Amazon Forecast
Predictive analytics software
Time series intelligence software
Machine learning software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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Pay-as-you-go
Free Trial
Free version unavailable
Small
Medium
Large
- Real estate and property management
- Retail and wholesale
- Accommodation and food services
What is Amazon Forecast
Amazon Forecast is a managed AWS service for building time-series forecasting models using machine learning. It is used by data science and analytics teams to generate demand, inventory, staffing, and other operational forecasts from historical time-series data and related attributes. The service provides automated model training and tuning, supports multiple forecasting algorithms, and integrates with AWS data storage and orchestration services for production deployment.
Managed ML forecasting workflow
Amazon Forecast provides an end-to-end workflow for importing time-series data, training models, and generating forecasts without requiring teams to manage ML infrastructure. It includes automated feature processing and model selection/tuning options that reduce the amount of custom modeling work for common forecasting tasks. This can shorten time-to-deployment compared with building and operating bespoke forecasting pipelines.
AWS-native integration options
The service integrates with AWS identity and access management and commonly used AWS data services for storage and processing. This makes it easier to operationalize forecasts in environments that already standardize on AWS for data pipelines and applications. Teams can embed forecasts into downstream analytics and planning workflows using other AWS services.
Supports related data signals
Amazon Forecast supports incorporating additional predictors beyond the target time series, such as item metadata and external factors, to improve forecast relevance for certain use cases. It also supports generating forecasts at scale across many items or locations when data is structured appropriately. These capabilities align with operational forecasting scenarios where multiple hierarchies and attributes influence demand.
AWS lock-in considerations
Amazon Forecast is designed to run within AWS and uses AWS-specific APIs, security, and operational patterns. Organizations with multi-cloud strategies or on-prem requirements may face additional integration and governance work. Migrating forecasting workflows to another platform can require re-implementing pipelines and retraining models.
Limited transparency and control
As a managed service, it provides less low-level control over modeling choices than custom ML frameworks. Teams that require full control over feature engineering, bespoke model architectures, or specialized evaluation methods may find the abstraction limiting. Model interpretability and debugging can also be more constrained than in fully custom implementations.
Cost and scaling management
Usage-based pricing can become difficult to predict when training and generating forecasts across many series, frequent retraining cycles, or large datasets. Cost optimization may require careful dataset design, retraining schedules, and monitoring. This can be a constraint compared with tools that run on fixed-capacity infrastructure or consolidate multiple analytics workloads under one pricing model.
Plan & Pricing
Pricing model: Pay-as-you-go (usage-based)
Free tier / trial: For the first two months: up to 100,000 forecast data points per month; up to 10 GB of data storage per month; and up to 10 hours of training per month (time-limited free tier as stated on the Amazon Forecast official pricing page).
Primary cost components (official):
- Imported data: $0.088 per GB.
- Training a predictor: $0.24 per hour (note: Amazon Forecast may deploy multiple instances in parallel so billed hours can exceed wall-clock time).
- Generated forecast data points (tiered, price per 1,000 forecast data points per quantile):
- First 100K forecast data points: $2.00 per 1,000.
- Next 900K forecast data points: $0.80 per 1,000.
- Next 49M forecast data points: $0.20 per 1,000.
- Over 50M forecast data points: $0.02 per 1,000.
- Note: forecasts are rounded up to the nearest thousand; legacy CreatePredictor API customers are charged $0.60 per 1,000 time series (per quantile).
- Forecast explanations (tiered, price per 1,000 explanations):
- First 50K explanations: $2.00 per 1,000.
- Next 950K explanations: $0.80 per 1,000.
- Next 9.9M explanations: $0.25 per 1,000.
- Over 10M explanations: $0.15 per 1,000.
- Note: explanations = forecast data points × number of attributes; explanations are rounded up to the nearest thousand; each explainability job limits: 50 time series and 500 time points.
Examples (from official page):
- Example 1 (product demand): imports 5 GB = $0.44; 3 training hours = $0.72; 50K forecast data points billed at $2/1,000 = $100 → Total example cost = $101.16.
- Example 2 (daily forecasts increasing forecast data points) shown on official page with tiered forecast charges — see AWS page for details.
Discounts / other pricing notes (official):
- AWS states: "you pay only for what you use; there are no minimum fees and no upfront commitments."
- AWS Pricing Calculator available for custom estimates; contact AWS for personalized quotes.
Source: Amazon Forecast official pricing page (aws.amazon.com/forecast/pricing).
Seller details
Amazon Web Services, Inc.
Seattle, Washington, USA
2006
Subsidiary
https://aws.amazon.com/
https://x.com/awscloud
https://www.linkedin.com/company/amazon-web-services/
