Foundation Model ยท Amazon BedrockStable FamilyFlagship TierImage
Stable Image Ultra
Amazon Bedrock token pricing and cost optimization reference for Stable Image Ultra.Live pricing reference across 2 AWS regions on Amazon Bedrock.
ProviderStability AI
Maker HQLondon, United Kingdom
Class / TierFlagship
ModalityImage
Prompt CachingSupported
Batch APISave 50%
AWS Regions2 Global
Bedrock Model ID
stability-ai.stable-image-ultra-v1:0Input / 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
Stable Image Ultra Spend Estimator
Model inference spend by prompt volume, cache hit rate, and batch processing.
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 AWSPrompt 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 regionsBaseline: โ/1M in2 Global Regions
Developer Quickstart
Invoke Stable Image Ultra via Amazon Bedrock Converse APIPython (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")Compare Stable Image Ultra with Alternatives
Open Multi-Model Estimator →Stable Image Ultra vsCommand LightCohere
Input Rate:$0.30/1M
Output Rate:$0.60/1M
Stable Image Ultra vsLlama 2 Chat 13BMeta
Input Rate:$0.75/1M
Output Rate:$1.00/1M