Top 4 Most Popular Feature Store Tools for Machine Learning in 2024

Check out the top 4 most popular feature store tools for machine learning in 2024. Enhance your ML projects with the best feature stores available.
Alon Lev
Alon Lev
Co-Founder & CEO at Qwak
August 28, 2023
Table of contents
Top 4 Most Popular Feature Store Tools for Machine Learning in 2024

What are Features for Machine Learning? To build predictive models using Machine Learning, we require both independent and dependent variables. A feature in machine learning is defined as the variables used for building a machine learning model. For instance, if your data is in a tabular format, you can consider rows as instances whereas the columns are the features or attributes.

Consider the scenario where we want to predict the weather for any given day. To achieve that, we can build a Linear Regression Model that considers past data on wind conditions, humidity, season, etc. We train this model on the available data and pass the unseen data to get the predictions. 

In the above scenario, what are the features in machine learning that we used? 

Here, the weather is a dependent attribute (or feature) that we are trying to predict using other independent attributes such as wind conditions, humidity, season, etc.

But where does a feature store come into the picture and what are the best feature stores to get started with?

Before Diving into Which Feature Stores are the Best

As the name suggests, a Feature Store stores the commonly used features so that they can be reused in the future. But why would you want to reuse these features again and again?

Well, if you’re a part of an organization that gets data regularly, for example, we get at least one instance of weather data every single day. In such a case, you would want to retrain your model to make it better. Hence, a feature store comes in handy as the feature would already be precomputed and available for inference.

Thus, a feature store not only centralizes the storage but also helps with operationalizing the data pipeline. The data pipeline helps convert our raw data into processed data with the same features so that it can be used for training in the future. 

Great, now we know what are the features in machine learning and what is a feature store but does it hold the same significance for everyone? 

Let’s talk about the importance of a feature store first.

Why is a Feature Store Important?

Feature Store not only makes your features available for reuse but also can be used to transform unfiltered data into a processed one. A Feature Store holds many benefits some of which are listed below:

1. It saves time and money!

The primary and perhaps, the most simple advantage it offers is saving time. The saying, “Time is money” literally holds true here. As an organization, you would not want to pay your data scientist to do the same tedious job over and over again. 

Sure, having a feature store would incur an additional cost but in the long run, this would also save time that data scientists would anyway have spent working on computing the same features again - which directly translates to paying them more money. 

Even if money is not a concern, Feature Store saves time by removing the redundancy of repeatedly performing the same tasks as feature engineering can be time-intensive. Moreover, the idea of accessing the features on the get-go is generally convenient.

2. Everything in one place

Inconsistent data is a programmer’s nightmare. When dealing with a big chunk of data, it can be difficult to keep track of the modifications. For instance, if you have been assigned to a project that already uses a huge dataset, then it is important to know how the features in use were calculated and what information they represent.

Without proper documentation and feature definitions, the task can be challenging. A Feature Store can solve this problem as it is a single centralized registry containing all the Machine Learning features. Moreover, it is easily accessible to all the teams within an organization via the cloud. Thus, a Feature Store promotes consistency and ease of integration. 

3. Makes collaboration easier

Since we already discussed that a Feature Store ensures consistency, a fortunate consequence resulting from this is easy communication across teams. A centralized platform encourages data scientists from multiple teams to collaborate and easily share ideas. 

Furthermore, it’s easy to track the development of features in one place irrespective of your location. 

4. Enhances the debugging game

Having a Feature Store also means having detailed information about your machine learning models such as what features are used, when they were created or modified, etc. Hence, one can instantly extract information about a machine learning model after, let’s say, its deployment. This could escalate the debugging process because now you have a log of where things might have gone wrong. 

We are now familiar with the benefits a Feature Store offers but the question - “Is it for everyone?” still remains unanswered. Alternatively, we can find out when a feature store should be used to draw a more general picture.

When should you use a Feature Store?

While it’s natural to be curious about new tech, it’s also essential to know when it can be useful (if at all). These technologies come to the marketplace to make your life easier. However, it’s important to know if it’s the right choice for you in the first place. Otherwise, these services can be cumbersome leading you to be even less productive.

Possible Challenges in Using a Feature Store Solution

A feature store may not be the optimal solution in every scenario. Using a Feature Store does involve some overhead that can’t be paid off if you have different sets of data. Thus, if the data is disparate, then the feature store solution can, in fact, make it more complex. For instance, if you have very limited use cases of your data, then it would not make much sense to store these features for reuse. 

Another challenge is the integration of a Feature Store platform into your existing framework. While a lot of Feature Stores for machine learning provide easy integration and support a lot of frameworks, it can still be time-consuming. This time and effort would not pay off unless you make good use of the platform.  

Despite its challenges, Feature Stores are becoming widely popular among data-driven companies, and for the right reasons! While a small company with datasets having a limited number of accompanying use cases may not reap benefits from a Feature Store, a comparatively larger organization definitely can. If your organization uses the same examples frequently for different use cases, then it can be a good idea to consider a Feature Store.

Nonetheless, each organization should carefully assess the trade-off of advantages vs. disadvantages before moving to a Feature Store solution. 

Let’s have a look at some of the Feature Store tools available in the market.

What are the Best Feature Store Tools to Try?

1. Google Feature Store

Google’s Feature Store - The Vertex AI Feature Store is a fully managed solution where you can create and manage feature stores, entity types, and features. A feature store is defined as the top-level container for storing the features and their values. It allows the permitted users to add and share their features without any additional support.

Note that the Vertex AI Feature Store is a sub-part of Vertex AI which offers other workflows as well. The main benefits of the Google Feature Store include:


Google Feature Store provides you with a low-latency serving platform so that you do not need to build one. This allows organizations to make online predictions without any hassle. Moreover, it lets you scale quickly because it manages the task of serving features independently while you focus on developing and computing your features. 

Handles training-service skew

The term “training-service skew” refers to a situation when the data used during production differs from the data used for training the model. This could create inconsistencies, and hence, is a critical issue to take care of. Google Feature Store addresses the problem by:

  • Ensuring that the feature value is ingested into the feature store and the same value is used for both training and production.
  • Allows you to fetch historical data for training using point-in-time lookups. 

Detects drift

Tangible changes occurring over time to your data distribution are called drift. Google Feature Store recognizes drift by constantly tracking the feature values’ distribution. If the drift becomes significant, you might need to retrain your model using those particular features. 

For more information, see Vertex AI’s Data Model. Lastly, regarding Vertex AI’s pricing, it depends on various factors such as the amount of data stored, the number of feature stores in use, etc. 

Pros Cons
Low-latency: Provides a low-latency serving platform for online predictions. Complexity for Beginners: Might be complex for users new to Google Cloud services.
Handles Training-Service Skew: Ensures consistent feature values for training and production. Cost: Can be expensive depending on the scale of usage and data storage.
Drift Detection: Tracks feature value distribution to detect and address drift. Integration: Requires integration with other Google Cloud services, which could be complex.

2. Qwak

Qwak is a fully managed platform which simplifies the productionization of machine learning models at scale. Qwak’s Feature Store and ML Platform empower the ML engineering teams to continuously build, train and deploy ML models to production. Talking about Qwak’s Feature Store particularly, the platform is designed to handle the entire feature lifecycle from development to deployment. Additionally, Qwak’s Feature Store allows you to easily integrate with any data source regardless of its type.

The design layout of Qwak’s Feature Store solution looks like this:

Qwak feature store architecture

Qwak’s key capabilities include:

Data Warehouse Sourced Features

Since a Data Warehouse is the central source of data for many organizations, Qwak’s Feature Store can store the ingested data from the Data Warehouse. Moreover, it can transform the data to contain all the relevant information followed by storing it back in the Feature Store database.

Multiple Data Sources

Qwak’s Feature Store also supports ingesting data from a variety of sources whether it’s structured such as a database or unstructured like logs and text files.

Streaming Aggregation

The service allows you to collect streaming data continuously from platforms such as Kafka. This is helpful because now you can aggregate such information for real-time analytics.

Training API

The platform stores historical versions of previously computed features which you can access using their Training API. The Training API is optimized for large datasets which can be beneficial for training and testing purposes.

Serving API

With the help of Serving API, the most recent version of precomputed features are available for use instantly. It is made to handle large-scale data with a high throughput and low latency.

Thus, by abstracting the complexities of model deployment, integration, and optimization, Qwak brings agility and high velocity to all ML initiatives designed to transform business, innovate, and create a competitive advantage.

If you’re still skeptical or want to know more, why not book a demo catered specifically for your needs?

Pros Cons
Data Warehouse Sourced Features: Integrates with data warehouses for centralized data management. Newer Platform: Less established in the market, which might raise concerns about long-term support and updates.
Streaming Aggregation: Supports continuous data collection from platforms like Kafka. Integration: May require effort to integrate with existing data pipelines and systems.
Training and Serving APIs: Optimized for large datasets and high-throughput, low-latency operations. Learning Curve: Might have a steeper learning curve for teams unfamiliar with its specific features.

3. AWS Feature Store

AWS Feature Store - SageMaker is a one-stop store for feature use across the entire ML cycle. Its main benefits comprise ingesting data from any source, feature store, security, etc. But these are the core benefits of using a Feature Store. 

What makes AWS SageMaker Feature Store special is that it also provides framework support for Jupiter Notebooks, TensorFlow, PyTorch, etc. These are the leading tools used for building machine learning models.

The layout below explains how SageMaker Feature Store works:

AWS feature store

Some other fascinating features SageMaker Feature Store offers are as follows:

Geospatial ML

AWS Feature Store now provides support for Geospatial data such as satellite imagery, location-based data, maps, etc. This includes several use cases:

  • Monitoring climate change
  • Maximizing harvest yield and food security
  • Predict retail demand based on location

Lineage Tracking

With the help of SageMaker Feature Store’s Lineage Tracking, you can create and store the workflow process step-by-step. This will help you reproduce those steps if need be and track the lineage. 

Time Travel

Sometimes you need to build your ML model using the data from a particular timeframe excluding anything and everything beyond that. SageMaker’s Feature store’s point-in-time queries can help you achieve that by retrieving the features at a specific point in time. Additionally, it can be executed in Apache Spark for a more interactive environment.

If you’re not familiar with SageMaker Feature Store’s platform, AWS has a dedicated page on how to get started. The section also covers installing the prerequisites and setting up a domain. 

Regarding the pricing, SageMaker Feature Store offers a free trial. AWS's free-tier feature offers multiple options such as free trials, 12 months free, always free, etc. 

Pros Cons
Framework Support: Compatible with popular tools like Jupyter Notebooks, TensorFlow, PyTorch. Cost: Can be expensive, especially for large-scale data storage and operations.
Geospatial ML Support: Offers capabilities for handling geospatial data like satellite imagery. AWS Ecosystem Dependency: Best suited for users already integrated with the AWS ecosystem.
Lineage Tracking and Time Travel: Facilitates detailed tracking and historical data queries. Complexity: Might be overwhelming for users not familiar with AWS services.

4. Tecton Feature Store

Tecton provides a fully-managed feature platform to monitor the whole lifecycle of your features. With Tecton, you can manage the features as files in a GitHub repository using a declarative framework. 

Tecton feature store

What does Tecton offer?

The platform offerings can be summarized as follows

  1. Transform: Allows you to define real-time transformation in the same manner as you would define your batch transformations. 
  2. Store: The Feature Store platform gives 2 options - offline and online. The offline Feature Store can be used for large-scale dataset retrieval whereas online Feature Stores are essential online serving with low latency. 
  3. Serve: Provides ultra-low latency to serve your features with the help of a REST API and scales up to 100,000 queries per second.
  4. Monitor: With the monitoring feature, you can easily monitor feature availability, machine learning pipelines, etc. It also gives you an insight into your storage and computation costs which can help you with managing your finances. 


  1. Scalability: Tecton optimizes resource allocation and it can scale dynamically based on your needs.
  2. Source data from multiple places: With the help of Tecton’s abstractions, data scientists do not need to worry about where the data is getting sourced from. It allows you to use batch, streaming, and real-time data to develop features.
  3. Compute and Storage Flexibility: Tecton also gives you the option to store features in the platform of your choice, thus providing compute and storage flexibility. 
  4. Seamless Integration: The platform can interact with the already existing machine learning tools in your organization to enhance the integration and make it seamless.
  5. Choice of tools: You can define the features using your platform of choice such as SQL, Python, etc. 

If you're new to the platform, consider watching this web demo uploaded by Tecton.

Pros Cons
Scalability: Dynamically scales based on needs, optimizing resource allocation. Cost and Accessibility: May be expensive for small-scale operations; not as accessible to smaller companies.
Multiple Data Source Integration: Can handle batch, streaming, and real-time data. Platform-Specific Learning Curve: Requires familiarization with Tecton's specific features and interface.
Monitoring and Financial Insight: Offers feature monitoring and insights into storage and computation costs. Integration Complexity: Integration into existing ML workflows can be complex and time-consuming.


Feature Stores are becoming a crucial part of companies and enterprises for building their machine-learning pipelines. Whether or not you require a feature store is a subjective question; we tried to address that through this article by outlining the benefits and limitations of the best feature stores out there. We hope the feature store comparison we provided will help you find a feature store platform that serves your needs.

Chat with us to see the platform live and discover how we can help simplify your AI/ML journey.

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