Navigating the LLM Tsunami: A Day in the Life of Platform Engineering

June 24, 2024

Today marks a significant day on our platform engineering team. The AI/LLM landscape has been a wild ride since ChatGPT hit the scene, but today we’re diving headfirst into a project that’s truly reshaping how we think about infrastructure.

The LLM Tsunami

The last few months have seen an unprecedented flood of new LLMs (Large Language Models) hitting production. Every other team wants to integrate one or more of these models into their services, and the demand is overwhelming. The conversation around AI has shifted from “if” to “how.” It’s not just about whether we can use these models; it’s about how we can leverage them without breaking our existing systems.

The Latest: Anthropic Integration

Today, I’m working on integrating Anthropic into our core platform services. We’ve been using StableLM for a while now, but Anthropic has some unique features that make it a compelling choice for certain use cases. However, this integration isn’t as straightforward as we hoped. Here’s where the fun begins.

Debugging Nightmares

One of the first issues I encountered was latency. Anthropic is resource-intensive, and our existing infrastructure wasn’t prepared to handle the load. We’ve spent hours tweaking server configurations and optimizing code paths. It’s a constant balancing act between performance and robustness.

Then there are the security concerns. Anthropic requires a high level of scrutiny due to its capabilities. We had to work closely with our security team to ensure that any data processed by these models is handled securely. This involved setting up strict access controls, encrypting sensitive information, and auditing every request.

Platform Engineering vs. DevOps

As we moved deeper into the integration, I found myself in a bit of a debate with one of my devops colleagues. We were discussing how to best manage the deployment process for Anthropic models. My approach was leaning towards a more traditional platform engineering solution: version control, automated testing, and rolling updates. However, he argued that we should take a DevOps approach—rolling out changes incrementally and constantly monitoring their impact.

After a heated but constructive discussion, we settled on a hybrid model. We would keep the platform engineering practices for stability and security but adopt some of the dynamic deployment strategies used in DevOps to ensure flexibility and responsiveness.

The Cost Dance

Another challenge is the financial aspect. AI models like Anthropic are not cheap to run, especially when you start scaling them across multiple services. FinOps is a big deal here, and we’re working with our finance team to understand the cost implications of running these models in production. We’re exploring ways to optimize costs without compromising performance.

Metrics and Monitoring

To keep track of everything, we’ve been rolling out DORA metrics to monitor our deployment and operations process. It’s a constant reminder that while we can deploy fast, maintaining quality is just as important. We’re setting up dashboards to track service latency, error rates, and system stability. Every minute counts when you’re dealing with production systems.

Personal Reflections

Working on this project has been both exhilarating and exhausting. The tech landscape is constantly shifting, and it’s easy to feel like you’re always playing catch-up. But that’s part of the fun—there are always new challenges to solve, new technologies to explore.

Today, as I sit in front of my monitor, surrounded by open tabs with code snippets and logs, I’m reminded of why I love this job. It’s not just about writing lines of code; it’s about building something that impacts real people and services. The road is bumpy, but the view from the top is worth every kilo of sweat.

So here’s to another day in platform engineering—full of bugs, debates, and breakthroughs. Bring on the next challenge!

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Stay tuned for more updates as we navigate this exciting yet complex world of AI/LLM integration!