Hi everyone! Still pretty new to the DevOps side of things, but my team is starting to use both GPT-4 and Gemini for different tasks. We need to start monitoring costs, latency, and errors for these API calls.
I've heard of Helicone and Datadog, but I'm not sure which is a better fit for our use case. We're a small team, so ease of setup and cost are big factors for us. Datadog seems powerful but maybe overkill? And I'm not sure how well it handles multiple LLM providers together.
Has anyone set up monitoring for a similar multi-LLM setup? Would love to hear about your experience, especially if you've used both tools. Any gotchas or things you wish you knew earlier? 😅
Helicone is the obvious choice for your use case. Datadog is enterprise-grade monitoring - you'll pay for infrastructure, log ingestion, and custom metrics before you even get useful LLM data. Helicone is purpose-built for this: it proxies your API calls, so you get cost, latency, and error tracking out of the box for both GPT-4 and Gemini without any extra instrumentation.
Setup is dead simple - you just change your base URL and add a header. Datadog would require you to manually tag each request, write custom dashboards, and probably still miss the provider-specific cost breakdowns.
The only gotcha with Helicone is that you're adding a proxy layer, which introduces a tiny latency overhead. For most teams it's negligible (a few ms), but if you're doing high-throughput real-time streaming, you might want to test it first. Also, Helicone's free tier is generous enough for a small team, while Datadog's free tier is basically useless.
What's your monthly call volume?
Data over opinions
The latency overhead isn't always a few ms. It's entirely dependent on your chosen Helicone region and your own infrastructure's location. I've seen it add 50-100ms for calls routed cross-continent, which can be significant for interactive applications.
Their cost breakdown for Gemini is also less granular than for OpenAI. You get a total, but the model-specific attribution can be fuzzy if you're switching between Gemini Pro and Ultra.
Still, the setup argument is correct. Instrumenting this manually in Datadog is a tedious pain.
Your fancy demo doesn't scale.