Input / Prompt
โ€”/ 1M tokens
Standard on-demand
Output / Completion
โ€”/ 1M tokens
Standard on-demand
Prompt Cache Read
โ€”/ 1M tokens
Optimized context reuse
Batch Inference
โ€”/ 1M in
Save 50% via Batch API
Price Ratio
โ€”
Prompt to completion factor
Regional Reach
7AWS Regions

Titan Image Generator v1 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: โ€”/1M ยท Output: โ€”/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Titan Image Generator v1 on AWS
Prompt CachingSave ~0%

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

  • Read Rate: โ€” / 1M tokens
  • Write Rate: โ€” / 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: โ€” / 1M tokens
  • Batch Output: โ€” / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers7 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 7 available AWS cloud regions
Baseline: โ€”/1M in7 Global Regions
Showing 6 benchmark regions
AWS RegionPrice TypeRateUnit
image_generation$0.0100image
image_generation$0.0140image
image_generation$0.0120image
Canada (Central)ca-central-1
image_generation$0.0107image
Canada (Central)ca-central-1
image_generation$19.2834hour
Canada (Central)ca-central-1
image_generation$13.9269hour
Canada (Central)ca-central-1
image_generation$0.0129image
Canada (Central)ca-central-1
image_generation$0.0086image
Canada (Central)ca-central-1
image_generation$17.3550hour
EU (Ireland)eu-west-1
image_generation$0.0130image
EU (Ireland)eu-west-1
image_generation$0.0090image
EU (Ireland)eu-west-1
image_generation$0.0110image
EU (Ireland)eu-west-1
image_generation$0.0112image
EU (London)eu-west-2
image_generation$0.0101image
EU (London)eu-west-2
image_generation$0.0151image
EU (London)eu-west-2
image_generation$0.0126image
EU (London)eu-west-2
image_generation$22.7115hour
EU (London)eu-west-2
image_generation$20.4404hour
EU (London)eu-west-2
image_generation$16.4028hour
image_generation$0.0180image
image_generation$0.0150image
image_generation$24.2970hour
image_generation$19.4976hour
image_generation$0.0120image
image_generation$26.9967hour
image_generation$0.0200image
image_generation$13.0000hour
image_generation$16.2000hour
image_generation$1.9500Model/month
image_generation$0.0100image
image_generation$0.0220image
image_generation$16.8500hour
image_generation$0.0120image
image_generation$23.4000hour
image_generation$0.0000hour
image_generation$0.0180image
image_generation$0.0080image
image_generation$0.0050Images Processed
image_generation$21.0000hour
image_generation$0.0200image
image_generation$0.0100image
image_generation$0.0050Images Processed
image_generation$0.0220image
image_generation$0.0000hour
image_generation$0.0080image
image_generation$1.9500Model/month
image_generation$13.0000hour
image_generation$0.0180image
image_generation$16.8500hour
image_generation$0.0120image
image_generation$16.2000hour
image_generation$21.0000hour
image_generation$23.4000hour

Developer Quickstart

Invoke Titan Image Generator v1 via Amazon Bedrock Converse API
Python (boto3) ยท Amazon Bedrock Runtime
import boto3

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

response = client.converse(
    modelId="amazon.titan-image-generator-v1-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Titan Image Generator v1."}]
        }
    ],
    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")