Live Demo
Watch how we find per-request savings from 25 lines of code.
1import openai23client = openai.OpenAI()45def analyze_document(doc: str) -> dict:6 # Full document sent as context every call7 response = client.chat.completions.create(8 model="gpt-4o",9 messages=[10 {"role": "system", "content": SYSTEM_PROMPT},11 {"role": "user", "content": doc} # 12K tokens avg12 ],13 temperature=0.7,14 )15 return response.choices[0].message.content1617def summarize_batch(documents: list[str]):18 results = []19 for doc in documents:20 # No caching — identical docs re-analyzed21 results.append(analyze_document(doc))22 return results
The Problem
Average code waste in LLM integrations
Average ROI within first month
Average time to first actionable insight
Saved in AI costs by 2,400+ developers
The Platform
Continuous cost monitoring
Tree-sitter powered AST parsing across Python, TypeScript, Go, Java, and Ruby. Detects every LLM call — even inside helper functions and async wrappers.
See your actual AI spend broken down by model, endpoint, and call site. No more surprise invoices — know exactly where every dollar goes.
Get actionable diffs you can apply in one click. Claude Code-compatible markdown reports with exact code changes and estimated savings per fix.
Connect once via GitHub or proxy and get continuous monitoring. Catches new inefficiencies before they compound into large bills.
How It Works
Four steps. Five AI agents. Zero setup. Just connect and go.
Paste code directly, upload a ZIP archive, connect your GitHub repo via OAuth, or point our OpenAI-compatible proxy at your existing API calls. Supports Python, TypeScript, JavaScript, Go, and 200+ languages out of the box. Zero configuration required — just connect and go.
Five specialist AI agents scan your codebase in parallel — Token Optimizer, Model Selector, Cache Analyzer, Architecture Reviewer, and Cost Projector. Each agent uses tree-sitter AST parsing, pattern matching, and a RAG knowledge base of 130+ optimization strategies to find every wasted token.
Get a detailed report with exact dollar savings per finding, before/after code diffs, and a Claude Code-compatible markdown patch you can apply with one command. Download a branded PDF for stakeholders, or feed the markdown directly to your AI coding assistant to auto-apply every fix.
Stay connected for continuous scanning. Every new commit gets checked automatically via GitHub Actions or our SDK. Set budget alerts with Slack/email notifications. Cost regressions surface before they hit your invoice — so your AI spend never spirals out of control.
Social Proof
“I was mass-feeding files to Claude and blowing through context windows. erabot showed me I was sending 3x more tokens than needed. Cut my API bill from $180/mo to $47.”
“We had no idea our RAG pipeline was re-embedding unchanged documents on every deploy. The scan caught it instantly — saved us $2,400/mo in OpenAI embeddings alone.”
“Rolled erabot out to 12 teams. The auto-fix patches cut our Claude API spend by 38% in week one. The exec dashboard finally gives leadership the visibility they wanted.”
“At our scale, even a 10% reduction is six figures annually. erabot found redundant context stuffing across 200+ services we never would have caught manually.”
Built For
Stop bleeding money on personal AI projects. See exactly where your tokens go and slash your API bill without sacrificing output quality.
Move fast without burning your runway on AI costs. Get the same output for a fraction of the spend.
Bring visibility and governance to distributed AI usage across hundreds of services and teams.
Integrate directly into CI/CD. Scan every PR automatically and block regressions before they ship.
Make data-driven decisions on AI provider selection, model choice, and optimization ROI.
Integrations
Drop erabot.ai into any AI stack — zero migration required.
Pricing
No hidden fees. Cancel anytime. Your savings pay for the plan.
Explore what erabot.ai can find
For individual developers serious about costs
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