Complete LLM evaluation platform

Create evals automatically from your AI issues

Turn every failure in production into a reusable evaluation to improve reliability with every incident

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Book demo

Up to

99%

of errors caught and fixed before reaching production

As little as

10 traces

is enough to start discovering repeating error patterns

As much as

100 times

better product

Observability

Human feedback

Failure discovery

Playground

Evals

A/B testing

Observability

Capture real inputs, outputs, and context from live traffic. Understand what your system is actually doing, not what you expect it to do.

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Full traces

Observe your AI’s behaviour in the most comprehensive way

Usage statistics

Keep track of the token usage and regulate expenses

Stop scoring outputs, improve your AI with aligned evals

Generic evals measure abstract “AI quality,” aligned evals are calibrated to your real use case and actually tell you what needs your attention

What's measured

What's considered good performance?

Success definition

Who defines success?

Data used

Context awareness

What's being considered upon judgment?

Failure detection

What issues are being discovered?

Optimization metric

What teams optimize for?

Adaptation over time

Most teams

Generic evals

Benchmark-style metrics (BLEU, ROUGE, generic QA sets, model scores)

The model provider or a public dataset

Static, generic datasets

No knowledge of your product, tone, edge cases, or business rules

Misses subtle but critical product-level failures

“Better abstract model score”

Static benchmarks that don’t evolve

Latitude's approach

Aligned evals

If people actually benefit from your AI product

You (PM, domain expert, AI owner)

Real production logs + real user feedback

Fully aware of your use case, constraints, and failure modes

Surfaces the exact patterns that hurt your users

Fewer user complaints. Higher reliability. Business KPIs

Continuously updated as new failures appear

Learn more about it

<- check out our AI PM course

Detect issues from first appearances

Evaluate automatically based on your issues

Convert real failure modes into evals that run continuously & catch regressions before they reach users.

Annotations

Annotate responses with real human judgment. Turn intent into a signal the system can learn from.

Analyse errors

Automatically group failures into recurring issues, detect common failure modes and keep an eye on escalating issues.

Observe

Capture real inputs, outputs, and context from live traffic to understand what your system is actually doing

Test your prompts

Automatically test prompt variations against real evals & iterate without switching environments

Start with visibility

Start with visibility. Grow into reliability.

Start the reliability loop with lightweight instrumentation. Go deeper when you’re ready.

View docs

Providers

OpenAI

Anthropic

Azure

Google AI Platform

Amazon Bedrock

Cohere

Together AI

Vertex AI

Gemini

Groq

Mistral AI

Ollama

LiteLLM

Replicate

AWS SageMaker

Hugging Face Transformers

Aleph Aplha

IBM watsonx.ai

import { LatitudeTelemetry } from '@latitude-data/telemetry'
import OpenAI from 'openai'

const telemetry = new LatitudeTelemetry(
  process.env.LATITUDE_API_KEY,
  { instrumentations: { openai: OpenAI } }
)

async function generateSupportReply(input: string) {
  return telemetry.capture(
    {
      projectId: 123, // The ID of your project in Latitude
      path: 'generate-support-reply', // Add a path to identify this prompt in Latitude
    },
    async () => {
      const client = new OpenAI()
      const completion = await client.chat.completions.create({
        model: 'gpt-4o',
        messages: [{ role: 'user', content: input }],
      })
      return completion.choices[0].message.content
    }
  )
}