OpenClaw Is Impressive. My Wallet Still Said No

OpenClaw is one of those tools that gets people excited fast. The setup looks easy, the demos are slick, and the idea of an AI assistant living inside your Telegram or WhatsApp feels like the future has finally arrived and decided to be useful. It is easy to see why people jump in quickly, and why some are even buying dedicated Mac minis just to run it full time.

I get it because I tried it too. I did not buy a Mac mini, thankfully, but I did spin up a cheap VPS and gave it a proper go. Like most people, I wanted to see whether it could take annoying tasks off my plate and become a genuinely useful part of my day. On paper, it sounded like exactly the kind of tool many of us have been waiting for.

What I found, however, was that OpenClaw is genuinely impressive while also being very good at turning token usage into a speedrun. It can get expensive much faster than most people expect, and I think part of that comes from how we have all been trained to think about AI pricing. If your main experience is consumer tools like ChatGPT, it is easy to assume AI is broadly affordable because the cost is hidden behind a subscription or occasional free usage.

But I used ChatGPT & Gemini for free!

API based agent setups are a different game. Once you plug in your own keys, every token matters, and the costs do not usually explode in one dramatic moment. They creep up quietly while you are still experimenting, and then suddenly you are staring at the billing page like it personally betrayed you. In enterprise work, token utilisation is often one of the biggest concerns for exactly this reason. The issue is rarely whether the model can do the task. The issue is whether the economics still make sense when usage starts piling up.

That is why I get more cautious anytime a tool asks for API keys, and OpenClaw does. My first run was with Claude keys, and in under two hours I had already racked up close to $30 dollars. The painful part was not just the amount, but how little I had actually done. I was mostly setting things up, troubleshooting, and testing basic tasks like checking emails and filtering older ones I could delete. In other words, I was still in the tutorial phase and somehow already paying premium tuition fees.

I disconnected the keys almost immediately. To be fair, I know Claude is not always the cheapest option for this kind of test, so I tried again using OpenRouter because I wanted model flexibility and the option to switch to cheaper models depending on the task. That felt like the responsible move. It just was not enough to solve the bigger issue.

After the usual setup, a few cron jobs, and some basic queries, I checked usage and saw roughly 4 million tokens burned in about an hour. I had barely done any heavy lifting. No complex automation chain, no giant processing workload, just normal assistant style usage and testing. At that point, the question stops being which model is cheapest and becomes why the meter is sprinting while I am still tying my shoelaces.

After digging around, the reason became pretty obvious. OpenClaw, like many agent systems, resends a lot of context on every request. That includes persona instructions, chat history, tool instructions, tool outputs, and the supporting context needed to keep the assistant coherent. From a product design perspective, that makes sense. From a cost perspective, it means you are repeatedly paying to carry the same luggage through every checkpoint.

That is why I do not think this is just a model pricing problem. It is also a systems design and usage pattern problem. The real cost driver is often how much context gets resent, how frequently background jobs run, and how the workflow is structured. A cheaper model helps, but it does not magically fix a token hungry workflow.

There is also the part demos rarely spend enough time on, which is total cost of ownership. The fun part is easy to showcase because it looks amazing. Connect messaging apps, pull calendar data, trigger automations, and watch the assistant do something clever. The less glamorous reality starts after the demo ends. You still need to think about infrastructure, setup time, troubleshooting, access management, permissions, and whether you are actually comfortable with what the assistant can see and do.

To make OpenClaw genuinely useful, you usually need to give it meaningful access to your accounts. In videos, this often looks like a few quick clicks and you are done. In real life, if you care even a little about permissions and security, it gets messy quickly. Even read only access can be more annoying than it sounds depending on the service. That is not a criticism of OpenClaw alone. It is just the reality of integrated assistants. The more useful they are, the more trust they tend to require.

Is it worth it?

Then comes the question that matters most for daily use: is it worth it? For me, not yet. I use AI heavily every day, so this is not coming from someone who is anti AI or resistant to experimentation. My current stack already works well. ChatGPT is my primary tool, Manus is a strong secondary, I use Gemini when it makes sense, and Claude or other models for specific tasks. I also use cheaper routed models when I need something fast and cost effective. From a total cost of ownership perspective, that mix is still much more efficient for me than running OpenClaw as a general purpose assistant.

I do not think the excitement around OpenClaw is fake hype. The interest is understandable, and in many ways deserved. It is ambitious, genuinely fun to experiment with, and points toward a direction many people want AI tools to go. But being impressed by a product and being ready to live with its cost profile are two very different things. Right now, OpenClaw feels a bit like adopting a dragon because the baby photos were cute. Amazing creature, questionable household economics.

Could it be optimised? Definitely. I am sure experienced users can reduce context bloat, tune jobs, and make the numbers much more reasonable. But that is also the point. If it takes real optimisation work to make it sensible, then it is not yet a straightforward fit for most everyday users.

So my view for now is simple. OpenClaw is powerful and exciting, but it is not the obvious default many people think it is. If you are a tinkerer, an engineer, or someone with a very specific high value automation in mind, it is probably worth exploring. If you are just looking for practical productivity gains, you may get better returns by tightening your workflows in more mature tools first.

I am glad I tested it, and I genuinely learned a lot from doing so. I also unplugged it before it developed expensive habits on my behalf.