Why event-driven data pipelines are important for AI applications

For AI to be effective, it needs real-time, high-quality data. Without it, you risk losing relevance, accuracy, and customer trust in using AI applications. This article explores what common challenges in scaling AI applications in production. What exactly is an event-driven data pipeline, and why is it so important for AI applications? How does it compare to other existing approaches like building a monolithic AI app or using workflow automation tools? If you are building AI applications, this article might help address those challenges using a modern event-driven solution with GlassFlow.

Challenges of scaling AI applications

Imagine you’ve just launched the first version of your AI application, gained your first users, and now you’re ready to take it to the next level by making it scalable to serve even more people. If you’re at this stage, congratulations! You’ve hit initial traction, but new challenges are just around the corner. These are the common issues that AI startups face as they start to grow their product:

• Latency: Sometimes, Large Language Models (LLMs) like GPT or Llama are too slow for good user experiences. Using APIs like OpenAI in production can be unpredictable because there is no exact SLA when an AI response is received. Especially, when there’s a high demand for AI models, response times can suffer, and some data might be missed entirely. The latency of LLMs like GPT-4 increases with the length of the output. Requesting a multi-paragraph response for a large document processing? You might end up waiting several minutes or even longer.
• Data processing with multiple LLMs: In real projects, you want to give context to AI from a large dataset. You also try using multiple models to reduce latency and increase accuracy. Instead of using GPT-4 in all parts of your product, you could switch to GPT-3.5 or BERT dynamically. That means creating a document-processing pipeline that depends on several AI models. However, it can lead to coordination issues, as one of the model failures might affect the others, response times can lag, and some data might even be lost, affecting accuracy.
• Logs and observability: To detect quickly latency and accuracy issues with AI models, you need to monitor continuously each model's performance in every stage. However, only a few open-source LLM frameworks really offer robust logging and observability capabilities.
• Data format validation across different data sources: When handling data from many data sources (Databases, APIs, files, etc.), each with different data structures, validating these data formats (schemas) is difficult before you feed data for AI.
• Security: Integrating public LLM providers can expose sensitive information.
• Offline testing: Testing AI applications offline can be tricky. It’s difficult to run tests on code and data output locally, as responses can vary with each request, making it hard to ensure consistent results.
• Real-time data integration: Keeping LLMs up-to-date with real-time data. Traditional batch updates can lead to outdated responses, making it hard for AI applications to stay relevant.
• Bring Your Own Cloud (BYOC): If you have enterprise customers, there is a high chance that enterprise customers want to run your AI service in their own cloud environments (BYOC). You may need to meet strict security and compliance requirements in each client’s cloud, which requires custom configurations and significant setup time. Most data orchestrating tools offer only cloud-based SaaS solutions.
• Automated onboarding of new customers: AI agent apps ideally have separate sessions and unique data processing flow for each customer. Data from Customer A should be somehow isolated from the data of Customer B. You start to set up a separate data processing pipeline for each new customer. It is hard to build them manually by creating automated scripts, it might slow down new customer onboarding.

These challenges show why scaling an AI app in the production environment is much harder than building a proof of concept or the first version of the app.

Scaling AI applications: Data ingestion pipelines and solutions

As your AI application grows and you take on more users, a key focus becomes setting up efficient data ingestion pipelines that keep your AI models updated and responsive. Here, we’ll explore three different approaches to building these pipelines for AI applications: Monolithic AI applications using APIs, Workflow automation tools, and Event-driven pipelines, highlighting their strengths and challenges in scaling.

Monolithic AI Applications

Starting with a monolithic architecture for an AI application and exposing multiple APIs to serve application needs can work well initially, especially for simpler, low-volume use cases until you onboard your first 2 or 3 customers. However, as the number scales, this approach often faces challenges. Scaling a monolithic structure usually involves replicating the entire service for every new feature, which can be inefficient and costly.

1. Scalability: Monolithic architectures often face limitations in scaling because the entire application must be replicated to handle more load. Let’s take an example of an AI-powered copilot application used to assist developers by suggesting code completions, syntax fixes, or auto-generating documentation. In a monolithic architecture, all features are within a single codebase. As user demand increases, particularly for real-time code suggestions, the app needs to handle more requests instantly. However, to scale, the entire application—including documentation generation and syntax analysis—needs to be replicated across multiple servers, even if only the code suggestion feature is in high demand.
2. Observability: Basic observability is usually available, but it can be challenging to get in-depth monitoring across all parts of the application without additional tools. When the AI chatbot’s response times slow down, there’s no way to pinpoint the exact part of the process causing the delay. Adding monitoring for specific stages like API user requests, response generation, or database access would require additional tools and customization, making troubleshooting difficult and time-consuming.
3. Code Maintainability: As more features are added, the codebase can become large and hard to manage. A monolithic app usually includes user profile management in one codebase. As the development team adds new features, like product reviews, overlapping dependencies increase, making the code harder to maintain and increasing the risk of bugs. Adding or changing features takes longer, as every change affects the entire codebase.
4. Error Handling: Error handling is limited and often requires custom solutions to manage failures, which can be challenging as the application scales. If one feature fails—like uploading a new file to the AI copilot app due to an API error—it often disrupts the entire application, requiring custom error-handling solutions to retry or reroute tasks.
5. Latency: Monolithic setups generally perform well under light loads but struggle with latency as traffic increases, particularly for AI applications needing real-time updates.
6. Time to Market: Monolithic APIs can be quick to launch for simpler use cases but may take longer to adapt and extend as the application grows. Launching an AI-based video highlights summarization tool with a monolithic API architecture is fast initially. However, when the team wants to add personalized summaries or multi-language support, integrating these features takes longer because every change impacts the main codebase. As a result, adapting to new user needs or improving the tool takes significant time, delaying updates and new releases.

Workflow automation tools

Workflow automation tools, like Apache Airflow or Zapier, are commonly used to automate complex data processing tasks. They’re ideal for managing scheduled batch processing tasks, which is helpful for batch AI workloads.

1. Scalability: Workflow automation tools scale by adding more tasks, but they aren’t designed for high-frequency, real-time updates, making them less ideal for applications that need to respond instantly.
2. Observability: These tools offer some monitoring capabilities, but observability is often limited to batch processing and scheduled jobs. They lack real-time tracking, making it difficult to detect and resolve issues quickly. For a content moderation AI solution that uses Zapier to automate workflows for processing flagged content, observability is limited to batch processing insights. While Zapier provides some tracking of scheduled jobs, it lacks real-time visibility into which parts of the workflow are delayed or failing. As a result, it’s hard to pinpoint issues immediately, meaning that flagged content could remain unreviewed longer than intended, impacting user experience and compliance.
3. Code Maintainability: At first, the workflow is manageable, but as more steps are added, dependencies grow, and any change requires manually updating multiple interconnected tasks. This makes it harder to maintain the code, especially if developers need to modify one part of the workflow without affecting the others. The rigid structure of workflows can turn into a bottleneck, slowing down development.
4. Error Handling: Workflow tools can handle errors within scheduled jobs but often require custom setups to rerun failed tasks. When an error occurs in one of the scheduled tasks—like a failed data fetch from the GoogleSheet API—the workflow stops, and the error may not be retried automatically. While Zapier allows retry logic to be configured, customizing it to rerun failed tasks in the correct order and ensure data integrity requires significant setup and monitoring, making error handling complex and manual.
5. Latency: Workflow automation tools are best suited for batch updates, introducing lag that can make it challenging for real-time AI applications.
6. Time to Market: The process might be fast using low-code capabilities offered by Zapier or similar tools. These tools help structure processes efficiently for batch tasks but can take longer to implement for real-time data needs, as workflows require manual configuration for each data format change from data sources.

Event-driven data pipelines

An event-driven data pipeline reacts to data changes, or “events,” in real time. For example, if you are building an AI search tool, you can apply an event-driven Generative AI solution pattern that uses an event-driven data processing pipeline. In this setup, the AI model receives real-time data updates through an event-driven pipeline. When a new piece of information (or event) arrives, such as an updated document or transaction in a database, it’s immediately processed and sent to the AI model.

1. Scalability: Event-driven pipelines are highly scalable, processing data as soon as an event occurs without waiting for batch schedules. Each event can be handled independently, making it ideal for high-volume, real-time applications. It doesn’t require reprocessing the entire file; instead, it tracks only the changes. When a small update is made to the document, the event-driven pipeline detects that specific change—down to the word level—streams only the updated portion of data to the AI model, and dynamically enriches the document’s information for AI use. So AI can make decisions as soon as an event occurs.
2. Observability: Event-driven architectures usually offer real-time insights across every stage of the pipeline. You can see detailed logs of each input event to monitor AI model performance easily and troubleshoot.
3. Code Maintainability: Event-driven pipelines promote a modular structure like in the microservices approach, allowing code to be easily updated or expanded as new requirements arise. For example, every component can be replaced or updated dynamically without stopping the whole pipeline. When data structure changes on data sources, you can validate data schema as it arrives, without waiting for the developer to come and fix the data schema mismatch.
4. Error Handling: With continuous error handling and retries at each stage, event-driven pipelines minimize disruptions, keeping data flowing smoothly and reducing the risk of data loss.
5. Latency: Event-driven architectures are optimized for low latency, making them suitable for AI applications to ingest real-time data, such as real-time AI searches or customer interactions with AI agents. For example, if someone searches for the “latest security policy,” the app retrieves only the most current version rather than outdated copies.
6. Time to Market: Event-driven setups enable rapid deployment without interrupting other parts of the product. If you have a monolith set up initially, you can understand the pain points of how a single bug might impact the whole system. As a result, the time needed to bring new features to market is fast from minutes to one hour with an event-driven setup. With the serverless infrastructure and easy creation of new pipelines, GlassFlow accelerates the creation process of PoC and launching new products and services.

Conclusion

With event-driven data, AI apps consume exactly the data they need, in the format they need it in, and whenever they need it. Event-driven pipelines are built with high observability and robust error handling, making them ideal for AI applications that need real-time responsiveness and continuous data flow. In contrast, monolithic architectures and workflow automation tools, while useful for simpler tasks, don’t provide the flexibility or responsiveness needed to fully leverage AI at scale.

GlassFlow is helping organizations to implement easily an event-driven data pipeline that keeps their AI applications fed with only the freshest, relevant data, even for large documents. As real-time data processing continues to evolve, we’re just beginning to tap into its potential for AI. By investing in this modern data infrastructure, AI founders and tech leaders can build the responsive, intelligent applications that today’s market demands. The future of AI is here—and it flows with real-time data. Explore other GlassFlow use cases in the GitHub examples repo.