What I like most about Amazon Forecast is that it can generate accurate time-series predictions using machine learning without requiring deep ML expertise. It’s also straightforward to integrate with other AWS services, and it supports better demand planning and more informed decision-making. Review collected by and hosted on G2.com.

One thing I dislike about Amazon Forecast is that it can get expensive when you’re working with large datasets or running predictions frequently. On top of that, the setup process can feel a bit complex, especially for new users who aren’t already familiar with AWS services. Review collected by and hosted on G2.com.

I like best Amazon Forecast's automated ML for precise demand forecasting without data science skills. At The Knowledge Academy, it predicts PMP/CISSP enrollment and HubSpot lead trends effortlessly, slashing manual analysis time by 40% and boosting planning accuracy for webinars and staffing. Seamless S3 integration seals the deal. Review collected by and hosted on G2.com.

The main drawbacks of Amazon Forecast are its steep learning curve for non-technical users and the higher costs that come with large-scale datasets. At The Knowledge Academy, the initial setup required AWS expertise, and the pricing scaled up quickly beyond smaller forecasting needs such as our PMP course predictions. As a result, even though the accuracy is strong, it can be less suitable for budget-conscious sales teams. Review collected by and hosted on G2.com.

What I like most about Amazon Forecast is how easy it is to generate accurate demand predictions using our internal historical data. The platform automates most of the heavy lifting, which helps me create forecasts for inventory and operational planning without needing to build models from scratch. The integration with AWS services is smooth, and once the datasets are uploaded, the workflow is very efficient. Review collected by and hosted on G2.com.

Some configuration steps can feel technical, especially when preparing datasets or tuning import settings. Also, training models can take a bit of time depending on the dataset size. Apart from that, the tool is stable and delivers consistent results once everything is set up. Review collected by and hosted on G2.com.

What I like best is how automated and production-ready it is. You don’t need deep ML expertise to get high-quality time-series forecasts because the service handles feature engineering, model selection, and tuning internally. Also, since it’s integrated with the AWS ecosystem, feeding data in and pushing outputs out is very smooth. Review collected by and hosted on G2.com.

A few things:

Learning curve — understanding data schemas (target, related, item metadata) takes time

Pricing complexity — costs vary with dataset size, training frequency, and inference usage

Limited transparency into internal model logic (it’s a managed “black box”)

Pre-processing effort — data must be clean and structured properly, or results suffer

Training time — initial training jobs can take hours for large datasets Review collected by and hosted on G2.com.

What I like best about Amazon Forecast is how it makes high-quality time-series forecasting accessible without forcing you to be a data science expert—while still giving depth if you are one. Review collected by and hosted on G2.com.

I dislike that Amazon Forecast trades transparency, control, and iteration speed for automation: it’s hard to understand why forecasts look the way they do, customization is limited for advanced users, experimentation can be slow and costly, and debugging poor results is difficult—making it frustrating when you need explainability, tight cost control, or domain-specific modeling behavior. Review collected by and hosted on G2.com.

What I like best is the high accuracy powered by the same ML technology used at Amazon.com. It handles complex relationships between variables like price, promotions, and shop traffic to deliver much more reliable results than traditional statistical models. Review collected by and hosted on G2.com.

The cost can escalate quickly when dealing with massive datasets or frequent model retraining. While it's cost-effective for small-to-medium projects, large-scale enterprise use requires very careful monitoring of the Training Hour and Inference costs to avoid budget surprises. Review collected by and hosted on G2.com.

amazon forecast is best used to, as mentioned in the name itself, to forcast the future demand, resource need and workforce requirements depending on the past metrics of the business. Review collected by and hosted on G2.com.

Because the prediction is made considering the past metrics, there is always a chance that the foreccst may go wrong. But out expirience is 40 - 50% accurate results. Review collected by and hosted on G2.com.

What I like most about Amazon Forecast is its ability to create accurate demand forecasts using machine learning without needing deep data science expertise. It handles large datasets well and automatically selects the best algorithms, which saves a lot of my time. The integration with other AWS services also makes it easy for me to use within the AWS ecosystem. Review collected by and hosted on G2.com.

The initial setup and configuration can feel a bit complex, especially for first-time users. It took some time to understand the data preparation steps and choose the right metrics. Also, when working with large datasets, keeping an eye on costs is important. Review collected by and hosted on G2.com.

The best thing about Amazon forecast is that it provides accurate and reliable data with fever array of information fed to it. It scales accuracy and delivers probabilistic forecast without ML expertise.

With the use of its strong algorithm in Machine Learning it extracts data from deep sources and presents the best facts and figures. Review collected by and hosted on G2.com.

Things like transparancy, pricing and fewer customization makes it bit disliking. Forecast is made but it's transparancy on why such prediction is made, is still opaque.

Pricing gets escalated for even small data requirement and customization regarding specific functions and logics are lacking. Review collected by and hosted on G2.com.

Amazon Forecast is very accurate in predicting future trends because it uses self-learning models to understand complex data patterns. It is also easy to set up, as the system does most of the work automatically without needing deep technical knowledge. Review collected by and hosted on G2.com.

One drawback of Amazon Forecast is that it is not easy for beginners, especially if they are not familiar with basic AWS concepts.

It can also be expensive and, while it shows predictions, it does not clearly explain the reasons behind the results. Review collected by and hosted on G2.com.