Crystal-clear visibility into cloud spend at the workload and tenant level
- Days
- Time to first cost insights after install
- Days
- From install to powerful cost reporting
- Workload & tenant
- Granularity of cost visibility
Sweeeft.ai is a pioneering HR technology startup harnessing artificial intelligence to transform how organizations manage human resources. The platform revolutionizes HR operations by automating critical tasks such as recruitment, talent assessment, and training. Leveraging advanced technologies like large language models and behavioral analysis algorithms, Sweeeft.ai streamlines candidate screening, matching, and evaluation. The company offers customizable and scalable solutions with flexible configurations tailored to each organization's unique requirements.
Sweeeft.ai needed to architect an AI-ready, cloud-native foundation but lacked in-house cloud expertise. The company encountered significant obstacles migrating AI workloads to Amazon SageMaker, with models failing during training due to version incompatibilities between SageMaker Deep Learning Containers, PyTorch, and Hugging Face Transformers.
DoiT conducted a comprehensive assessment and recommended a cloud-native AWS approach. The team determined optimal combinations of Python, PyTorch, and HuggingFace versions compatible with AWS Deep Learning Containers. DoiT implemented auto-scaling policies on SageMaker asynchronous inference endpoints and provided training to enable independent operation.
Through the assistance of Doit, we were able to successfully migrate our LLM workloads to AWS using SageMaker. Rather than simply transferring our existing infrastructure, DoiT enabled Sweeeft.ai to embrace a cloud-forward architecture, swiftly implementing cutting-edge generative Al innovations and expanding our capabilities. These innovations are helping us help our own customers, enabling global HR teams to reduce their hiring time by up to 60%.
Verona Selimaj, International Development Lead, sweeeft.ai
Explore how Cloud Intelligence™ helps teams improve visibility, governance, and unit economics across cloud environments.
Attribute™'s platform is truly unique. We now have crystal-clear visibility into our cloud spend at the workload and tenant level, and that insight has already led to actionable savings and powerful insights as we further scale our service.
Paul White, VP of Engineering, Shasta Cloud
We have worked with DoiT for many years, and there has been an increasing number of capabilities and features in DoiT Cloud Intelligence. We've embedded features such as Cloud Analytics and Reports in our own FinOps processes, it's become core to what we do.
Martin Lee, Director of Operations
DoiT gave us the confidence to move from experimentation to production. They helped us understand the right way to build AI for the real world.
Milad Rezazadeh, CTO
Attribute™'s cost grouping technology took our cost visibility and allocation to a whole new level. Now, our teams are fully accountable for their budgets, significantly improving our cloud efficiency and helping us minimize unnecessary costs.
Eli Zilbershtein, Head of DevOps, Hippo
You can't tag a customer in a multi-tenant environment. Attribute™ finally shows us what each customer costs and what's driving those costs.
Omri Cohen, Director of Engineering, Platform
Attribute™'s data is truly unmatched. No other solution on the market could deliver the precise customer cost and usage profiles we needed in such a complex infrastructure. Within weeks, the data from Attribute™ transformed our understanding of cost structures, influencing key strategic decisions in pricing, renegotiations, and market positioning.
Jonathan Langer, COO, Claroty
Attribute™ simplified tracking customer costs in our multi-tenant environments. Customer cost measurement is now clear and standardized, and finance gets the business context they need. Integration was quick and required no changes.
Kfir Lippmann, CFO, Salt Security
Attribute™ translates complex cloud bills into actionable, business-centric insights that empower our engineering teams to take true ownership of their costs.
Balamurugan Mohandossgandhi, Head of IT and Infrastructure, PropertyGuru
This has let us get a better idea of what our cost of goods sold really is. It's not every day you come across something that delivers value as quickly as yours did for us. I was seeing useful insights inside the POC, and we had only deployed it to a couple of real clusters.
Jason Moore, Principal DevOps Engineer, Accrete AI
Eliminating the need to tag thousands of resources has freed up my team and we've invested our efforts in enhancing our platform significantly.
Ziv Sivan, VP of Engineering
PerfectScale by DoiT has become an important part of how we optimize Kubernetes at scale at OneFootball. It gives our platform team the visibility, automation, resiliency insights, and confidence we need to balance cost efficiency with production readiness, especially as we prepare for major global football moments like the 2026 FIFA World Cup.
Andrea Benfatto, Platform/Cloud Runtime Engineering Manager
Cloudflow's new RDS End of Life alerts have allowed us to be more proactive on keeping our database instances up-to-date. The new solution gives us internal visibility ahead of time so that we can prepare for upgrades, instead of having to upgrade under pressure while incurring extended support costs.
Jon Fairbanks, Site Reliability Engineering Manager
PerfectScale cut 40% off our total EKS spend, and the automations handle what used to take our team 20 hours a month. Now we spend that time on reliability and performance instead of chasing cost metrics.
Caio Cristo, Director of Infrastructure/SRE
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