Claude API Integration - AI-Optimized Technical Reference

Configuration - Production Settings

API Authentication

• Store API keys in environment variables - Never hardcode (security requirement)
• Rotate keys periodically - Especially after accidental commits
• Bearer token authentication - No complex OAuth flows
• Test connection: curl -X GET https://docs.anthropic.com/en/api/models-list

Model Selection & Costs

Model	Input Cost ($/MTok)	Output Cost ($/MTok)	Use Case	Performance Impact
Claude Sonnet 4	$3	$15	Production workhorse - Code review, analysis	Best balance cost/capability
Claude Opus 4.1	$15	$75	Complex architecture only	5x more expensive, significantly smarter
Claude Haiku 3.5	$0.80	$4	Simple tasks only	4x cheaper but unreliable for complex logic

Context Window & Pricing Traps

• Standard: 200K tokens
• Beta 1M window: 2x input cost, 1.5x output cost above 200K tokens
• Critical: 500K token request = $100+ cost
• PDF explosion: 50-page PDF = 100K+ tokens without warning

Resource Requirements - Real Costs

Rate Limits (Production Reality)

• Documented: 200 requests/min
• Reality: Token limits hit first in production
• New accounts: Severely restricted until spending threshold met
• Rate limit reset: 60 seconds from first request (not hourly)
• Error identification: Rate limit errors don't specify which limit hit

Time Investment

• Initial setup: 1-2 hours
• Production debugging: 4+ hours for mysterious tool timeouts
• Cost optimization: Ongoing monitoring required
• Migration from OpenAI: 1-2 days with compatibility layer

Hidden Costs

• Extended thinking: 3-5x token consumption (hidden reasoning tokens)
• Large context: Exponential cost scaling above 200K tokens
• Failed cache placement: Full price when cache structure wrong
• Tool timeouts: Wasted input tokens when external APIs fail

Critical Warnings - Production Failures

Extended Thinking Cost Explosion

• Warning: Simple classification can generate 3K reasoning tokens
• Example failure: $800 batch job from unmonitored extended thinking
• Mitigation: Enable selectively, monitor token consumption
• Impact: Can double or triple API costs without visible output

Rate Limiting Behavior

• Failure mode: Inconsistent rate limit enforcement
• Debug difficulty: Generic "rate_limit_error" messages
• Timing issue: 60-second windows from first request create unpredictable failures
• Batch job impact: Random failures in middle of processing

Content Safety False Positives

• Impact: Complete generation stop, wastes input tokens
• Trigger words: "attack", "exploit" vs safer "code review", "vulnerability assessment"
• No partial responses: Unlike other APIs, Claude stops entirely
• Debugging: No explanation of what triggered filter

Tool Use Silent Failures

• Critical bug: External API timeouts >60s cause silent Claude shutdown
• No error message: Request appears to hang indefinitely
• Discovery time: 4+ hours debugging when not recognized
• Mitigation: 20-30 second timeouts in tool functions mandatory

Implementation Reality - What Works

SDK Selection

• Python SDK: Only reliable choice - async support, retry handling
• TypeScript SDK: Acceptable for JavaScript environments
• Ruby/Go SDKs: Afterthoughts - expect debugging SDK issues
• Community libraries: Hit or miss quality

Retry Logic That Works

async def claude_with_realistic_retry(client, **kwargs):
    max_attempts = 3
    for attempt in range(max_attempts):
        try:
            return await client.messages.create(**kwargs)
        except Exception as e:
            if "rate" in str(e).lower():
                wait_time = min(60, 2 ** (attempt + 3))  # Start at 16s
                await asyncio.sleep(wait_time)
            elif "overloaded" in str(e).lower():
                await asyncio.sleep(120)  # API having bad day
            else:
                raise e
    raise Exception("Claude API failed after retries")

Prompt Caching Optimization

• 90% cost reduction when structured correctly
• Cache system prompts, documentation, code style guides
• Don't cache user-specific or changing content
• Placement critical: Everything before breakpoint cached, after processed fresh
• TTL options: 5 minutes (sessions) vs 1 hour (batch processing)

Batch Processing Trade-offs

• 50% cost savings
• 24-hour processing delay
• Ideal for: Content generation, overnight analysis
• Avoid for: Real-time applications, user-facing features

Decision Criteria - When to Use

Choose Claude API When:

• Need superior reasoning and analysis capabilities
• Working with complex documents or code
• Require reliable tool use integration
• Cost acceptable for quality difference
• Not time-critical (rate limits manageable)

Avoid Claude API When:

• Budget-constrained simple tasks (use Haiku or alternatives)
• Real-time applications with strict latency requirements
• High-volume workloads hitting rate limits
• Extended thinking costs exceed budget

Model Selection Decision Tree:

1. Simple classification/completion: Haiku 3.5
2. Code review/analysis/complex tasks: Sonnet 4
3. Critical architecture/complex reasoning: Opus 4.1
4. Cost-sensitive batch work: Batch API + Sonnet 4

Breaking Points & Failure Modes

Context Window Limits

• 200K includes conversation history
• 50-exchange conversation: 100K+ tokens before documents
• Solution: Conversation pruning or summarization required
• 1M window: Available but 2-5x cost increase

API Stability Issues

• Tool failures: Silent timeouts without errors
• Rate limit inconsistency: Same usage patterns different results
• Model availability: Occasional model downtime
• Cache failures: Wrong structure = full price

Cost Escalation Scenarios

• PDF processing: Innocent slide deck = hundreds of dollars
• Extended thinking: Classification job = thousands unexpectedly
• Large context: Codebase analysis = $100+ per request
• Cache misses: Wrong breakpoints = 10x expected costs

Operational Intelligence

Monitoring Requirements

• Token usage tracking: Predict costs before requests
• Error rate monitoring: Identify rate limit patterns
• Response time tracking: Detect API performance issues
• Cost alerts: Prevent budget overruns

Production Hardening

• Timeout configuration: 60s max, prefer 20-30s for tools
• Retry strategy: Exponential backoff with rate limit respect
• Error handling: Graceful degradation for tool failures
• Cache strategy: Stable content only, monitor hit rates

Security Baseline

• Environment variable storage: Never hardcode keys
• Key rotation: Periodic or after incidents
• Enterprise features: SSO, audit logs, compliance for production
• Content filtering: Plan fallback strategies for false positives