Marengo Embed 3.0 Pricing & Token Calculator (TwelveLabs)

Input / Prompt

—/ 1M tokens

Standard on-demand

Output / Completion

—/ 1M tokens

Standard on-demand

Prompt Cache Read

—/ 1M tokens

Optimized context reuse

Batch Inference

—/ 1M in

Save 50% via Batch API

Price Ratio

—

Prompt to completion factor

Regional Reach

3AWS Regions

Compare 3 regions ↓

Marengo Embed 3.0 Spend Estimator

Model inference spend by prompt volume, cache hit rate, and batch processing.

Prompt Tokens (Millions / month)

M tokens

Completion Tokens (Millions / month)

M tokens

Prompt Cache Hit Rate(0% discount on cached context)

30%

Batch API Mode50% discount on non-realtime async jobs via S3

Estimated Monthly Spend

$0.00

Fresh Prompts:$0.00

Cached Prompts:$0.00

Completions:$0.00

Active: Input: —/1M · Output: —/1M

Key Cost Levers & Architecture

Practical mechanisms to reduce inference costs with Marengo Embed 3.0 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: — / 1M tokens
Write Rate (5m TTL): — / 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: — / 1M tokens
Batch Output: — / 1M tokens
Ideal for: Offline classification, synthetic data generation, benchmark evals

Cross-Region & Tiers3 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 3 available AWS cloud regions

Baseline: —/1M in3 Global Regions

AWS Region	Price Type	Rate	Unit
ap-northeast-2 Asia Pacific (Seoul)	input_token	$0.0001	seconds
ap-northeast-2 Asia Pacific (Seoul)	input_token	$0.0001	Text Requests
ap-northeast-2 Asia Pacific (Seoul)	input_token	$0.0007	seconds
ap-northeast-2 Asia Pacific (Seoul)	input_token	$0.0001	Input Images
eu-west-1 Europe (Ireland)	input_token	$0.0001	Text Requests
eu-west-1 Europe (Ireland)	input_token	$0.0001	seconds
eu-west-1 Europe (Ireland)	input_token	$0.0007	seconds
eu-west-1 Europe (Ireland)	input_token	$0.0001	Input Images
us-east-1 US East (N. Virginia)	input_token	$0.0001	Input Images
us-east-1 US East (N. Virginia)	input_token	$0.0001	Text Requests
us-east-1 US East (N. Virginia)	input_token	$0.0007	seconds
us-east-1 US East (N. Virginia)	input_token	$0.0001	seconds

Developer Quickstart

Invoke Marengo Embed 3.0 via Amazon Bedrock Converse API

Python (boto3) · 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.marengo-embed-3-0-v1:0",
    messages=[
        {
            "role": "user",
            "content": [{"text": "Summarize key features and cost levers of Marengo Embed 3.0."}]
        }
    ],
    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")