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
2AWS Regions

Stable Image Ultra 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 Stable Image Ultra on AWS
Prompt CachingSave ~0%

Caches repetitive system instructions, schemas, and document prefixes in memory with 5-minute and extended 1-hour TTL tiers.

  • Read Rate: โ€” / 1M tokens
  • Write Rate (5m TTL): 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 & Tiers2 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 2 available AWS cloud regions
Baseline: โ€”/1M in2 Global Regions
Showing 2 benchmark regions
AWS RegionPrice TypeRateUnit
us-east-1
US East (N. Virginia)
output_token$0.1400image
us-west-2
US West (Oregon)
output_token$0.1400image

Developer Quickstart

Invoke Stable Image Ultra 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="stability-ai.stable-image-ultra-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Stable Image Ultra."}]
        }
    ],
    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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