With Amazon Personalize, you pay only for what you use, and there are no minimum fees and no upfront commitments. You are charged based on the amount of data processed and stored, the compute hours used to train your models, and for the throughput of recommendations.

Pricing at a glance

Data ingestion

You are charged per GB of data uploaded to Amazon Personalize. This includes real-time data streamed to Amazon Personalize and batch data uploaded via Amazon S3.

You are charged for the training hours consumed to train a custom model with your data. Note: A training hour represents 1 hour of compute capacity using 4v CPUs and 8 GiB memory. Amazon Personalize automatically chooses the most efficient instance types to train your data, which may be an instance that exceeds the baseline specifications in order to complete your job more quickly. Therefore, the number of training hours billed may be greater than the number of elapsed hours.
Real Time recommendations
You are charged for the personalization requests processed by Amazon Personalize. The service supports real-time recommendations, which is measured in transactions per second (TPS). Developers need to specify the minimum limits of the throughput, with Amazon Personalize guaranteeing low latency response for requests up to the provisioned throughput. If your requested throughput is more than the minimum provisioned TPS, Amazon Personalize will scale up to serve the additional requests and then scale down up to the minimum if the traffic reduces. The actual TPS used is calculated as the average requests/second within a 5-minute window. You pay for maximum of either the minimum provisioned TPS or the actual TPS.

When serving real-time recommendations, you are charged for throughput capacity per hour in units of TPS-hour (rounded up to the nearest hour). This is calculated as the maximum of either the minimum provisioned TPS or the actual TPS multiplied by the total time (in 5 minute increments within each hour) that requests are processed. These are then aggregated for the month’s usage and billed according to the pricing tiers.

TPS-hours = Maximum of (minimum provisioned TPS, actual TPS) x (5/60 minutes)

Batch recommendations

You are charged for number of users processed when using ‘USER_PERSONALIZATION’ and ‘PERSONALIZED_RANKING’ recipes and for items processed when using ‘RELATED_ITEMS’ recipe for a batch inference job.

Free Trial

Get started with AWS Personalize for free today. For the first two months after sign-up, you are offered:

Data Processing & Storage

Up to 20GB per month


Up to 100 training hours per month


Up to 50 TPS-hours of real-time recommendations/month






Pricing details


Data Ingestion

$0.05 per GB


$0.24 per training hour

Recommendations (Inference)

Real time


First 20K TPS-hour per month

$0.20 per TPS-hour for real-time recommendations

Next 180K TPS-hour per month

$0.10 per TPS-hour for real-time recommendations

Over 200K TPS-hour per month

$0.05 per TPS-hour for real-time recommendations

Batch recommendations


First 20 million recommendations per month

$0.067/ 1000 recommendations

Next 180 million recommendations per month

$0.058/1000 recommendations

Over 200 million recommendations per month

$0.050/1000 recommendations

Pricing example

Example 1: Real-time recommendations for a media company

A media company powers content discovery and recommendation through real-time profiling of their user’s preferences and consumption behavior. They upload 200 GB of data in the month, and train a single solution once per day with each training taking 20 mins to complete and consuming 10 training hours/training. Further the customer uses inference capacity of 10 TPS for 720 hours for the month for generating real-time recommendations.

The bill for the month for using Amazon Personalize will be:

Data processing and storage charge = 200 GB x $0.05 per GB = $10

Training charge = 300 training hours x $0.24 per training hour = $72

Inference charge (real-time) = 10x 720 x $0.20/ TPS-hour = $1440

Total cost = $10 + $72 + $1440 = $1552

Example 2: Real time recommendations with variable inference traffic

For simplicity, let us assume same amount of data upload and training as in example 1.

Thus for:
Data processing and storage charge = 200 GB x $0.05 per GB = $10
Training charge = 300 training hours x $0.24 per training hour = $72

But, this time, we’ll vary the volume of requests throughout the day.

Inference usage and charge:
In following table, we walk through a variable traffic scenario and calculate the TPS-hours consumed in a day of usage:

Inference charge calculation
Time Time (hours elapsed) minProvisioned TPS actualTPS max. (minProvisionedTPs, actual TPS) TPS-hours (max. (minProvisionedTPS, actualTPS) x Time (in hours))
12:00 am - 6:00 pm 18 30 20 30 540
6:00 pm - 10:00 pm 4 30 40 40 160
10:00 pm - 11:00 pm 1 30 5 30 30
11:00 - 12:00 am 1 20 0 20 20
Total TPS-hours consumed/day         750
TPS hours/month                                                                                                                                                                              22500
Total recommendation (inference) charge    Usage TPS-hours (in Tier) Unit price ($/TPS-hour) Cost ($)
Tier 1     20000 $0.2 $4,000
Tier 2     2500 $0.1 $250
Example 3: Batch recommendations

A retailer uses batch recommendations to generate item recommendations for its users, for use in personalizing emails for them. Let us assume the model training was done using 10GB of data, the training consumes 50 training-hours and then a batch inference job was used to generate recommendations for 1 million users with 10 item recommendations for each user.

In this case the charges for using Personalize will be

Data processing and storage = 10 GB x $0.05/GB =$0.5

Training charge = 50 training-hours x $0.24/training-hour = $12

Inference charge = 1 million users* x $0.067/1000 recommendations = $67

*recommendations generated for each user when using a ‘Solution’ based on ‘USER-PERSONALIZATION’ recipe type are counted as 1 recommendation regardless of number of results (items) requested per user for batch pricing. Similarly, you only pay for number of users processed for PERSONALIZED-RANKING regardless of number of items reranked per user and for number of items processed when using ‘RELATED_ITEMS’ recipe regardless of number of similar items per item requested.

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