How SkuCaster Transformed SKU Demand Forecasting with Azure Databricks

In the age of hyper-competitive retail and manufacturing, the difference between thriving and barely surviving often comes down to how well you predict demand. Overstock, and you burn capital on inventory sitting in warehouses. Understock, and you lose customers to competitors who can fulfill orders faster. For companies managing thousands of SKUs across multiple product lines, demand forecasting is both a science and an art.

That’s the problem SkuCaster set out to solve.

SkuCaster is an AI-driven forecasting platform that helps businesses predict demand at the SKU level with up to 94% accuracy. By analyzing historical sales, shipment records, external market factors, and customer behavior, SkuCaster gives retailers and manufacturers the insights they need to optimize stock levels, reduce waste, and capture more revenue.

But like many startups, SkuCaster began with an on-premises infrastructure that worked for early product iterations but wasn’t built for scale. As demand grew, the infrastructure creaked under the weight of compute-heavy workloads, manual operations, and escalating costs.

This is the story of how SkuCaster partnered with beCloudReady to migrate from a Kubernetes-based on-premises system to Azure Databricks Serverless, saving over $100,000 in operational expenses in just one year, while simultaneously improving forecasting accuracy and freeing up valuable engineering time.

The Challenge: When Growth Meets Infrastructure Bottlenecks

SkuCaster’s core product depends on machine learning models that crunch large volumes of SKU-level data. Each forecast involves processing data pipelines, running feature engineering workflows, training predictive models, and delivering outputs to clients in near real time.

Initially, SkuCaster ran this entire setup on-premises, using a Kubernetes (k8s) cluster integrated with multiple data systems. While flexible in theory, the setup quickly became a burden in practice:

1. Rising Infrastructure Costs

   • Running Kubernetes clusters and maintaining the underlying servers was costing around $1,200 per month.

   • Scaling compute for demand spikes only added to the bill.

2. DevOps Overhead

   • Patching, security updates, monitoring, and scaling were all handled manually.

   • The company needed dedicated DevOps resources, which added to payroll.

3. Limited Scalability

   • As compute needs increased, the existing setup struggled to keep up.

   • Scaling required intervention, delaying workflows and affecting agility.

4. Risk of Stagnation

   • Time spent managing infrastructure was time not spent improving forecasting models.

   • For a small, fast-moving company, this was a dangerous trade-off.

A lift-and-shift migration to Azure VMs was considered. But this would have replicated the same problems in the cloud: around $1,000/month in VM costs plus the ongoing need for DevOps staff. In other words, it wasn’t modernization — it was just moving the pain elsewhere.

SkuCaster needed a cloud-native, serverless solution that would scale effortlessly, cut costs dramatically, and eliminate infrastructure overhead.

The Solution: Modernization with Azure Databricks Serverless

Instead of lifting and shifting, SkuCaster made the smarter choice: modernization.

With beCloudReady’s expertise as a Databricks Partner, the team redesigned SkuCaster’s architecture to run on Azure Databricks Serverless Jobs. Here’s how the new setup looks:

1. Serverless Execution

   • Instead of running their own Kubernetes clusters, SkuCaster now relies on Databricks’ serverless compute.

   • This means no more managing nodes, patching software, or worrying about cluster scaling.

2. Seamless Data Integration

   • Azure Databricks connects directly to SkuCaster’s source systems.

   • Pipelines ingest data securely, transform it, and feed it into ML workflows without manual intervention.

3. Managed ML Platform

   • Databricks provides a fully managed ML environment, covering everything from notebook-based development to automated job execution.

   • Built-in versioning, experiment tracking, and collaboration features improve productivity.

4. Built-In Security & Compliance

   • With Azure’s patching and compliance baked in, SkuCaster no longer worries about vulnerabilities or compliance audits.

5. Pay-Per-Use Scalability

   • Compute resources scale up automatically when workloads spike and scale down when idle.

   • This prevents over-provisioning and reduces costs dramatically.

In short, SkuCaster’s team no longer runs infrastructure. They just run forecasting jobs.

The Results: $100K Saved, Accuracy Improved

The migration wasn’t just a technical success — it was a business transformation.

1. Massive Cost Savings

• Infrastructure costs dropped from ~$1,200/month to ~$75/month.

• That’s a savings of over $12,000 annually on infrastructure alone.

• Add in the elimination of DevOps salaries and overhead, and SkuCaster saved over $100,000 in OpEx in just one year.

2. Zero Operational Overhead

• No more Kubernetes headaches.

• No more patching, monitoring, or scaling issues.

• The team no longer needs in-house IT staff, DevOps engineers, or DBAs dedicated to infrastructure.
  
  

3. Improved Accuracy and Data Quality

• With infrastructure burdens lifted, the data science team could focus on improving ML models.

• Databricks’ managed pipelines improved data consistency and quality, leading to better forecasting accuracy.
  
  

4. Scalable, Future-Proof Architecture

• The platform now grows seamlessly with demand.

• SkuCaster can onboard new customers without worrying about compute capacity.

• The architecture is ready for future AI/ML innovations.

As one SkuCaster engineer put it:

“We used to spend half our time making sure the system didn’t break. Now we spend 100% of our time making the forecasts smarter.”

Why This Matters for AI-Driven Businesses

SkuCaster’s case study highlights a critical truth: AI-driven businesses can’t afford to waste energy on infrastructure plumbing.

Every hour spent patching servers or fixing pipelines is an hour not spent improving models, building features, or delivering value to customers.

For startups and mid-sized companies, especially, the benefits of modernization are clear:

• Cost Efficiency — Serverless reduces both cloud bills and staff overhead.

• Agility — Teams can focus on innovation, not maintenance.

• Scalability — Growth no longer requires painful infrastructure upgrades.

• Security — Managed platforms reduce risk and ensure compliance.

In a market where margins are thin and competition is fierce, these advantages can make the difference between scaling successfully and stalling out.

Lessons Learned from SkuCaster’s Migration

For other businesses considering a similar journey, SkuCaster’s story offers several takeaways:

1. Don’t Just Lift-and-Shift
   Moving existing infrastructure to the cloud without rethinking architecture often just moves the problems. Look for modernization opportunities.

2. Leverage Serverless Where Possible
   Serverless platforms eliminate the invisible costs of patching, monitoring, and scaling — costs that add up quickly.
   
   

3. Partner with Experts
   Cloud modernization can be complex. Working with partners like beCloudReady accelerates the process and ensures best practices.

4. Focus on Your Core Competency
   SkuCaster’s value lies in demand forecasting, not in running servers. By outsourcing infrastructure to Azure Databricks, they doubled down on what makes them unique.
   
   

The Bigger Picture: Cloud-Native Future for Forecasting

SkuCaster isn’t just a case study in cost savings — it’s a case study in focus.

By modernizing their infrastructure, they shifted from being part-time infrastructure operators to being full-time innovators. That’s the real win.

As forecasting grows more complex — incorporating real-time data streams, IoT signals, and external datasets — platforms like SkuCaster will need more compute, not less. With Azure Databricks, they now have a foundation that scales indefinitely.

For customers, that means more accurate forecasts, faster insights, and better decisions. For SkuCaster, it means the ability to grow without limits.

Final Thoughts

SkuCaster’s journey from on-prem Kubernetes to Azure Databricks Serverless shows how smart cloud modernization can unlock both cost savings and innovation.

• $100K saved in one year

• 94% SKU-level forecasting accuracy

• Zero DevOps overhead

• Future-proof, scalable architecture

This is more than just an IT success story — it’s a business growth story.

SkuCaster now stands as a proof point for every AI-driven startup wrestling with infrastructure headaches: modernization isn’t just an option; it’s a competitive necessity.

And for customers relying on SkuCaster’s forecasts, the result is simple: better predictions, lower costs, and smarter decisions.

About SkuCaster
SkuCaster is an AI-powered platform that helps retailers, manufacturers, and distributors predict SKU-level demand with industry-leading accuracy. By combining machine learning with cloud-native infrastructure, SkuCaster empowers businesses to optimize inventory, reduce waste, and maximize revenue.