Input / Prompt
โ€”/ 1M tokens
Standard on-demand
Output / Completion
$7.50/ 1M tokens
$0.0075 per 1K tokens
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
32AWS Regions

TwelveLabs Pegasus 1.5 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: $7.50/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with TwelveLabs Pegasus 1.5 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: $3.75 / 1M tokens
  • Ideal for: Offline classification, synthetic data generation, benchmark evals
Cross-Region & Tiers32 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 32 available AWS cloud regions
Baseline: โ€”/1M in32 Global Regions
Showing 5 benchmark regions
AWS RegionPrompt Input $/1MCompletion Output $/1MCache Read $/1MBatch Input $/1MPricing Tier
us-east-1
US East (N. Virginia)
โ€”$7.50โ€”โ€”Baseline
us-west-2
US West (Oregon)
โ€”$7.50โ€”โ€”Baseline
eu-central-1
Europe (Frankfurt)
โ€”$7.50โ€”โ€”Baseline
eu-west-1
Europe (Ireland)
โ€”$7.50โ€”โ€”Baseline
ap-northeast-1
Asia Pacific (Tokyo)
โ€”$7.50โ€”โ€”Baseline
af-south-1
Africa (Cape Town)
โ€”$7.50โ€”โ€”Baseline
ap-south-2
Asia Pacific (Hyderabad)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-3
Asia Pacific (Jakarta)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-5
Asia Pacific (Malaysia)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-4
Asia Pacific (Melbourne)
โ€”$7.50โ€”โ€”Baseline
ap-south-1
Asia Pacific (Mumbai)
โ€”$7.50โ€”โ€”Baseline
ap-northeast-3
Asia Pacific (Osaka)
โ€”$7.50โ€”โ€”Baseline
ap-northeast-2
Asia Pacific (Seoul)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-1
Asia Pacific (Singapore)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-2
Asia Pacific (Sydney)
โ€”$7.50โ€”โ€”Baseline
ap-east-2
Asia Pacific (Taipei)
โ€”$7.50โ€”โ€”Baseline
ap-southeast-7
Asia Pacific (Thailand)
โ€”$7.50โ€”โ€”Baseline
ca-central-1
Canada (Central)
โ€”$7.50โ€”โ€”Baseline
ca-west-1
Canada West (Calgary)
โ€”$7.50โ€”โ€”Baseline
eu-west-2
Europe (London)
โ€”$7.50โ€”โ€”Baseline
eu-south-1
Europe (Milan)
โ€”$7.50โ€”โ€”Baseline
eu-west-3
Europe (Paris)
โ€”$7.50โ€”โ€”Baseline
eu-south-2
Europe (Spain)
โ€”$7.50โ€”โ€”Baseline
eu-north-1
Europe (Stockholm)
โ€”$7.50โ€”โ€”Baseline
eu-central-2
Europe (Zurich)
โ€”$7.50โ€”โ€”Baseline
il-central-1
Israel (Tel Aviv)
โ€”$7.50โ€”โ€”Baseline
mx-central-1
Mexico (Central)
โ€”$7.50โ€”โ€”Baseline
me-south-1
Middle East (Bahrain)
โ€”$7.50โ€”โ€”Baseline
me-central-1
Middle East (UAE)
โ€”$7.50โ€”โ€”Baseline
sa-east-1
South America (Sao Paulo)
โ€”$7.50โ€”โ€”Baseline
us-east-2
US East (Ohio)
โ€”$7.50โ€”โ€”Baseline
us-west-1
US West (N. California)
โ€”$7.50โ€”โ€”Baseline

Developer Quickstart

Invoke TwelveLabs Pegasus 1.5 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="twelvelabs.twelvelabs-pegasus-1-5-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of TwelveLabs Pegasus 1.5."}]
        }
    ],
    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")