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AWS Solutions Library

Guidance for Tulip Manufacturing Execution System (MES) on AWS

Overview

This Guidance illustrates how to build seamless data flows between the Tulip Frontline Operations Platform and an Amazon Virtual Private Cloud (Amazon VPC). This integration helps manufacturers digitize operator workflows, automate data collection, and gain real-time visibility into shop floor operations. The Tulip Manufacturing Execution System (MES) provides a user-friendly, no-code environment where users can create functional applications that replace manual, paper-based processes. Through Tulip Connectors, users can seamlessly ingest data into their applications and store it in Tulip Tables, transforming fragmented systems into a unified digital solution for manufacturing operations.

How it works

This architecture diagram showcases comprehensive data flows between a Tulip Manufacturing Execution System (MES) and AWS. The Tulip MES is offered as a software as a service (SaaS) on AWS, enabling data transparency and generating actionable insights.

Download the architecture diagram PDF

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon EKS and the ALB automate critical infrastructure management tasks. Specifically, Amazon EKS provisions and maintains control plane nodes across multiple Availability Zones (AZs), delivering high availability for the Kubernetes environment. The service handles Kubernetes version upgrades, security patches, and bug fixes, eliminating manual maintenance overhead. ALB complements this automation by monitoring target health and routing traffic away from unhealthy instances, maintaining application reliability across AZs. The integrated monitoring, security, and compliance features provide comprehensive operational visibility, allowing teams to focus on application development rather than infrastructure management.

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Multiple layers of security are implemented through three core AWS services: Amazon S3 , ALB , and Amazon EKS . Amazon S3 enforces data isolation by assigning dedicated IAM credentials and isolated buckets to each customer, preventing unauthorized cross-tenant access. ALB secures application communication through SSL/TLS termination and client authentication, protecting data in transit between clients and services. Within Amazon EKS , Kubernetes namespaces create logical boundaries that isolate customer workloads so that resources remain separate and secure. This multi-layered security approach delivers a robust multi-tenant environment with clear security boundaries and access controls.

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Within this architectural framework, Tulip MES deploys multiple AWS services across application, data, and monitoring layers.  At the application layer, Amazon EKS hosts core services while ALB distributes traffic across AZs, maintaining service availability during zone failures. The data layer combines Amazon RDS for PostgreSQL and Amazon S3 , providing automated backups, replication, and failover capabilities for structured data, while offering durable object storage for customer files. To maintain platform stability, Tulip MES monitors infrastructure health through integrated open-source tools—Prometheus, Grafana, and ClusterFlow—deployed within their Amazon EKS cluster. This comprehensive approach enables rapid issue detection and resolution for consistent platform availability.

Read the Reliability whitepaper

The Tulip MES architecture optimizes performance efficiency through integrated AWS services. Amazon EKS automatically scales application components based on demand, while Amazon RDS for PostgreSQL adjusts compute, memory, and storage resources to match database workload requirements. Amazon S3 provides consistent performance for file operations regardless of storage volume, handling concurrent access without degradation. OpenSearch Service processes log data and analytics queries at scale, enabling rapid search and visualization of operational data. These services work together to maintain performance as workloads grow, reducing infrastructure bottlenecks and delivering responsive experiences across a multi-tenant platform.

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Tulip optimizes costs through the flexible pricing models of AWS and automated resource management. Amazon EKS and Amazon RDS for PostgreSQL implement demand-based autoscaling, preventing resource waste by matching capacity to actual usage. For predictable workloads, Tulip reduces costs through Amazon EC2 Reserved Instances and Savings Plans , applying discounted rates to steady-state Amazon Elastic Compute Cloud (Amazon EC2) and Amazon RDS for PostgreSQL usage. This combination of automated scaling and strategic pricing commitments minimizes operational overhead while maintaining optimal resource utilization.

Read the Cost Optimization whitepaper

By using managed AWS services like Amazon EKS , Amazon RDS for PostgreSQL , and Amazon S3 , Tulip can reduce the energy and resources required to provision, operate, and maintain their own infrastructure. Managed services often have a higher degree of efficiency and optimization compared to self-managed infrastructure. By using the autoscaling features in services like Amazon EKS and Amazon RDS for PostgreSQL , Tulip automatically scales resources up and down based on demand. This can help minimize resource waste and improve energy efficiency by only using the necessary compute and storage capacity at any given time.

Read the Sustainability whitepaper

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.