Automat-it Reduces Monce’s Infrastructure Costs With AWS Migration

As Monce added new customers and entered additional industrial verticals, its cloud setup became harder to scale efficiently, prompting the company to work with Automat-it on the AWS migration discussed in this case study. The goal was to improve cost efficiency, reduce infrastructure overhead, and create a model that better matched actual demand.

The industrial order workflow Monce streamlined

Monce runs B2B commercial operations for major industrial groups across construction, glass manufacturing, surface treatment, aerospace, aluminum, and B2B distribution. Its proprietary multi-agent pipeline reads inbound orders across any format, extracts technical specifications, matches them against product catalogs with customer-specific pricing, and sends the result directly into ERP.

The company describes the platform as a way to replace time-consuming manual processing. Built by operators who typed orders into AS400 for years, Monce says it reduces around 25 minutes of manual data entry per order to under 60 seconds of AI processing. It also reduces order errors from 8% to 12% to under 1% and lowers processing costs by 70%.

Those improvements helped Monce expand from a single factory deployment to multiple enterprise accounts across France while moving into new industrial sectors. As the company grew, however, the cost structure of its cloud setup became a more serious issue.

The cost pressures in Monce’s previous environment

The case study identifies three constraints that were putting pressure on Monce’s growth trajectory.

The first was fixed compute spending. Azure’s container architecture maintained fixed compute costs regardless of processing volume. That meant infrastructure spending increased with each new client, even during off-peak hours when demand was lower.

The second was AI inference economics. Monce’s multi-agent LLM pipeline reads full order conversations, performs proprietary catalog matching, applies customer-specific logic, and learns vocabulary and patterns. Running that workload on Azure AI services was more expensive than equivalent AWS alternatives, which affected unit economics as the company scaled.

The third was deployment overhead. Each new client required custom infrastructure configuration. That meant engineering time was being used on setup work rather than on product development and Monce’s expansion into revenue intelligence and multi-channel ordering.

These issues combined to make infrastructure spending harder to align with actual business activity. Costs were rising, but not always in a way that reflected real usage.

The AWS migration implemented by Automat-it

Automat-it addressed those problems by migrating Monce to AWS serverless architecture, including ECS on EC2. The solution implemented by Automat-it’s engineers and DevOps experts was based on Amazon ECS architecture and delivered through Terraform Infrastructure-as-code.

That approach gave Monce a repeatable way to create the same infrastructure while applying different configuration for each deployment. It also created the basis for a more elastic environment, rather than one dependent on fixed compute spending.

The case study says Automat-it applied best practices developed across hundreds of AWS migrations completed for other startups. These included cost optimization through infrastructure design and FinOps expertise, along with scalability planning intended to support a secure and stable environment.

On the technical side, Automat-it integrated Monce’s existing Firebase frontend with AWS ECS. The FastAPI Python application structure, which had been part of Monce’s monolithic backend before the migration, ran in that AWS environment. WebSocket connectivity between the frontend and backend was handled through an Application Load Balancer.

The cost and scaling outcomes

The migration delivered a significant reduction in monthly infrastructure costs because elastic scaling eliminated fixed compute spend during off-peak hours. That changed the way cloud costs behaved as Monce added more customers.

The case study also says the migration was completed with zero client downtime, allowing live industrial deployments to continue uninterrupted. At the same time, Terraform Infrastructure-as-code automated environment creation for each new factory, reducing new client deployment from days to minutes.

Another important result was that infrastructure costs now scale with order volume rather than increasing mainly because another client contract has been added. That gave Monce a closer connection between activity and spending, which is especially important for an AI platform serving enterprise customers across multiple sectors.

How the migration improved Monce’s cost model

This case study shows that cloud cost is not just a technical issue when a company begins to scale. For Monce, the previous setup was creating fixed spending, higher inference costs, and repeated deployment overhead at the same time. The move to AWS addressed those issues together rather than separately.

Automat-it’s work gave Monce lower monthly infrastructure costs, a more flexible cost structure, and a more repeatable deployment model. For a company expanding across industrial verticals and customer environments, that created a stronger financial and operational base for continued growth.