CodeLab› Projects› Inference Optimizer
⚡

RouteLLM — AI Inference Optimizer

Submit any AI task and the platform automatically selects the optimal model, compresses prompts, minimizes tokens, and routes to the cheapest provider that meets quality thresholds. An intelligent efficiency layer between users and LLM APIs.

infrastructure vibe coding model routing cost optimization
01
Task Queue & Smart Router
Incoming tasks are analyzed, routed to the optimal model, and tracked with cost savings
routellm.codelab.sh/tasks
📋 Task Queue
🔀 Router
📊 Analytics
🧩 Models
⚙️ Settings
Task Queue · 47 today
Filters + Submit Task
🧑‍💻

Generate REST API endpoints for user auth module

Code generation · complexity: high · 2,840 tokens

Claude Opus
$0.042
↓ 38% saved
Done
📝

Summarize 12-page research paper on transformer efficiency

Summarization · complexity: low · 890 tokens

Claude Haiku
$0.001
↓ 92% saved
Done
👁️

Extract table data from scanned invoice PDF

Vision + extraction · complexity: medium · 1,560 tokens

Claude Sonnet
$0.008
↓ 71% saved
Done
🧠

Multi-step math proof: convergence of infinite series

Reasoning · complexity: very high · 4,120 tokens

Claude Opus
$0.061
↓ 24% saved
Running
02
Optimization Engine
Task analysis, prompt compression, model selection rationale, and cost comparison
routellm.codelab.sh/router/task-0847
📋 Task Queue
🔀 Router
📊 Analytics
🧩 Models
Task #0847 · Routing Decision
Re-routeView Output
Task Analysis auto-detected
Task Type Code Generation
Complexity High (0.82)
Reasoning Depth Multi-step
Input Tokens 4,580
Compressed To 2,840 (↓38%)
Est. Output ~1,200 tokens
Selected Model Claude Opus
Confidence 96%
Optimization Pipeline 5 / 6 applied
1. Prompt Compression Applied
Removed redundant instructions, deduplicated context. 4,580 → 2,840 tokens (38% reduction).
2. Input Restructuring Applied
Converted free-text spec into structured JSON schema for clearer model comprehension.
3. Model Downgrade Check Skipped
Complexity 0.82 exceeds Sonnet threshold (0.65). Opus required for multi-step code gen.
4. Token Budget Cap Applied
max_tokens set to 1,500 (est. 1,200 + 25% buffer) to prevent runaway generation.
$0.068
Naïve Cost
$0.042
Optimized Cost
03
Cost Analytics Dashboard
Track savings, model distribution, and token efficiency across all tasks
routellm.codelab.sh/analytics
📋 Task Queue
🔀 Router
📊 Analytics
🧩 Models
Cost Analytics · Last 30 days
This MonthExport CSV
1,284
Tasks Routed
$127
Total Saved
64%
Avg Savings
41%
Token Reduction
Cost Trend (Naïve vs Optimized)
$0.05 $0.15
Feb 9 --- Naïve — Optimized Mar 9
Model Distribution
Claude Haiku
52%
Claude Sonnet
28%
Claude Opus
12%
GPT-4o
8%
52% of tasks routed to Haiku — cheap model handles majority of simple tasks