Let's be honest upfront: you probably don't need OptixLog.
There, I said it. And before our investors read this and panic, let me explain why this might be the most important thing we tell you.
You don’t need Optixlog IF…
You’re running one-off experiments.
If you are a student working on a semester project or running a handful of experiments for a paper, you probably don’t need us. A well-organized folder structure and a spreadsheet will serve you just fine. Seriously.
The overhead of learning a new tool, setting up integrations, and changing your workflow isn’t worth it for 5-10 experiments. Use what you know.
Your experiments are perfectly reproducible every time
If you can honestly say "I can reproduce any experiment from 6 months ago in under 10 minutes," you've already solved the problem we're trying to solve. You have incredible discipline, detailed note-taking, or a very simple experimental setup.
Keep doing what you're doing. It's working!
You never need to compare experiments
If you run experiments in isolation and never need to ask "which hyperparameters worked best?" or "how does model A compare to model B on dataset X?", then experiment tracking is overkill.
Not every research project requires systematic comparison. Some are exploratory. Some are proof-of-concepts. Some are just for learning.
You Work Alone and Always Will
Solo researchers with complete context of their own work can get by with minimal tooling. Your brain is the database. You remember why you made certain decisions, what parameters you tried, and what failed.
If that's your reality and it's sustainable, more power to you.
You don’t care about AI-assisted analysis
I’ve met many people who do not trust AI within engineering. And I get that, I totally do. The AI we use today are generic LLMs trying to pick bits and pieces from the little information you give them and expect them to do wonders. It just doesn’t happen.
BUT if you have zero interest in letting Optixlog to create knowledge graphs out of your data, and create a new set of eyes trying to find patterns, analyses, and errors across your experiments and be your 24/7 data companion, we might not be the right fit.
So wait, who DOES need Optixlog?
I believe (and if I’m wrong I would love to learn) you need Optixlog when
Your experiments multiply faster than your memory
Three experiments become thirty. Thirty become three hundred. Suddenly you can't remember if you tried the grating width at 0.5 with height = 0.7 or if that was the run that crashed.
You start collaborating
The moment you have a teammate asking "what were the settings for the good model?" or a PI requesting "show me all experiments from the past month," your spreadsheet breaks down.
You want AI to work for you, not against you.
Imagine asking "what were my best performing experiments with dropout > 0.3?" or "show me all failed runs with OOM errors and suggest parameter adjustments."
AI can do this, but only if your data is structured, searchable, and queryable. Random folders and inconsistent naming schemes don't cut it. Even if you're not using AI today, having your experiments properly logged means you're ready when you are.
The researchers who will benefit most from AI in the next few years aren't the ones with the most data, they're the ones with the most organized data.
Your experiments run overnight (or longer)
We have all done experiments taking 5+ hrs, leaving them overnight and coming back in the morning to check on results. Every failed run, costs real time and pushes tapeout further. You need to know immediately if something’s wrong, and you need to learn from what worked without running everything.
You're iterating on the same problem long-term.
Six months in, you'll try something you tested in week 2 and forgot about. Without systematic tracking, you're repeating failures and rediscovering solutions.
The Real Cost Isn't OptixLog
The real cost is: