Llama 4 Scout 17B
Amazon Bedrock token pricing and cost optimization reference for Llama 4 Scout 17B.Rates normalized to USD per 1 Million Tokens ($/1M) with prompt caching and batch API discounts across 4 AWS regions.
meta.llama-4-scout-17b-v1:0On Google Cloud Vertex AI
The same model is sold through Vertex AI at one global rate per price type. These are Google's published rates, not the AWS rates above; both hosts are shown because the model is sold on both.
| Rate | Vertex AI, USD per 1M tokens |
|---|---|
| Input | $0.25 |
| Output | $0.70 |
| Cache read | — |
| Batch input | $0.13 |
| Batch output | $0.35 |
| Input, long context | — |
| Output, long context | — |
| Output, thinking | — |
Vertex AI rates are global: Google publishes one rate covering every region where the model runs, against the per-region AWS prices above. A dash means Google publishes no rate for that price type.
Alternatives to Llama 4 Scout 17B
| Option | Price vs Llama 4 Scout 17B | Why look at it |
|---|---|---|
| Llama 3.1 8BCompare → | input+29%+$0.05/1M output-67%-$0.44/1M Standard tier, per 1M tokens | Next step down in price from Meta |
| Llama 3 8BCompare → | input+76%+$0.13/1M output-9%-$0.06/1M Standard tier, per 1M tokens | Next step up in price from Meta |
| Llama 4 Maverick 17BCompare → | input+41%+$0.07/1M output+47%+$0.31/1M Standard tier, per 1M tokens | Same Llama line, different version |
| Llama 3.3 70BCompare → | input+324%+$0.55/1M output+9%+$0.06/1M Standard tier, per 1M tokens | Also from Meta (balanced tier) |
Llama 4 Scout 17B Spend Estimator
Model inference spend by prompt volume, cache hit rate, and batch processing.
Key Cost Levers & Architecture
Practical mechanisms to reduce inference costs with Llama 4 Scout 17B on AWSCaches repetitive system instructions, schemas, and document prefixes in memory with 5-minute and extended 1-hour TTL tiers.
- Read Rate: $0.04 / 1M tokens
- Write Rate (5m TTL): $0.21 / 1M tokens
- Ideal for: Multi-turn conversations, RAG document queries, structured schemas
Asynchronous bulk processing submitted via Amazon S3 with a standard 24-hour turnaround SLA.
- Batch Input: $0.09 / 1M tokens
- Batch Output: $0.33 / 1M tokens
- Ideal for: Offline classification, synthetic data generation, benchmark evals
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
Rates by region
Live rates across all 4 available AWS cloud regions| AWS Region | Prompt Input $/1M | Completion Output $/1M | Cache Read $/1M | Batch Input $/1M | Pricing Tier |
|---|---|---|---|---|---|
| us-east-1 US East (N. Virginia) | $0.17 | $0.66 | — | $0.09 | Baseline |
| us-west-2 US West (Oregon) | $0.17 | $0.66 | — | $0.09 | Baseline |
| us-east-2 US East (Ohio) | $0.17 | $0.66 | — | $0.09 | Baseline |
| us-west-1 US West (N. California) | $0.17 | $0.66 | — | $0.09 | Baseline |
Developer Quickstart
Invoke Llama 4 Scout 17B via Amazon Bedrock Converse APIimport boto3
# Amazon Bedrock Converse API invocation
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="meta.llama-4-scout-17b-v1:0",
messages=[
{
"role": "user",
"content": [{"text": "Summarize key features and cost levers of Llama 4 Scout 17B."}]
}
],
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")