Claude API in Production: The Complete Developer Guide (2026)

The Claude API is genuinely different from OpenAI's API in ways that matter for production applications. Having shipped products on both, here's the practical guide I wish existed when I started.

The Model Lineup

As of 2026, the main Claude models:

Model	Speed	Cost	Best For
claude-haiku-4-5	Fastest	Lowest	Classification, simple extraction
claude-sonnet-4-6	Balanced	Mid	Most production tasks
claude-opus-4-6	Slowest	Highest	Complex reasoning, agentic tasks

Start with Sonnet. Upgrade to Opus only if quality isn't sufficient. Drop to Haiku for high-volume, low-complexity tasks.

Basic Setup

npm install @anthropic-ai/sdk

import Anthropic from "@anthropic-ai/sdk"

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
})

The Messages API

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "Explain async/await in one paragraph." }
  ],
})

const text = response.content[0].type === "text"
  ? response.content[0].text
  : ""

With a System Prompt

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  system: "You are a code reviewer. Be concise and focus on critical issues only.",
  messages: [
    { role: "user", content: `Review this function:

${code}` }
  ],
})

The system prompt sets persistent behavior. Use it for persona, constraints, and formatting instructions.

Multi-Turn Conversations

const messages: Anthropic.MessageParam[] = []

// First turn
messages.push({ role: "user", content: "What is dependency injection?" })
const r1 = await client.messages.create({ model: "claude-sonnet-4-6", max_tokens: 512, messages })
messages.push({ role: "assistant", content: r1.content[0].type === "text" ? r1.content[0].text : "" })

// Second turn
messages.push({ role: "user", content: "Give me a TypeScript example." })
const r2 = await client.messages.create({ model: "claude-sonnet-4-6", max_tokens: 512, messages })

You manage conversation history by appending messages. The API is stateless -- send the full history each time.

Streaming

For chat interfaces, stream the response so users see tokens as they generate:

// In your API route
export async function POST(req: NextRequest) {
  const { messages } = await req.json()

  const stream = await client.messages.stream({
    model: "claude-sonnet-4-6",
    max_tokens: 1024,
    messages,
  })

  // Return as a ReadableStream
  const readable = new ReadableStream({
    async start(controller) {
      for await (const chunk of stream) {
        if (
          chunk.type === "content_block_delta" &&
          chunk.delta.type === "text_delta"
        ) {
          controller.enqueue(new TextEncoder().encode(chunk.delta.text))
        }
      }
      controller.close()
    },
  })

  return new Response(readable, {
    headers: { "Content-Type": "text/plain; charset=utf-8" },
  })
}

On the frontend:

const response = await fetch("/api/chat", {
  method: "POST",
  body: JSON.stringify({ messages }),
})

const reader = response.body!.getReader()
const decoder = new TextDecoder()
let result = ""

while (true) {
  const { done, value } = await reader.read()
  if (done) break
  result += decoder.decode(value)
  setStreamedText(result)  // Update UI incrementally
}

Tool Use (Function Calling)

Claude's tool use lets you give it access to external data and actions:

const tools: Anthropic.Tool[] = [
  {
    name: "get_weather",
    description: "Get current weather for a city",
    input_schema: {
      type: "object",
      properties: {
        city: { type: "string", description: "City name" },
        unit: { type: "string", enum: ["celsius", "fahrenheit"] },
      },
      required: ["city"],
    },
  },
]

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 1024,
  tools,
  messages: [{ role: "user", content: "What's the weather in Tokyo?" }],
})

// Check if Claude wants to use a tool
if (response.stop_reason === "tool_use") {
  const toolUse = response.content.find(b => b.type === "tool_use")
  if (toolUse && toolUse.type === "tool_use") {
    const weatherData = await fetchWeather(toolUse.input as { city: string })

    // Send the tool result back
    const finalResponse = await client.messages.create({
      model: "claude-sonnet-4-6",
      max_tokens: 1024,
      tools,
      messages: [
        { role: "user", content: "What's the weather in Tokyo?" },
        { role: "assistant", content: response.content },
        {
          role: "user",
          content: [{
            type: "tool_result",
            tool_use_id: toolUse.id,
            content: JSON.stringify(weatherData),
          }],
        },
      ],
    })
  }
}

Structured Output

For extracting structured data, tell Claude to respond in JSON:

const response = await client.messages.create({
  model: "claude-sonnet-4-6",
  max_tokens: 512,
  system: "Always respond with valid JSON. No markdown, no explanation.",
  messages: [{
    role: "user",
    content: `Extract the key information from this job posting:

${jobText}`,
  }],
})

const data = JSON.parse(
  response.content[0].type === "text" ? response.content[0].text : "{}"
)

For guaranteed valid JSON, use tool use with a single tool -- Claude is more reliable at producing valid JSON when it's filling a tool call rather than responding in free text.

Error Handling and Retries

import Anthropic from "@anthropic-ai/sdk"

async function callWithRetry(
  params: Anthropic.MessageCreateParams,
  maxRetries = 3
): Promise<Anthropic.Message> {
  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await client.messages.create(params)
    } catch (err) {
      if (err instanceof Anthropic.RateLimitError) {
        const waitMs = Math.pow(2, attempt) * 1000  // Exponential backoff
        await new Promise(r => setTimeout(r, waitMs))
        continue
      }
      if (err instanceof Anthropic.APIError && err.status >= 500) {
        await new Promise(r => setTimeout(r, 1000))
        continue
      }
      throw err  // Don't retry on 4xx errors
    }
  }
  throw new Error("Max retries exceeded")
}

Token Counting and Cost Estimation

// Count tokens before sending (useful for cost estimation)
const tokenCount = await client.messages.countTokens({
  model: "claude-sonnet-4-6",
  messages: [{ role: "user", content: yourText }],
})

console.log("Input tokens:", tokenCount.input_tokens)

// Pricing as of 2026 (Sonnet)
const inputCost = tokenCount.input_tokens * 0.000003   // $3 per 1M
const estimatedOutputCost = 1024 * 0.000015            // $15 per 1M

Key Differences From OpenAI

Context window: Claude Sonnet has 200k tokens. GPT-4o has 128k. For long documents, Claude wins.

System prompts: Claude separates system from user messages. OpenAI uses role: "system" in the messages array. Both work, different syntax.

Tool use: The patterns are similar but not identical. Claude uses input_schema (JSON Schema), OpenAI uses parameters. Claude returns tool_use blocks, OpenAI returns tool_calls.

Streaming events: Different event names and shapes. Abstract behind a helper function if you want to support both.

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This API setup -- with streaming, tool use, error handling, and token tracking -- is pre-built in the AI SaaS Starter Kit.

AI SaaS Starter Kit ($99) ->

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