👋 Hey devs — Looking for feedback on my AI cost-optimization + “AI Slop Prevention” tool
I'm Zach, and I’ve been building AI features for a while now.
Like many of you, I started noticing the same painful problems every time I shipped anything that used LLMs.
💸 The problem (from a developer’s perspective)
AI bills get out of control fast.
Even if you log usage, you still can't answer:
“Which model is burning money?”
“Why did this prompt suddenly cost 10× more?”
“Is this output identical to something we already generated?”
“Should this request even go to GPT-4, or would Groq/Claude suffice?”
“Why did the LLM produce 3,000 tokens of slop when I asked for 200?”
“How do I give my team access without accidentally giving them access to ruin my budget?”
And then there’s AI Slop —
unnecessary tokens, verbose responses, hallucinated filler text, or redundant reasoning chains that waste tokens without adding value.
Most teams have no defense against it.
I got tired of fighting this manually, so I started building something small…
and it turned into a real product.
🚀 Introducing PricePrompter Cloud
A lightweight proxy + devtool that optimizes AI cost, reduces token waste, and prevents AI slop — without changing how you code.
You keep your existing OpenAI/Anthropic calls.
We handle the optimization layer behind the scenes.
🔧 What it does
1️⃣ Smart Routing (UCG Engine)
Send your AI request to PricePrompter →
we send it to the cheapest model that satisfies your quality requirements.
GPT-4 → Claude-Sonnet if equivalent
GPT-3.5 style → Groq if faster/cheaper
Or stay on your preferred model with cost warnings
Your code stays unchanged.
2️⃣ FREE Semantic Caching
We automatically store/recognize semantically similar requests and return cached results when safe.
You get real observability:
Cache hits
Cache misses
Percentage matched
Total savings
Caching will always remain free.
3️⃣ AI Slop Prevention Engine
This is one of the features I’m most excited about.
We detect:
Overlong responses
Repeated sections
Chain-of-thought that isn’t needed
Redundant reasoning
Token inflation
Hallucinated filler
And we trim, constrain, or guide the LLM to reduce token waste before the request hits your billing.
Think of it as:
“Linting for LLM calls.”
4️⃣ Developer Tools (Cursor-style SDK)
A VS Code extension + SDK that gives you:
Cost per request (live)
Alternative model suggestions
Token breakdown
“Why this request was expensive” explanation
Model routing logs
Usage analytics directly in your editor
No need to open dashboards unless you want deeper insights.
5️⃣ Team & Enterprise Governance
Practical controls for growing teams:
Spending limits
Model-level permissions
Approval for high-cost requests
PII masking
Key rotation
Audit logs
Team-level reporting
Nothing enterprise-y in a bad way — just the stuff dev teams actually need.
🎯 Who this is for
Developers building LLM features
SaaS teams using expensive models
Startups struggling with unpredictable OpenAI bills
Agencies running multi-client workloads
Anyone experimenting with multi-model routing
Anyone who wants visibility into token usage
Anyone tired of “AI slop” blowing up their costs
💬 What I’m looking for:
I’d love real feedback from developers:
Would you trust a proxy that optimizes your LLM cost?
Is AI slop prevention actually useful in your workflow?
Is free semantic caching valuable?
What would make this a must-have devtool?
What pricing model makes sense for you?
Any dealbreakers or concerns?
Still shaping the MVP — so your input directly influences what gets built next.
Happy to answer questions or share a preview.
Thanks dev.to! 🙌
— Zach
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