HAQM Personalize pricing

With HAQM Personalize, you pay only for what you use, and there are no minimum fees and no upfront commitments

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For the first two months of using HAQM Personalize, you are offered the following:

Data processing and storage: Up to 20 GB per month.

Training:

  • Up to 5 million interactions per month for User-Personalization-v2 and up to 5 million interactions per month for Personalized-Ranking-v2.
  • Up to 100 training hours per month for other Custom Recommendation Solutions.

Recommendations:

  • Up to 50,000 real-time recommendation requests per month for User-Personalization-v2 and Personalized-Ranking-v2.
  • Up to 180,000 real-time recommendation requests per month for other Custom Recommendation Solutions.
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  • Enhanced Custom Recommendation Solutions

  • HAQM Personalize v2 recipes (User-Personalization-v2 and Personalized-Ranking-v2) use a Transformer-based architecture, making it easy for you to build a wide array of personalization experiences without requiring machine learning expertise.

    There are three components of costs to use v2 recipes:

    • Data ingestion: You are charged per GB of data uploaded to HAQM Personalize. This includes real-time data streamed to HAQM Personalize and batch data uploaded via HAQM Simple Storage Service (S3).
    • Training: For each model training job, you are charged based on the number of interactions ingested for training. You can ingest interactions via real-time data streaming or S3 batch uploads. If you ingest more interactions than the service quota, you will be charged based on the maximum number of item interactions that are considered by a model during training (3 billions by default).
    • Inference: You are charged based on the number of recommendation requests for both real-time and batch recommendations. For real-time recommendations, HAQM Personalize charges a minimum of 1 recommendation request transaction per second (TPS) for all active campaigns by default. This minimum 1 TPS charge applies even if you make no requests. You can also provision a higher minimum transaction rate if needed. When the rate of recommendation requests exceeds the minimum provisioned TPS, HAQM Personalize auto-scales to serve your requests and returns to the minimum provisioned TPS when your traffic reduces. You are billed for the greater of minimum provisioned TPS (1 TPS by default) and the actual TPS incurred. Pricing examples 1 and 2 illustrate how the real-time inference charge is calculated.

    Pricing table

    The pricing table applies when using the following recipes:

    • User-Personalization-v2
    • Personalized-Ranking-v2
    Pricing
    Data Ingestion $0.05 per GB of data uploaded to HAQM Personalize
    Training $0.002 per 1,000 interactions ingested for training
    Inference $0.15 per 1,000 recommendation requests for both real-time recommendations and batch recommendations

    Pricing examples

    Example 1: Custom real-time recommendations

    A company uses custom real-time recommendations to generate recommendations for a carousel on their homepage. They upload 200 GB of data in the month, and train their solution twice per week with each training considering 10M interactions ingested. For 10 hours per day, the carousel receives traffic of 36,000 visits per hour. During non-peak hours, the carousel receives less than 3,600 visits per hour, or less than the minimum transaction rate of 1 TPS. As a result, Personalize auto-scales down to the minimum 1 TPS and the customer is billed for 3,600 recommendation requests per hour during this period (1 transaction per second * 3,600 seconds per hour).
     
    The bill for the month for using HAQM Personalize will be:

    • Data processing and storage charge = 200 GB * $0.05 per GB = $10.00
    • Solution training charge = 10 million interactions ingested for training * $2.00 per 1M interactions * 8 trainings per month = $160.00
    • Inference consumption and charge (real-time inference):
      • Peak traffic usage: 36,000 recommendation requests * 10 hours per day * 30 days per month = 10,800,000 recommendation requests
      • Off-Peak traffic usage: 3,600 recommendation requests * 14 hours per day * 30 days per month = 1,512,000 recommendation requests
      • 12,312,000 recommendation requests * $0.15 per 1,000 real-time recommendation requests = $1,846.80

    Total cost = $10.00 + $160.00 + $1,846.80 = $2,016.80

     

    Example 2: Custom real-time recommendations with variable inference traffic

    For simplicity, let us assume the company in Example 1 creates another carousel of recommendations that uses the same amount of data ingestion and training. However, this carousel’s traffic varies more throughout the day. In this example, the customer has provisioned a higher minimum TPS.
     
    Inference consumption and charge: In following table, we walk through a variable traffic scenario and calculate the recommendation requests consumed in a day of usage:

    Inference charge calculation
    Time Time (hours elapsed) Minimum Provisioned TPS Minimum recommendation request transactions per hour (min. Provisioned TPS * 3,600 seconds per hour) Actual Recommendation Requests per Hour  Billed Consumption per Hour [max. (minimum, actual)]

    Total Billed Consumption
    (Hourly Consumption * Hours)
    12:00 am - 6:00 pm 18 30 108,000 72,000 108,000 1,944,000
    6:00 pm - 10:00 pm 4 30 108,000 144,000 144,000 576,000
    10:00 pm - 11:00 pm 1 30 108,000 18,000 108,000 108,000
    11:00 pm - 12:00 am 1 20 72,000 0 72,000 72,000
    Total Recommendations Requests per day           2,700,000
    Total Recommendations Requests per month           81,000,000

    Inference charge: 81,000,000 recommendation requests * $0.15 per 1,000 real-time recommendation requests = $12,150.00

     

    Example 3: Custom batch recommendations

    A company uses Custom Recommendations to generate personalized item recommendations for each user in their email marketing campaigns. They ingest 10 GB of data and 5M interactions for training. The company uses a batch inference to generate recommendations for 1 million users. Each recommendation request returns 10 items per user, however the company is only charged for the 1 million requests.
     
    In this case the charges for using Personalize will be:

    • Data processing and storage charge = 10 GB * $0.05 per GB = $0.50
    • Solution training charge = 5M interactions ingested for training * $2.00 per 1M interactions = $10.00
    • Inference charge = 1 million requests * $0.15 per 1,000 real-time recommendation requests = $150.00

    Total cost = $0.50 + $10.00 + $150.00 = $160.50

     

  • Custom Recommendation Solutions

  • HAQM Personalize makes it easy for you to build a wide array of personalization experiences, including specific product recommendations, personalized product re-ranking and customized direct marketing. Recommendations can be served in real-time to quickly respond to changing user intent or in batch.

    The following pricing applies when using the following recipes:

    • user-personalization
    • popularity-count
    • Personalized-Ranking
    • Next-Best-Action
    • Trending-Now
    • Similar-Items
    • SIMS
    • HRNN (legacy)
    • HRNN-Metadata (legacy)
    • HRNN-Coldstart (legacy)
    Data ingestion

    You are charged per GB of data uploaded to HAQM Personalize. This includes real-time data streamed to HAQM Personalize and batch data uploaded via HAQM Simple Storage Service (S3).

    Data ingestion costs: $0.05 per GB

    Training

    When creating a custom solution, you are charged for the training hours used to train a custom solution with your data. HAQM Personalize automatically chooses the best instance types to train your solution. Personalize calculates training hours based on the instance used which means the number of training hours charged may be higher than the time elapsed on the clock during training.

    Training costs: $0.24 per training hour

    Recommendations (inference)

    Real-time recommendations
    For real-time recommendations, you are charged for the number of recommendations requested, regardless of the number of results returned in the response. HAQM Personalize charges a minimum of 1 recommendation request transaction per second (TPS) for all active campaigns by default. HAQM Personalize also allows you to provision a higher minimum transaction rate as needed. When the rate of recommendation requests exceeds the minimum provisioned TPS, HAQM Personalize auto-scales to serve your requests and returns to the minimum provisioned TPS when your traffic reduces. Note that increasing the minimum provisioned TPS will increase the number of recommendation requests that you are billed for.

    Real-time recommendations Price per 1,000 recommendation requests
                                                                      First 72 million requests per month                                                        $0.0556
                                                                      Next 648 million requests per month                                                        $0.0278
                                                                      Over 720 million requests per month                                                         $0.0139

    * HAQM Personalize allows you to configure your campaign to return Item metadata with the recommendation request response. You are charged an additional $0.0167 per 1,000 recommendation requests for all campaigns with Item metadata enabled. Note that this additional charge also applies to the minimum provisioned TPS when Item metadata is enabled.

    Batch recommendations
    For batch recommendations, you are charged for the number of recommendations requested, regardless of the number of results returned.

    Content Generator uses large language models to generate themes for batch recommendations. You are charged an additional $1 per theme output.

     

    Batch recommendations Price per 1,000 recommendations
    First 20 million recommendations per month per eligible Region $0.067
    Next 180 million recommendations per month per eligible Region $0.058
    Over 200 million recommendations per month per eligible Region $0.050

    Pricing examples

    Example 1: Custom real-time recommendations

    A company uses custom real-time recommendations to generate recommendations for a carousel on their homepage. They upload 200 GB of data in the month, and train their solution twice per week with each training consuming 15 training hours. For 10 hours per day, the carousel receives traffic of 36,000 visits per hour. During non-peak hours, the carousel receives less than 3,600 visits per hour, or less than the minimum transaction rate of 1 TPS. As a result, Personalize auto-scales down to the minimum 1 TPS and the customer is billed for 3,600 recommendation requests per hour during this period (1 transaction per second * 3,600 seconds per hour).

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

    • Data processing and storage charge = 200 GB * $0.05 per GB = $10.00
    • Solution training charge = 15 training hours * 8 trainings per month * $0.24 per training hour = $28.80
    • Inference consumption and charge (real-time inference)
            o Peak traffic usage: 36,000 recommendation requests * 10 hours per day * 30 days per month = 10,800,000 recommendation requests
            o Off-Peak traffic usage: 3,600 recommendation requests * 14 hours per day* 30 days per month = 1,512,000 recommendation requests
            o 12,312,000 recommendation requests * $0.0556 per 1,000 real-time recommendation requests = $684.55

    Total cost = $10 + $28.80 + $684.55 = $723.35
     

    Example 2: Custom real-time recommendations with variable inference traffic

    For simplicity, let us assume the company in Example 1 creates another carousel of recommendations that uses the same amount of data ingestion and training. However, this carousel’s traffic varies more throughout the day. In this example, the customer has provisioned a higher minimum TPS.

    Inference consumption and charge: In following table, we walk through a variable traffic scenario and calculate the recommendation requests consumed in a day of usage:

    Inference charge calculation
    Time Time (hours elapsed) minProvisioned TPS Minimum recommendation request transactions per hour (min. Provisioned TPS * 3,600 seconds per hour) Actual Recommendation Requests per Hour  Billed Consumption per Hour [max. (minimum, actual)]

    Total Billed Consumption
    (Hourly Consumption * Hours)
    12:00 am - 6:00 pm 18 30 108,000 72,000 108,000 1,944,000
    6:00 pm - 10:00 pm 4 30 108,000 144,000 144,000 576,000
    10:00 pm - 11:00 pm 1 30 108,000 18,000 108,000 108,000
    11:00 pm - 12:00 am 1 20 72,000 0 72,000 72,000
    Total Recommendations Requests per day           2,700,000
    Total Recommendations Requests per month           81,000,000
    Total recommendation (inference) charge Usage Recommendation Requests (in Tier) Price per 1,000 Real-Time Recommendation Requests Cost ($)
    Tier 1 72,000,000 $0.0556 $4,003
    Tier 2 9,000,000 $0.0278 $250
          $4,253
    Example 3: Custom batch recommendations

    A company uses Custom Recommendations to generate personalized item recommendations for each user in their email marketing campaigns. They ingest 10 GB of data and training consumes 50 training-hours. The company uses a batch inference to generate recommendations for 1 million users. Each recommendation request returns 10 items per user, however the company is only charged for the 1 million requests.

    In this case the charges for using Personalize will be:

    • Data processing and storage charge = 10 GB * $0.05/GB =$0.50
    • Solution training charge = 50 training-hours * $0.24/training-hour = $12
    • Inference charge = 1 million users * $0.067/1,000 recommendations = $67

    Total cost = $0.50 + $12 + $67 = $79.50
     

    Example 4: Custom themed batch recommendations with Content Generator

    A company uses Custom Recommendations to generate personalized item recommendations with themes. They ingest 10 GB of data and training consumes 50 training-hours. The company uses a batch inference to generate themed recommendations for 100 seed items. Each recommendation request returns 25 items per seed item. The company will get 100 themes in total.

    In this case the charges for using Personalize will be:
    Data processing and storage charge = 10 GB * $0.05/GB =$0.50
    Solution training charge = 50 training-hours * $0.24/training-hour = $12
    Inference charge = 100 seed items * $0.067/1,000 recommendations + 100 themes * $1/theme = $100.0067
    Total cost = $0.50 + $12 + $100.0067 = $112.5067


     

  • Use Case Optimized Recommenders

  • HAQM Personalize offers use case optimized recommenders that simplify the creation and maintenance of common recommendation solutions. Select the recommenders you wish to use and HAQM Personalize automatically configures the underlying machine learning (ML) models and fully manages their lifecycle. You can select from nine recommenders that offer personalized recommendations for different touchpoints in your user experience.

    The following pricing applies when using the following recipes:

    • aws-ecomm-popular-items-by-view
    • aws-ecomm-popular-items-by-purchases
    • aws-ecomm-frequently-bought-together
    • aws-ecomm-customers-who-viewed-x-also-viewed
    • aws-ecomm-recommended-for-you
    • aws-vod-most-popular
    • aws-vod-because-you-watched-x
    • aws-vod-more-like-x
    • aws-vod-top-picks
    Data ingestion

    You are charged per GB of data uploaded to HAQM Personalize. This includes real-time data streamed to HAQM Personalize and batch data uploaded via HAQM Simple Storage Service (S3).

    Data ingestion costs: $0.05 per GB

    Recommender Hours

    You are charged an hourly rate for each active recommender based on the number of users* in your datasets processed by HAQM Personalize. Each recommender serves a fixed recommendations per hour at no extra cost based on the number of users in your dataset.

    Users per recommender Price per 100,000 users Free recommendations per hour
    First 100,000 users $0.375 4,000
    Next 900,000 users $0.045 6,000
    Next 9 million users $0.018 9,000
    Over 10 million users $0.005 14,000

    * HAQM Personalize allows you to configure your Recommender to return Item metadata in the API response. You are charged an additional $0.1 per hour for Recommenders that are configured to return Item metadata.

    Additional recommendations

    When the recommendations in an hour exceed the free recommendations for the user tier (see table above), you are charged for the additional recommendations used per hour.

    Additional recommendations Price per 1,000 recommendations
    First 100,000 recommendations per hour per eligible Region $0.0833
    Next 900,000 recommendations per hour per eligible Region $0.0417
    Over 1 million recommendations per hour per eligible Region $0.0208

    * HAQM Personalize allows you to configure your Recommender to return Item metadata in the API response. You are charged an additional $0.0167 per 1,000 additional recommendations for all Recommenders with Item metadata enabled.

    *The number of users (identified with a ‘user_id’) is calculated as the number of unique users in the union of your ‘Users’ and ‘Interactions’ datasets.

    You have the option to specify the minimum throughput for Use Case Optimized Recommenders in recommendations per second (RPS). If the minimum provisioned RPS exceeds the actual recommendations requested per second, the minimum provisioned RPS will count against the free recommendations per hour included in your user tier. If the minimum provisioned RPS causes you to exceed the free recommendations per hour included in your user tier, you will also be charged for additional recommendations. For example, if you set the minimum RPS to 10, you will be charged against the 36,000 recommendations for that hour (3,600 second per hour x 10 RPS) barring the free recommendations per hour in your user tier. 

    Pricing examples

    Example 1: Use case optimized recommenders for a media company

    A media company powers three different carousels of recommendations on their app using three use case optimized recommenders. They ingest 200 GB of data in the month and have 2,000,000 users. The carousels each typically see fewer than 9,000 visits per hour; however, there are 140 peak hours per month where they see 39,000 visits per hour.

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

    • Data processing and storage charge = 200 GB * $0.05 per GB = $10
    • Recommender Hour charge:
      • First 100,000 users = $0.375 per hour * 720 hours per month * 3 recommenders = $810.00
      • Next 900,000 users = 900,000 users * $0.045 per hour/100,000 users * 720 hours per month * 3 recommenders = $874.80
      • Next 1,000,000 users = 1,000,000 users * $0.018 per hour/100,000 users * 720 hours per month * 3 recommenders = $388.80
      • Total recommender hour charges = $810.00 + $874.80 + $388.80 = $2,073.60
    • Additional recommendations charge:
      • 39,000 recommendations per peak hour – 9,000 free recommendations per hour = 30,000 additional recommendations per hour.
      • 30,000 additional recommendations per peak hour * $0.0833/1,000 recommendations * 140 peak hours * 3 recommenders = $1,049.58

    Total cost = $10 + $2,073.60 + $1,049.58 = $3,133.18

    Example 2: Use case optimized recommenders for an online retailer

    An online retailer uses four use case optimized recommenders to serve product recommendations on their product detail page. They upload 10 GB of data in the month and have 800,000 users. The traffic to these recommenders never exceeds 6,000 visits per hour.

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

    • Data processing and storage charge = 10 GB * $0.05/GB =$0.50
    • User charges:
      • First 100,000 users = $0.375 per hour * 720 hours per month * 4 recommenders = $1,080.00
      • Next 700,000 users = 700,000 users * $0.045 per hour/100,000 users * 720 hours per month * 4 recommenders = $907.20
      • Total recommender hour charges = $1080.00 + $907.20 = $1,987.20
    • Additional recommendation charge:
      • Because the company never exceeds the 6,000 recommendations per hour included with their recommenders there are no additional recommendation charges.

    Total cost = $0.50 + $1,987.20 = $1,987.70

     

  • User Segmentation

  • HAQM Personalize uses machine learning to automatically segment your users based on their affinity for different products, categories, brands, and more to create more effective marketing campaigns.

    The following pricing applies when using the following user segmentation recipes:

    • aws-item-affinity
    • aws-item-attribute
    Data ingestion

    You are charged per GB of data uploaded to HAQM Personalize. This includes real-time data streamed to HAQM Personalize and batch data uploaded via HAQM Simple Storage Service (S3).

    Data ingestion costs: $0.05 per GB

    Training

    You are charged for the training hours used to train a custom solution with your data. HAQM Personalize automatically chooses the best instance types to train your solution. Personalize calculates training hours based on the instance used which means the number of training hours charged may be higher than the time elapsed on the clock during training.

    Training costs: $0.24 per training hour

    Batch segments (inference)

    You are charged for the number of segments requested based on the number of users* in the dataset processed by HAQM Personalize.

    Users in dataset Price per 1,000 users per segment
    First 100,000 users $0.016
    Next 900,000 users $0.008
    Next 9 million users $0.004
    Next 40 million users $0.001

    *The number of users (identified with a ‘user_id’) is calculated as the unique users in the union of your ‘Users’ and ‘Interactions’ datasets.

    Pricing examples

    Example 1: Batch segmentation at an online retailer

    A retailer uses batch segmentation to generate lists of users for SMS and in-app messaging campaigns about particular products that are on sale. They run campaigns on 10 products and consider 2,000,000 users for each campaign. They ingest 10GB of data and training requires 50 training-hours.

    The bill for using HAQM Personalize for these campaigns will be:

    • Data processing and storage charge = 10 GB * $0.05/GB = $0.50
    • Solution training charge = 50 training-hours * $0.24/training-hour = $12.00
    • Batch segment generation charge, first 100,000 users = 100,000 users * $0.016/1,000 users * 10 queries = $16.00
    • Batch segment generation charge, next 900,000 users = 900,000 users * $0.008/1,000 users * 10 queries = $72.00
    • Batch segment generation charge, next 1,000,000 users = 1,000,000 users * $0.004/1,000 users * 10 queries = $40.00

    Total cost = $0.50 + $12 + $16 + $72 + $40 = $140.50
     

    Example 2: Batch segmentation at a media company

    A media company uses batch segmentation to identify users that would be interested in streaming movies based on attributes of the movies, such as genre, lead actor/actress, and awards won. The company uses the segments of users generated to target their email marketing campaigns. The company has 20 million users that are considered for each campaign. The company uses 650 GB of data, and training requires 1,800 training-hours. They run segmentation on 25 different movie attributes for their campaigns.

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

    • Data processing and storage = 650 GB * $0.05/GB =$32.50
    • Solution training charge = 1,800 training-hours * $0.24/training-hour = $432.00
    • Inference charge, first 100,000 users = 100,000 users * $0.016/1,000 users * 25 queries = $40
    • Batch segment generation charge, next 900,000 users = 900,000 users * $0.008/1,000 users * 25 queries = $180
    • Batch segment generation charge, next 9 million users = 9,000,000 users * $0.004/1,000 users * 25 queries = $900
    • Batch segment generation charge, next 10 million users = 10,000,000 users * $0.001/1,000 users * 25 queries = $250

    Total cost = $32.50 + $432 + $40 + $180 + $900 + $250 = $1,834.50