Comparisons
Qwak
Vertex AI
vs

Qwak vs. Vertex AI

Compare Qwak with Vertex AI by the following set of capabilities. We want you to choose the best ML platform for you.

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Qwak vs. Vertex AI on Ease of Use

Feature
Zero-config model build & deploy
V
X
Data source integration
Snowflake, MongoDB, BigQuery, Athena, Redshift and more
GCP
Multi-cloud support
AWS, GCP
GCP
Intuitive UI
V
X
Support
24/7 by ML engineering experts
Standard GCP support

Qwak ease of use

Designed with a user-friendly interface, Qwak aims to make the MLOps process as straightforward as possible. The platform is built to be intuitive, allowing users to focus on machine learning tasks without the distraction of complex configurations.

Vertex AI ease of use

While offering a robust set of features, Vertex AI has a steeper learning curve, especially for those not already familiar with Google Cloud Platform. The platform is feature-rich but may require navigating through various services and configurations.

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Qwak vs. Vertex AI on Model Building and Model Deployment

Feature
Model build system
V
X
Model deployment & serving
V
V
Real-time model endpoints
V
V
Model auto scaling
V
V
Model A/B deployments
V
Engineers required
Inference analytics
V
Engineers required
Managed notebooks
V
V
Automatic model retraining
V
Engineers required

Qwak model building and model deployment

Qwak is designed to abstract away most of the engineering complexities, allowing data scientists and ML engineers to focus on what they do best: building and deploying models. The platform handles everything from data storage to model monitoring, reducing the need for specialized engineering skills.

Vertex AI model building and model deployment

Using Vertex AI in production demands a broad skill set, including ML engineering, containerization, Kubernetes orchestration, Infrastructure as Code (with tools like Terraform or Google Cloud Deployment Manager), and networking (VPC, firewall rules). Additional GCP services like Google Cloud Storage, Google Kubernetes Engine (GKE), and Google Cloud Monitoring add complexity, requiring diverse engineering skills for effective management.

Qwak vs. Vertex AI on Feature Platform

Feature
Managed feature store
V
V
Vector database
V
V
Batch features
V
V
Realtime features
V
V
Streaming features
V
Engineers required
Streaming aggregation features
V
Engineers required
Online and offline store auto sync
V
X

Qwak feature platform

Qwak provides a fully abstracted environment, allowing users to focus on ML tasks without worrying about the underlying infrastructure. It supports both CPU and GPU instances and can run on AWS or GCP.

Vertex AI feature platform

Vertex AI is partially managed, meaning some services are fully managed while others may require manual setup. For example, AutoML is fully managed, but custom training and data pipelines might require additional configurations or integration with other GCP services.

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Qwak vs. Vertex AI on Pricing

Qwak pricing

Qwak pricing is measured in QPU or Qwak Processing Units. All the compute resources are measured on a pay-as-you-go basis, or with pre-commitment discounts.

Vertex AI feature platform

Vertex AI offers various pricing options based on usage and project requirements.

Qwak vs. Vertex AI on Maintenance

Qwak maintenance

Qwak offers a fully managed platform that simplifies the maintenance and updating of models in production for its users. It effectively removes the necessity for users to deal with any infrastructure or engineering-related tasks, streamlining the entire process.

Vertex AI maintenance

Managing GCP Vertex AI involves understanding Google Cloud's infrastructure, configuring multiple services, and utilizing its data and AI tools. Key tasks include optimizing costs, ensuring security and compliance, and maintaining efficient data pipelines and ML workflows. Regular updates and platform changes also demand continuous learning and adaptation.
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Qwak vs. Vertex AI on Scalability

Qwak scalabilty

Qwak supports ML projects at any scale, accommodating both small and large ML initiatives. Use Qwak’s auto scaling features to scale your models automatically

Vertex AI scalability

Vertex AI is built on Google Cloud, which provides scalable cloud-based ML services. Google's infrastructure is known for its elasticity, making Vertex AI suitable for projects with fluctuating workloads.

Qwak vs. Vertex AI on Support

Qwak support

Qwak offers 24/7 support by top ML engineering experts via Slack, console chat or Zoom.

Vertex AI support

Vertex AI benefits from Google Cloud's support resources, including a community, documentation, and various support plans. Users can access assistance and expertise as needed.
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