Skip Complex & Costly MLOps

Easily build and deploy ML applications with a fully managed sleek and modern ML Platform that contains everything you need to build high-quality ML applications simply.

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Chosen by the world's best ML team

Why SageMaker users migrate over to Qwak

With Qwak

With SageMaker

Using SageMaker, you’re forced to use multiple AWS tools like S3, Docker, Airflow, Jenkins, Cloudwatch, and more to hit the ground running with your models.

With Qwak, the only thing you need is Python ML Libraries to build, train, deploy, and automate your models seamlessly.

Don’t just take our word for it

OpenWeb, a social engagement platform that builds online communities around better conversations, needed a way to scale their expanding data science team’s efforts and show immediate value. Qwak enabled OpenWeb to seamlessly deploy their models quickly and without having to deal with maintenance or infrastructure configuration.

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Idan Benaun

Director of ML and Data Science

“People ask me how I managed to deploy so many models while onboarding a new team within a year. My answer is: JFrog ML.”

Qwak is the fastest growing Sagemaker Alternative

Feature
Time to Live Model	Quick	Slow
Intuitive UI	V	X
Zero-config model build & deploy	V	X
Data Source Integration	Snowflake, MongoDB, BigQuery, Athena, Redshift and more	AWS data sources
Multi-Cloud support	AWS, GCP	AWS
Support	24/7 by ML engineering experts	Standard AWS support

Qwak on 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.

Amazon SageMaker on ease of use

SageMaker, though powerful, demands a solid grasp of AWS and engineering expertise. Its UI is less intuitive than specialized platforms, requiring navigation and expertise through multiple AWS services.

A powerful end-to-end ML platform that works for you

Qwak simplifies data pipelines creations through feature sets.

Model infrastructure and data monitoring allow users to easily set up alerts and automations.

A/B testing, variable traffic split and gradual deployments are all supported within Qwak.

What does SageMaker actually cost you?

Supporting multiple models requires a major investment of time, money and maintenance:

Setup expenses can reach up to $1M.
Ongoing maintenance costs add up to an additional $500K yearly.

See the real cost

Total cost of ownership TCO of Qwak vs Amazon Sagemaker

Don’t just take our word for it

A key aspect of Lili’s business strategy is harnessing data and machine learning to enhance customer solutions. This case study explores how Qwak, hosted on AWS, empowers Lili to leverage advanced machine learning capabilities and drive better customer outcomes.

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Eyal Solnik

Head of Data, Lili

“With JFrog ML's intuitive AI Infra platform and AWS, we achieve operational efficiency and smarter personalized experiences. We get to make data-driven decisions impacting the customers and driving the company metrics.”