Input / Prompt
$3.00/ 1M tokens
$0.0030 per 1K tokens
Output / Completion
$15.00/ 1M tokens
$0.0150 per 1K tokens
Prompt Cache Read
$0.30/ 1M tokens
Save 90% vs prompt
Batch Inference
$1.50/ 1M in
Save 50% via Batch API
Price Ratio
1 : 5.0
Prompt to completion factor
Regional Reach
1AWS Regions

Claude 3.5 Sonnet Spend Estimator

Model inference spend by prompt volume, cache hit rate, and batch processing.

Workload Presets:
M tokens
M tokens
30%
Estimated Monthly Spend
$0.00
Fresh Prompts:$0.00
Cached Prompts:$0.00
Completions:$0.00
Active: Input: $3.00/1M · Output: $15.00/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Claude 3.5 Sonnet on AWS
Prompt CachingSave ~90%

Caches repetitive system instructions, schemas, and document prefixes in memory with a 5-minute TTL.

  • Read Rate: $0.30 / 1M tokens
  • Write Rate: $3.75 / 1M tokens
  • Ideal for: Multi-turn conversations, RAG document queries, structured schemas
Batch InferenceSave 50%

Asynchronous bulk processing submitted via Amazon S3 with a standard 24-hour turnaround SLA.

  • Batch Input: $1.50 / 1M tokens
  • Batch Output: $7.50 / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers1 Regions

AWS Bedrock supports standard on-demand inference, priority reserved throughput, and cross-region routing.

  • Standard: Lowest latency, pay-per-token with zero commitments
  • Cross-Region Routing: Automatically burst traffic to available regional capacity
  • GovCloud: Dedicated isolated compliance partitions with +20% uplift

Regional Pricing Matrix

Live rates across all 1 available AWS cloud regions
Baseline: $3.00/1M in1 Global Regions
Showing 1 benchmark regions
AWS RegionPrompt InputCompletion OutputCache ReadBatch InputPricing Tier
$3.00/1M$15.00/1M$0.30/1M$1.50/1MBaseline

Developer Quickstart

Invoke Claude 3.5 Sonnet via Amazon Bedrock Converse API
Python (boto3) · Amazon Bedrock Runtime
import boto3

# Amazon Bedrock Converse API invocation
client = boto3.client("bedrock-runtime", region_name="us-east-1")

response = client.converse(
    modelId="anthropic.claude-3-5-sonnet-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Claude 3.5 Sonnet."}]
        }
    ],
    inferenceConfig={
        "maxTokens": 1024,
        "temperature": 0.7
    }
)

output_text = response["output"]["message"]["content"][0]["text"]
usage = response["usage"]
print(f"Response: {output_text}")
print(f"Usage: {usage['inputTokens']} in, {usage['outputTokens']} out")

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