Advantages of Elastic Computing for Automations with Python RPA

1) ECONOMY: Pay only for the infrastructure actually used, eliminate idleness, implement parallelism, and have significant savings;

2) AGILITY: Quickly scale up or down resources (even automatically) as needed to meet variations in demand, faster execution of automations, and easier portability;

3) INNOVATION: Testing new ideas without requiring significant infrastructure investments;

👉 As RPA grows in enterprises, the dynamism of needs requires modern computing architectures that enable dynamic resource allocation for scale gain and more agility in the execution of operations.

✅ Elastic computing is the ability to rapidly (or even automatically) expand or shrink computing resources of storage, memory, and processing to meet peak demand, eliminate idleness, and optimize costs.

✅ In elastic computing, you pay only for what you use. Thus, you can achieve significant savings and provide a better user experience by immediately making infrastructure and resources available as needed.

✅️ Furthermore, elastic computing favors experimentation and innovation by providing access to computational resources without requiring significant infrastructure expenditures.

✅️ Python automations can run in any environment: desktops/VMs (Win/Linux/Mac), containers, and even serverless, and can run multiple automations in the background simultaneously (ultra-parallelism).

✅️ With the BotCity platform, you can easily make deploys, manage the deploys on the dashboard, and orchestrate the automations;

✅️ Containers are lighter than VMs. They enable multiple tasks in the same instance, reduce implementation time, and facilitate portability between environments. Serverless is lighter than containers, scales faster, and further abstracts infrastructure provisioning.

✅️ VMs, containers, and serverless have different maintenance and limitations. It is up to you to evaluate which model is most suitable for the automation scenario.

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