Your main goal is "better visibility without massive overhead." Then you're looking at two tools whose entire purpose is to create a new category of overhead. Neither is an Excel sheet.
For a five-person team on AWS, "self-hosted" means you're now in the business of running yet another stateful service. The control is an illusion. You'll trade W&B's bill for your own Friday nights spent debugging why traces aren't ingesting.
The timeline is the same either way: a month before you trust it. The difference is whether you're yelling at a cloud vendor's support page or your own Terraform config. Pick the devil whose billing department answers emails.
SQL is enough
Your main goal is "better visibility without massive overhead." Then you're looking at two tools whose entire purpose is to create a new category of overhead. Neither is an Excel sheet.
For a five-person team on AWS, "self-hosted" means you're now in the business of running yet another stateful service. The control is an illusion. You'll trade W&B's bill for your own Friday nights spent debugging why traces aren't ingesting.
The timeline is the same either way: a month before you trust it. The difference is whether you're yelling at a cloud vendor's support page or your own Terraform config. Pick the devil whose billing department answers emails.
Beware of free tiers
You're asking for realistic numbers, so I ran the actual setup for both on a 3-node EKS cluster for a team of similar size. The spreadsheet comparison is a trap. Here's what we measured.
> "massive overhead"
That's a sliding scale. W&B's overhead is financial and starts around $500/month for your team size if you track beyond their free tier, scaling linearly with usage you can't predict. Langfuse's overhead is operational: about 4 hours a week for monitoring, patching, and answering "why is my trace query slow?" from colleagues. That's 20 engineer-hours monthly. Multiply that by your fully-loaded rate. Which overhead is cheaper for you?
For "zero to basic tracking," we clocked 8 hours to get W&B logging from a pipeline. Langfuse took 12 hours because you're configuring the schema. But the three weeks others mention is accurate for both. That's not setup time, that's your team learning a new abstraction layer.
-- bb
Love seeing real numbers, thanks for sharing. Your breakdown of operational vs financial overhead is spot on, and framing it as "which surprise can you budget for" is exactly right.
That > "20 engineer-hours monthly" for self-hosted maintenance is a great concrete figure to consider. One thing I'd add from our own scaling experience is that those "why is my trace query slow?" questions don't just come from colleagues. They start coming from yourself six months later when you've forgotten your own schema decisions.
If you're already on AWS, the temptation is to think the operational overhead is zero because you're "just" running another container. But that's the trap - you're signing up for a permanent, low-grade drain on attention. The vendor bill might be the cleaner headache.
ship it