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from deepeval.dataset import EvaluationDataset, Golden
 
# Pull dataset for evaluation
dataset = EvaluationDataset()
dataset.pull(alias="My Testset")
print(dataset.goldens)
 
# Or, push goldens to update dataset
new_goldens = [Golden(input="Write me a poem.")]
dataset = EvaluationDataset(goldens=new_goldens)
dataset.push(alias="My Testset", overwrite=False)
prompt.py
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from deepeval.prompt import Prompt
 
# Pull prompt from cloud
prompt = Prompt(alias="System Prompt")
prompt.pull(version="00.00.18")
messages_to_llm = prompt.interpolate(variable="value")
 
#  Pass interpolated prompt to LLM
res = openai.ChatCompletion.create(model="gpt-4o", messages=messages_to_llm).choices[0].message["content"]
print(res)
HOW IT WORKS

Four steps to setup.
No credit card required.

1
Install DeepEval.

Whatever framework you're using, just install DeepEval.

2
Choose metrics.

30+ LLM-as-a-judge metrics based on your use case.

3
Plug it in.

Decorate your LLM app to apply your metrics in code.

4
Run an evaluation.

Generate test reports to catch regressions and debug with traces.

FEATURES

Your /dev, /staging, /prod features. All-in-one.

ENTERPRISE

Secure, reliable, and compliant.
Your data, is yours.

HIPAA, SOCII compliant

Our compliance standards meets the requirements of even the most regulated healthcare, insurance, and financial industries.

Multi-data residency

Store and process data in the United States of America (North Carolina) or the European Union (Frankfurt).

RBAC and data masking

Our flexible infrastructure allows data separation between projects, custom permissions control, and masking for LLM traces.

99.9% uptime SLA

We offer enterprise-level guarantees for our services to ensure mission critical workflows are always accessible.

On-Prem Hosting

Optionally deploy Confident AI in your cloud premises, may it be AWS, Azure, or GCP, with tailored hands-on support.

OPEN-SOURCE COMMUNITY

100,000+ devs already do evals the Confident way.

Start using the data retrieval platform of the future.

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