Foundation Model ยท Amazon BedrockTextMulti-Tier Pricing
Nova Lite
Amazon Bedrock token pricing and cost optimization reference for Nova Lite.Rates normalized to USD per 1 Million Tokens ($/1M) with prompt caching and batch API discounts across 24 AWS regions.
ProviderAmazon
ModalityText
Prompt CachingSave 50%
Batch APISave 50%
AWS Regions24 Global
Bedrock Model ID
amazon.nova-lite-v1:0Input / Prompt
$0.03/ 1M tokens
$0.0000 per 1K tokens
Output / Completion
$0.12/ 1M tokens
$0.0001 per 1K tokens
Prompt Cache Read
$0.01/ 1M tokens
Save 50% vs prompt
Batch Inference
$0.01/ 1M in
Save 50% via Batch API
Price Ratio
1 : 4.0
Prompt to completion factor
Nova Lite 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: $0.03/1M ยท Output: $0.12/1M
Key Cost Levers & Architecture
Practical mechanisms to reduce inference costs with Nova Lite on AWSPrompt CachingSave ~50%
Caches repetitive system instructions, schemas, and document prefixes in memory with a 5-minute TTL.
- Read Rate: $0.01 / 1M tokens
- Write Rate: $0.04 / 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: $0.01 / 1M tokens
- Batch Output: $0.06 / 1M tokens
- Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers24 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 24 available AWS cloud regionsBaseline: $0.03/1M inGovCloud: $0.10/1M in24 Global Regions
| AWS Region | Prompt Input | Completion Output | Cache Read | Batch Input | Pricing Tier |
|---|---|---|---|---|---|
| $0.06/1M | $0.12/1M | $0.01/1M | โ/1M | +100% vs Baseline | |
| $0.03/1M | $0.24/1M | $0.01/1M | โ/1M | Baseline | |
| $0.08/1M | $0.16/1M | $0.02/1M | โ/1M | +160% vs Baseline | |
| $0.07/1M | $0.28/1M | $0.02/1M | โ/1M | +130% vs Baseline | |
| $0.04/1M | $0.14/1M | $0.02/1M | โ/1M | +20% vs Baseline | |
| $0.07/1M | $0.14/1M | $0.02/1M | โ/1M | GovCloud (+140%) | |
| $0.07/1M | $0.29/1M | $0.02/1M | โ/1M | +140% vs Baseline | |
| $0.03/1M | $0.26/1M | $0.02/1M | โ/1M | +8% vs Baseline | |
| $0.04/1M | $0.28/1M | $0.02/1M | โ/1M | +18% vs Baseline | |
| $0.07/1M | $0.28/1M | $0.02/1M | โ/1M | +137% vs Baseline | |
| $0.08/1M | $0.16/1M | $0.02/1M | โ/1M | +170% vs Baseline | |
| $0.06/1M | $0.13/1M | $0.02/1M | โ/1M | +110% vs Baseline | |
| $0.07/1M | $0.14/1M | โ/1M | โ/1M | +140% vs Baseline | |
| $0.07/1M | $0.29/1M | $0.02/1M | โ/1M | +140% vs Baseline | |
| $0.03/1M | $0.26/1M | $0.02/1M | โ/1M | +7% vs Baseline | |
| $0.08/1M | $0.34/1M | โ/1M | โ/1M | +180% vs Baseline | |
| $0.10/1M | $0.38/1M | $0.02/1M | โ/1M | +220% vs Baseline | |
| $0.09/1M | $0.35/1M | $0.02/1M | โ/1M | +193% vs Baseline | |
| $0.03/1M | $0.13/1M | $0.02/1M | โ/1M | +8% vs Baseline | |
| $0.07/1M | $0.26/1M | $0.02/1M | โ/1M | +120% vs Baseline | |
| $0.04/1M | $0.30/1M | $0.02/1M | โ/1M | +25% vs Baseline | |
| $0.06/1M | $0.24/1M | $0.01/1M | โ/1M | +100% vs Baseline | |
| $0.03/1M | $0.24/1M | $0.01/1M | โ/1M | Baseline | |
| $0.08/1M | $0.31/1M | โ/1M | โ/1M | +157% vs Baseline |
No AWS regions found matching your filter.
Developer Quickstart
Invoke Nova Lite 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="amazon.nova-lite-v1:0",
messages=[
{
"role": "user",
"content": [{"text": "Summarize key features and cost levers of Nova Lite."}]
}
],
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 Nova Lite with Alternatives
Open Multi-Model Estimator →Nova Lite vsQwen3 235B A22B 2507Alibaba (Qwen)
Input Rate:$0.11/1M
Output Rate:$0.44/1M