DoiT

DoiT is a global technology company that works with cloud-driven organizations to leverage the cloud to drive business growth and innovation.

The only intent-aware FinOps platform that goes beyond cost optimization to drive reliability, performance, and security – turning endless good ideas into real, implemented solutions at scale – ensuring teams achieve their business outcomes. We combine data, technology, and human expertise to ensure you’re operating in a well-architected and scalable state - from planning to production. Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help you solve complex multicloud problems and drive efficiency. With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work alongside more than 4,000 customers worldwide.

10/22/2026

Reading your cloud bill is the easy part. The invoices next to it decide whether you can forecast.

Jupiter Exchange runs mostly on Amazon Web Services, Google Cloud, Cloudflare Workers and bare metal picked up through acquisition. As a decentralized exchange aggregator on Solana, some of its biggest line items are RPC providers billing by PDF. Around 20% to 40% of infrastructure spend sat outside any system.

Satyam Gupta and our Forward Deployed Engineering team turned one real invoice into a working pipeline in DoiT Cloud Intelligence™, reporting and alerts included. Jupiter tweaked it and shipped it.

✅ 26 outside vendors ingested, RPC providers to ClickHouse
✅ Spend coverage up from 60% to 100%
✅ Forecasting across the full vendor catalog

Read the full story: https://bit.ly/4BAk0zo

09/22/2026

Guessing a workload's memory limit takes thirty seconds. Living with that guess takes months.

helps pharmaceutical researchers run observational clinical trials, processing large volumes of medical data across several Kubernetes clusters. CPU and memory needs shift job to job, but resource requests were set at creation and rarely touched again until the out-of-memory errors arrived. Jobs failed and retried, queues built up, shared databases took the load.

PicnicAI came to PerfectScale.io by DoiT through a broader cloud cost optimization initiative. Our Field Engineering and Customer Success teams worked with their engineers on how HPA and the cluster autoscaler interact, and which workloads were safe to automate first.

PerfectScale by DoiT carried the optimization: PerfectScale's InfraFit exposed node configurations that did not fit their workloads, and automation policies let them widen rightsizing at their own pace across development, staging and production.

✅ 85-90% of application workloads on automated resizing, up from zero
✅ ~50% less team time spent managing infrastructure
✅ Fewer memory-related failures across variable data-processing workloads

Read the full story: https://bit.ly/4izrE5Y

09/02/2026

A platform's goal is to hide Kubernetes complexity from your engineers. Keeping it efficient as demand explodes is the real work.

leveraged PerfectScale.io to keep its Kubernetes platform efficient as AI-driven demand took off. The team runs a self-service, multicloud Kubernetes platform, primarily on Microsoft Azure, so engineers can build and ship without becoming Kubernetes experts. Native open-source tools couldn't surface where cost and waste were hiding.

The PerfectScale team worked hand in hand with Stefanini's engineers, learning their environment, flagging drifting spend, and shipping their feature requests within months.

PerfectScale carried the optimization: automation continuously right-sizes live workloads, and cluster-level visibility exposes cost and waste down to the individual workload.

✅ ~26% of total Kubernetes compute saved every month, about half the potential still open
✅ Fewer production incidents, with faster root-cause when they do happen
✅ Growth absorbed without doubling the size of the platform team

Watch the whole video here: https://bit.ly/4idjxfj

08/25/2026

Moving 40 servers to AWS is a project plan. Getting two clouds to talk to each other first is the real work.

came to DoiT with a live four-tier environment on IBM Cloud Infrastructure: load balancers, application servers, self-managed Docker Swarm clusters, MySQL and Cassandra. All of it had to reach Amazon Web Services while staying connected to what was still running on IBM.

Our Forward Deployment Engineering team worked through the options, moved them from IBM Classic to AWS VPC, and traced the subnet overlap that was breaking the routing.

Cloud Intelligence™ carried the rest: Insights surfaced the cost optimizations, including right-sizing their security tooling spend, and Cloud Diagrams gave them a live visual of the new environment.

✅ All 40 servers migrated 1:1, four-tier topology intact
✅ Two IBM Cloud regions consolidated into one AWS VPC across multiple Availability Zones
✅ All three AWS MAP phases delivered: Assess, Mobilize, Modernize

Read the full story: https://bit.ly/4wG8ej6

08/06/2026

With Google Cloud providing the infrastructure and DoiT providing Cloud Intelligence, can focus on building the future of retail.

We are proud to share this video case study with the Flooid team, filmed at their Birmingham HQ. It is a brilliant example of how cloud partnerships go beyond billing, into real FinOps maturity, continuous optimisation, and scalable innovation.

Thank you Andrea, Martyn, Martin and Thomas for this great interview.

🎬 Watch the full interview here → https://bit.ly/4yX4Ti7

07/14/2026

Great cloud engineering deserves cloud spend you can actually read.

On a multitenant SaaS platform, cost management too often lives disconnected from how engineers actually build and run services. 's CIO, Flavio Alberti, describes what changed when FinOps stopped being a separate exercise and became part of the everyday engineering workflow:

"Before DoiT, managing Amazon Web Services(AWS) costs across a multitenant SaaS platform meant constant manual effort and fragmented visibility. DoiT changed that. DoiT Cloud Intelligence platform made it natural to embed FinOps practices into our everyday engineering workflows, not as a separate exercise, but as part of how we build and run our services. Combined with responsive technical support and a team that genuinely understands cloud architecture, it has helped us keep costs under control and continuously optimize the infrastructure behind our platform. After ten years as an AWS partner, DoiT is the layer that finally made that investment fully legible."

This is what intent-aware FinOps looks like in practice: costs that stay legible while the team keeps shipping.

Click here to learn how customers leverage the DoiT Portfolio: https://www.doit.com/customers?utm_campaign=Sprout+Social+Traffic&utm_content=Great+cloud+engineering+d&utm_medium=social&utm_source=facebook

06/25/2026

Most GenAI prototypes don't make it to production. The reason isn't always the technology.

"The technical side was not the biggest challenge," says Milad Rezazadeh, CTO of Finlex. "The real challenge was understanding how to bring AI into the business in a way that actually solves problems."

partnered with DoiT through the AWS GenAI Accelerator program to close that gap. Working with Rajan Bhave, AI Architect at DoiT, the team:

→ Re-built the Finlex Assistant on Amazon Bedrock AgentCore with secure, multi-tenant identity
→ Hardened the design for GDPR and EU AI Act considerations
→ Cut cumulative cloud costs 65% since 2024 through governance with Cloud Intelligence™

Now Finlex is reinvesting those savings into the next phase: building AI deeper into the platform, not bolting it on top.

What changed wasn't just the cost number. It was the confidence to evaluate, build, and ship new AI capabilities to production.

Read the full story: https://bit.ly/4gCZ1Uf

06/23/2026

You can run the predictions on the 2026 FIFA World Cup. Your Kubernetes infrastructure shouldn't be a prediction.

Onefootball reaches 200M+ fans, and its platform team runs the Kubernetes that absorbs every traffic spike. To protect the fan experience, they overprovisioned, which kept things safe but buried cost in idle capacity and left manual rightsizing unable to keep pace with live-sports demand.

The answer wasn't more dashboards. It was continuous, automated optimization. OneFootball leverages PerfectScale by DoiT to move from defensive overprovisioning to data-backed rightsizing across their Kubernetes environment .

→ 25% reduction in Kubernetes infrastructure costs
→ ~80% less engineering effort on cost optimization and resiliency tuning
→ A fundamental shift in how rightsizing, autoscaling, and reliability decisions get made

Now optimization is part of how the team prepares for global football moments, scaling for the unpredictable without trading efficiency for confidence.

Full story: https://bit.ly/4xOLPCe

06/11/2026

Tracking which database engines are about to lose support is the kind of task nobody owns, until something breaks.

Raptive powers independent creators and digital publishers, running a fleet of dozens of Amazon Web Services RDS databases. As engine versions approached end of standard support, the team was looking at either rushed upgrades or climbing extended-support bills. Documenting it all by hand was on the table, until they found a better way.

DoiT deployed a Cloud Intelligence™ CloudFlow template that scans every RDS instance across all accounts and AWS regions, then alerts the team six months before any engine reaches end of support.

→ 37 databases covered automatically, and counting
→ ~$10k/year in extended-support costs prevented
→ Six months of runway to plan upgrades instead of scrambling

The "RDS Engines Nearing EOL" template is available inside CloudFlow. Open it, search the Templates tab, and you're set.

Full story: https://bit.ly/3QxDim8

06/09/2026

When your platform connects 100 million patients to care, "optimize cloud costs" can never mean "risk an outage."

That was the tightrope for the SRE team at Luma Health. Nine engineers across Brazil and Europe were spending 20+ hours every month manually rightsizing Kubernetes pods on Amazon EKS: reviewing metrics, tuning requests and limits, and still defending every dollar to finance.

They deployed PerfectScale.io by DoiT across their EKS clusters with environment-aware policies, aggressive automation in lower environments and a balanced approach in production. The first optimization cycle delivered:

→ 40% reduction in Amazon EKS costs
→ 90% less time spent on manual rightsizing
→ 1,700+ engineering hours a year redirected to reliability and performance

Read the full story → https://bit.ly/4uGGfix

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