LangSmith vs Helicone vs Braintrust: LLM Observability Compared

LLM applications fail in ways traditional monitoring can't catch. Token costs spiral, prompts drift, and quality degrades silently. Specialized observability tools have emerged to address these challenges. We compared LangSmith, Helicone, and Braintrust to help you choose.

Quick verdict

Our recommendation: Start with Helicone for straightforward cost and performance monitoring. Move to LangSmith if you're building with LangChain or need sophisticated evaluation. Use Braintrust when evaluation and dataset management are primary concerns.

What LLM observability requires

Traditional APM tools miss LLM-specific concerns:

Effective LLM observability platforms address all five concerns.

LangSmith

Overview

LangSmith is LangChain's observability platform. Deep integration with the LangChain ecosystem provides comprehensive tracing, evaluation, and debugging for AI applications.

Architecture

LangSmith uses automatic instrumentation for LangChain applications:

import { Client } from 'langsmith';
import { ChatOpenAI } from '@langchain/openai';

// Configure client
const client = new Client({
  apiKey: process.env.LANGSMITH_API_KEY,
  apiUrl: 'https://api.smith.langchain.com'
});

// Automatic tracing for LangChain
process.env.LANGCHAIN_TRACING_V2 = 'true';
process.env.LANGCHAIN_PROJECT = 'my-project';

const llm = new ChatOpenAI({ model: 'gpt-4o' });
const response = await llm.invoke('Hello'); // Automatically traced

For non-LangChain code, use decorators:

import { traceable } from 'langsmith/traceable';

const processQuery = traceable(
  async (query: string) => {
    const response = await openai.chat.completions.create({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: query }]
    });
    return response.choices[0].message.content;
  },
  { name: 'processQuery' }
);

Strengths

Deep LangChain integration: Automatic tracing of chains, agents, tools, and retrievers with zero configuration.

Evaluation framework: Built-in evaluators for hallucination, relevance, and custom criteria. Dataset management for systematic testing.

Hub and playground: Prompt versioning, sharing, and iterative testing in the browser.

Annotation queues: Structured workflow for human review and feedback collection.

Weaknesses

LangChain-centric: Non-LangChain applications require more manual instrumentation.

Complexity: Feature-rich but steeper learning curve than simpler alternatives.

Pricing opacity: Credits-based system can be confusing to estimate.

Performance overhead: Full tracing adds latency, especially for complex chains.

Evaluation capabilities

LangSmith's evaluation is its strongest differentiator:

from langsmith import evaluate, Client
from langsmith.evaluation import LangChainStringEvaluator

client = Client()

# Create evaluation dataset
dataset = client.create_dataset("qa-pairs")
client.create_examples(
    inputs=[{"question": "What is Python?"}],
    outputs=[{"answer": "A programming language"}],
    dataset_id=dataset.id
)

# Run evaluation
results = evaluate(
    my_chain,
    data=dataset.name,
    evaluators=[
        LangChainStringEvaluator("qa"),
        LangChainStringEvaluator("criteria", config={"criteria": "conciseness"})
    ]
)

Rating: 5/5 for evaluation. Best-in-class for systematic quality assessment.

Pricing

Additional traces: $0.50/1K traces on Plus plan.

Helicone

Overview

Helicone takes a proxy approach - route your LLM API calls through Helicone's gateway to capture observability data without SDK integration.

Architecture

Helicone works as an API proxy:

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: 'https://oai.helicone.ai/v1', // Proxy URL
  defaultHeaders: {
    'Helicone-Auth': `Bearer ${process.env.HELICONE_API_KEY}`
  }
});

// All calls automatically logged
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello' }]
});

Supports OpenAI, Anthropic, and other major providers with a simple URL change.

Strengths

Simple integration: Change base URL and add header. No SDK or code changes required.

Cost tracking: Real-time cost dashboards with breakdown by user, model, and feature.

Caching: Built-in semantic caching reduces costs and latency.

Rate limiting: Configurable limits per user or API key.

Weaknesses

Limited tracing depth: Proxy approach captures requests but not internal application flow.

Basic evaluation: No built-in evaluation framework. You see requests, not quality metrics.

Latency addition: Proxy adds 10-50ms per request (typically negligible).

Provider limitations: Some providers not supported or require different configuration.

Cost tracking features

Helicone excels at cost visibility:

// Add custom properties for segmentation
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: query }]
}, {
  headers: {
    'Helicone-Property-User': userId,
    'Helicone-Property-Feature': 'chat',
    'Helicone-Property-Environment': 'production'
  }
});

Dashboard shows:

• Cost by user, feature, model
• Token usage trends
• Request latency percentiles
• Error rates and types

Rating: 5/5 for cost tracking. Best visibility into LLM spending.

Caching

Built-in caching reduces costs:

const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: query }]
}, {
  headers: {
    'Helicone-Cache-Enabled': 'true',
    'Helicone-Cache-Bucket-Max-Size': '100' // Similar queries grouped
  }
});

Semantic caching matches similar (not identical) requests. Configurable similarity threshold.

Pricing

Very cost-effective for basic monitoring. Additional requests: $0.05/10K on Growth.

Braintrust

Overview

Braintrust focuses on evaluation and experimentation. It's designed for teams who want to measure and improve LLM output quality systematically.

Architecture

Braintrust uses function wrapping for tracing:

import { initLogger, wrapOpenAI } from 'braintrust';

const logger = initLogger({ projectName: 'my-project' });

const openai = wrapOpenAI(new OpenAI());

// Automatically logged with Braintrust
const response = await openai.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello' }]
});

For experiments:

import { Eval } from 'braintrust';

Eval('my-project', {
  data: () => loadTestCases(),
  task: async (input) => {
    const response = await llm.complete(input.query);
    return response;
  },
  scores: [
    (output, expected) => ({
      name: 'accuracy',
      score: output === expected.answer ? 1 : 0
    }),
    // Custom evaluators
  ]
});

Strengths

Evaluation-first: Designed around systematic quality measurement. Experiments are first-class.

Dataset management: Version-controlled datasets with collaboration features.

AI-powered evaluators: Built-in LLM judges for factuality, relevance, and more.

Prompt playground: Iterate on prompts with real-time evaluation against datasets.

Weaknesses

Less real-time monitoring: Optimised for evaluation over production observability.

Smaller ecosystem: Newer than LangSmith with fewer integrations.

Learning curve: Evaluation concepts require investment to use effectively.

Evaluation framework

Braintrust's evaluation is comprehensive:

import { Eval, LLMClassifierFromTemplate } from 'braintrust';

const factualityScorer = LLMClassifierFromTemplate({
  name: 'Factuality',
  promptTemplate: `Is this response factually accurate?
    Question: {{input}}
    Response: {{output}}
    Expected: {{expected}}`,
  choiceScores: { Yes: 1, No: 0 }
});

Eval('qa-system', {
  data: loadDataset,
  task: qaChain,
  scores: [
    factualityScorer,
    (output, expected) => ({
      name: 'length',
      score: output.length < 500 ? 1 : 0.5
    })
  ]
});

Rating: 5/5 for evaluation workflow. Excellent for teams focused on quality improvement.

Pricing

Additional eval runs: $0.01/run on Pro plan.

Head-to-head comparison

Feature matrix

Integration comparison

LangSmith:

• LangChain: Native, zero-config
• OpenAI SDK: Requires traceable decorator
• Anthropic SDK: Requires traceable decorator
• Custom frameworks: Manual instrumentation

Helicone:

• OpenAI: Proxy URL change
• Anthropic: Proxy URL change
• Other providers: Varies
• LangChain: Via proxy or SDK

Braintrust:

• OpenAI: wrapOpenAI helper
• Anthropic: wrapAnthropic helper
• LangChain: Via OpenAI wrapper
• Custom: Function wrapper

Performance overhead

We measured latency impact:

All platforms add measurable but typically acceptable overhead. Async logging reduces impact.

Use case recommendations

LangChain-based applications

Winner: LangSmith

Native integration means zero-effort observability. Automatic tracing of chains, agents, and tools provides deep visibility.

Cost optimisation focus

Winner: Helicone

Best cost dashboards, built-in caching, and rate limiting. Easiest to integrate with any stack.

Evaluation-driven development

Winner: Braintrust or LangSmith

Both excel at systematic evaluation. Choose LangSmith if using LangChain; Braintrust if you want evaluation-first design.

Simple logging and monitoring

Winner: Helicone

Lowest friction integration. Add proxy URL and you're done. Sufficient for basic monitoring needs.

Enterprise compliance

Winner: LangSmith or Braintrust

Both offer enterprise plans with SOC2, SSO, and self-hosting options. Helicone's enterprise offering is less mature.

Multi-provider architecture

Winner: Helicone

Proxy approach handles multiple providers cleanly. Single configuration for all LLM calls.

Integration patterns

Combining platforms

Some teams use multiple platforms:

// Helicone for cost tracking and caching
const openai = new OpenAI({
  baseURL: 'https://oai.helicone.ai/v1',
  defaultHeaders: { 'Helicone-Auth': `Bearer ${HELICONE_KEY}` }
});

// LangSmith for evaluation and debugging
const client = new Client({ apiKey: LANGSMITH_KEY });
const dataset = await client.createDataset('test-cases');

// Run evaluations in CI/CD

This captures operational metrics via Helicone while using LangSmith's evaluation for quality assurance.

Production monitoring setup

Recommended minimal setup:

// 1. Cost tracking (Helicone proxy)
const openai = new OpenAI({
  baseURL: 'https://oai.helicone.ai/v1',
  defaultHeaders: {
    'Helicone-Auth': `Bearer ${HELICONE_KEY}`,
    'Helicone-Property-Environment': process.env.NODE_ENV
  }
});

// 2. Quality evaluation (scheduled)
async function runDailyEvaluation() {
  const results = await evaluate(
    productionChain,
    data: 'production-samples',
    evaluators: [relevanceEvaluator, factualityEvaluator]
  );

  if (results.averageScore < 0.8) {
    alertTeam('Quality regression detected');
  }
}

Our verdict

Helicone is the best starting point for most teams. Proxy integration works with any stack, cost tracking is immediately valuable, and the free tier is generous. Start here.

LangSmith is the right choice for LangChain users and teams who need sophisticated evaluation. The ecosystem integration is unmatched, and the evaluation framework enables serious quality engineering. Higher learning curve but higher ceiling.

Braintrust excels for teams where evaluation is the primary concern. If your workflow centers on measuring and improving output quality, Braintrust's evaluation-first design is compelling. Less suited for pure operational monitoring.

The best observability strategy combines tools: Helicone for operational metrics, LangSmith or Braintrust for evaluation. The platforms are complementary rather than mutually exclusive.

Further reading:

• /blog/langsmith-vs-helicone-vs-langfuse-comparison
• /blog/ai-agent-logging-observability-production
• LangSmith Documentation
• Helicone Documentation
• Braintrust Documentation