Multi-vendor architecture: Merging Low-Code and Python RPA for Optimal Automation in 2024

The debate between low-code and Python RPA is ongoing in Robotic Process Automation (RPA) and Hyperautomation Industry. While platforms like UiPath, Automation Anywhere, BluePrism, and Power Automate have gained traction due to their user-friendly interfaces, there’s an emerging trend towards integrating these with more flexible, code-based platforms such as BotCity Python RPA. Here, we explore the advantages of this multi-vendor approach, especially for businesses with underused licenses.

1. Multi-vendor RPA Architecture = Comprehensive Solution Coverage

A multi-vendor RPA architecture allows organizations to leverage the best of both worlds. While low-code platforms excel at quick deployments for standard processes, a code-based platform like BotCity can address more complex, custom automation needs. This means no process is left behind due to platform limitations.

combine low-code and python rpa.

2. Enhanced Scalability and performance

Low-code platforms often come with predefined scalability thresholds. By integrating with a code-based platform, organizations can ensure that their RPA efforts can scale in line with business growth without being restricted by license limitations or platform capabilities.

Performance matters, but it depends on the use cases. For those needing speed, Python automations can be up to 20x faster than the same automation built in low-code.

3. Cost Efficiency

For many companies, underused licenses of popular low-code platforms represent sunk costs. By augmenting these platforms with BotCity Python RPA, businesses can maximize ROI by deploying the right tool for the right job, ensuring that all licenses – low-code or code-based – are used to their full potential. In plain math, you can balance your automation stack towards Python, scale your operations, and have significant savings. Some of our customers saved thousands of dollars by cutting 50-60% of low-code licenses and adopting BotCity Python RPA to a more scalable operation and reduced licensing costs.

4. Enterprise Governance

There was a constructed myth that Python RPA is not viable due to Governance issues. BotCity’s code-based nature does not mean compromising on governance. On the contrary, it offers robust governance mechanisms that align with enterprise standards. We built (and keep evolving) BotCity Maestro and the state of the art of code-based orchestration, not just thinking about scheduling or logging automations but informing stakeholders, providing insights, and high-scale operations. This ensures that all automations, whether developed through drag-and-drop interfaces or coding, adhere to organizational policies and best practices.

RPA Report created based on orchestration metrics

5. Skillset Diversification

Skillset and use case diversification are some of the most exciting outcomes (after the saving on licenses and scale power). A multi-vendor approach encourages skill diversification among RPA teams. While some developers work on low-code platforms, and you can empower business users and citizen developers, others can delve deep into code, fostering a culture of continuous learning and cross-skilling. You can organize your operation by use cases: business users can build simple automations on task level using low-code, while your developer team can build critical enterprise-wide scaled automations can be built in Python. You can have the best of both worlds.

6. Lock-in Protection and Leverage over Vendors

We all get worried about depending on a single technology vendor, which is not a secret in the whole IT industry and is similar in the RPA and Hyperautomation Industry. Adopting a Python RPA solution as BotCity gives you the advantage of the open source RPA, which means that the code is yours but is Open Source. You are not stuck with BotCity. You stay with us while we deliver productivity for your development team and Enterprise-level governance to your company and CoE.

7. Generative AI + Python RPA

Adopting a Python RPA solution allows you to implement custom algorithms, modify existing ones, or integrate with advanced machine learning and AI libraries like TensorFlow, PyTorch, and GPT models. This flexibility is vital for specialized tasks in generative AI.

If you want a severe application of Gen AI, you need fine-grained control over every aspect of the automation and AI process. This control is crucial when optimizing generative AI models or ensuring they operate under specific constraints. Python RPA can be easily scaled to handle large datasets for Scalability, which is essential for training robust generative AI models. Python RPA can be deployed on robust cloud infrastructures or on-premises servers tailored for heavy computation.

dall+e+chat gpt+python rpa
Python RPA + Gen AI – Image generated by DALL-E 2

8. Seamless Integration Capabilities

BotCity’s Python RPA, being code-centric, can seamlessly integrate with various systems, databases, and APIs. This ensures that automation workflows, even those spanning multiple platforms, are smooth and interruption-free.

Planning multi-vendor architecture for 2024:

If you are responsible for the Automation Stack in your company, we invite you to consider this topic for 2024: a multi-vendor RPA strategy is not just a nice to have; it’s essential for modern businesses. By harnessing the strengths of low-code platforms like UiPath, Automation Anywhere, BluePrism, and Power Automate alongside the power and flexibility of BotCity Python RPA, organizations can ensure they are fully equipped to meet the diverse challenges of today’s digital landscape.

If you want to know about real-world cases of coexisting platforms, you can book a conversation with our experts. And if you will try BotCity by yourself, feel free to sign up!

BotCity Cofounder and CEO

Leave a Reply