Why Claude for Production Apps

When choosing an LLM for production, I evaluated GPT-4, Gemini, and Claude. For the use cases I build - document processing, academic assistance, and structured data extraction - Claude's instruction-following and long-context performance won out.

Basic Integration with Django

import anthropic

client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))

def ask_claude(system_prompt: str, user_message: str) -> str:
    message = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        system=system_prompt,
        messages=[{"role": "user", "content": user_message}]
    )
    return message.content[0].text

Prompt Caching - The Feature That Cuts Costs by 80%

If your system prompt is long (instructions, context, documents), prompt caching is essential. Anthropic caches up to 10 minutes of content.

message = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": LONG_SYSTEM_PROMPT,  # your 2000-token instructions
            "cache_control": {"type": "ephemeral"}  # cache this!
        }
    ],
    messages=[{"role": "user", "content": user_question}]
)

Cache hits cost 90% less than regular input tokens. On one app processing ~500 questions/day with a 1,500-token system prompt, this saved roughly $40/month.

Streaming for Better UX

For chat interfaces, stream the response so users see text as it's generated:

# Django view with streaming response
from django.http import StreamingHttpResponse

def chat_stream(request):
    user_msg = request.GET.get("message", "")

    def generate():
        with client.messages.stream(
            model="claude-sonnet-4-6",
            max_tokens=1024,
            messages=[{"role": "user", "content": user_msg}]
        ) as stream:
            for text in stream.text_stream:
                yield f"data: {json.dumps({'text': text})}

"

    return StreamingHttpResponse(generate(), content_type="text/event-stream")

Real Cost Numbers

For a student Q&A app averaging 200 sessions/day:

Plan your token budgets before launch.