So you've seen my real-world testing results from those three platforms above. Now let me break down exactly how these assholes designed their pricing to fuck you over.
I've burned through $14k in three months testing AI agent platforms. Here's what nobody tells you about how these dickheads price their services.
Pay-Per-Token Is A Trap For The Unwary
OpenAI's pricing looks innocent enough - $0.03 per 1,000 input tokens, $0.06 for output tokens. Sounds cheap until your agent starts having conversations with itself and runs up $3,200 over the weekend. I learned this the hard way when our CrewAI agents got stuck in a recursive loop discussing task assignments. Ran all weekend, maybe longer - I stopped checking after it hit 200k tokens debating whether to send a fucking customer satisfaction email. Woke up Monday to our CFO asking why we burned through our quarterly budget in two days.
The "free tier" bullshit is even worse. LangSmith gives you 10,000 free traces, which sounds generous until you realize a single agent conversation can burn through 50+ traces. We hit the limit in day two of testing. Now we're paying $39/month per developer plus $0.50 per thousand traces. Do the math - that's $200+/month for a team of three once you factor in actual usage.
Open Source Isn't Free (And Neither Is Your Time)
Everyone says "just use AutoGen or CrewAI - they're open source!" These people have never debugged a multi-agent conversation at 3am when everything's on fire and your slack is blowing up.
Sure, the code is free. But you'll spend like $2k/month on AWS hosting, another $500 or so on monitoring with Weights & Biases, and I was basically working weekends just keeping the damn thing running. I calculated our "free" open source setup costs somewhere around $8k/month in hidden expenses.
The Haystack documentation claims you can self-host easily. That's a lie. Their Docker setup breaks on ARM machines, their vector store integration has memory leaks, and their community forum is full of unanswered questions about production deployments.
Token Optimization Or How I Stopped Worrying And Learned To Love GPT-4 Mini
Here's what actually works: Use GPT-4 Mini for 90% of tasks and save GPT-4 for the complex shit. Our token costs dropped 80% and users can't tell the difference. Took me $8k in overages to figure this out.
Context window management is where most people fuck up. Don't let your agents maintain massive conversation histories - ours hit like 50k tokens discussing whether to send an email. Implement summarization strategies or your bill will bankrupt your startup. We learned this when our LangGraph conversation agent accumulated maybe 200k tokens discussing a simple customer refund. A fucking customer refund.
Pro tip: CrewAI's hierarchical agents actually work for cost reduction. Unlike the marketing claims, this one delivers. We cut redundant API calls by 60% using their crew structure instead of independent agents.
The LangChain caching everyone raves about? It works, but only if you configure Redis properly. Their default settings will eat your RAM and crash your app. Read the production deployment guide and actually follow it, unlike us.
