Vishal VeeraReddy

Posted on Dec 31

I Slashed My AI Coding Bills by 65% With This One Weird Trick.

#programming #ai #openai #opensource

The Problem Every Dev Using AI Assistants Faces.You know that moment when you're using Claude Code CLI, crushing it with AI-powered coding, and then you check your Anthropic bill at the end of the month?
Yeah. $347 for me last month. 😱
And here's the kicker: 65% of my requests were literally just "write a hello world function" or "explain this error message" - stuff that could easily run on my laptop.
I was paying premium API rates for queries that a local 7B model could handle in 300ms.
So I did what any reasonable developer would do: I spent a weekend building a solution that now saves me hundreds of dollars monthly.
Meet Lynkr: The Claude Code "Jailbreak" Nobody Asked For
Lynkr is a self-hosted proxy that sits between Claude Code CLI and... well, literally any LLM backend you want.
Databricks? ✅
Azure? ✅
OpenRouter with 100+ models? ✅
Local Ollama models that cost $0 per request? ✅✅✅
llama.cpp with your own GGUF quantized models? ✅✅✅✅
But here's where it gets interesting...
The 3-Tier Routing System That Changed Everything
Instead of sending every single request to expensive cloud APIs, Lynkr automatically routes based on complexity:

🏎️

Tier 1: Local/Free (0-2 tools needed)

Ollama or llama.cpp running on your machine
Response time: 100-500ms
Cost: $0.00
Handles: "explain this code", "write a function", "fix this bug"

💰 Tier 2: Mid-Tier Cloud (3-14 tools)

OpenRouter with GPT-4o-mini ($0.15 per 1M tokens)
Response time: 300-1500ms
Cost: ~$0.0002 per request
Handles: Multi-file refactoring, moderate complexity

🏢 Tier 3: Enterprise (15+ tools)

Databricks or Azure Anthropic (Claude Opus/Sonnet)
Response time: 500-2500ms
Cost: Standard API rates
Handles: Complex analysis, heavy workflows

The proxy automatically decides which tier to use. No configuration. No manual routing. It just works.
The Results Speak For Themselves

Here's what happened after I switched:

Metric	Before Lynkr	After Lynkr	Improvement
Avg Response Time	1500-2500ms	400-800ms	70% faster
Monthly API Bill	$347	$122	65% cheaper
Local Request %	0%	68%	$0 cost on 68% of requests
Downtime Impact	100% blocked	0% (fallback works)	∞% more reliable

That's not a typo. I'm getting 70% faster responses while spending 65% less money.
Automatic Fallback = Zero Downtime

The killer feature nobody talks about: if your local Ollama server crashes (mine does, frequently), Lynkr automatically falls back to the next tier.

Request → Try Ollama → [Connection Refused]
       → Try OpenRouter → [Rate Limited]  
       → Try Databricks → ✅ Success

MCP Server Integration (Because Why Not)

Want to integrate GitHub, Jira, Slack, or literally any other tool via Model Context Protocol?
Just drop a manifest file in ~/.claude/mcp and Lynkr automatically:

Discovers it
Launches the MCP server
Exposes the tools to your AI assistant
Sandboxes it in Docker (optional but recommended)

Production-Ready From Day One

I learned from my mistakes. This isn't a weekend hack held together with duct tape:

✅ Circuit breakers (no cascading failures)
✅ Load shedding (503s when overloaded, not crashes)
✅ Prometheus metrics api(because you can't improve what you don't measure)
✅ Kubernetes health checks (liveness + readiness probes)
✅ Graceful shutdown (zero-downtime deployments)
✅ Request ID correlation (debug production issues in seconds)

Quick Install (curl)

curl -fsSL https://raw.githubusercontent.com/vishalveerareddy123/Lynkr/main/install.sh | bash

For .env

Template 1: Databricks Only (Simple)
bash# .env
MODEL_PROVIDER=databricks
DATABRICKS_API_BASE=https://your-workspace.cloud.databricks.com
DATABRICKS_API_KEY=dapi1234567890abcdef
DATABRICKS_ENDPOINT_PATH=/serving-endpoints/databricks-claude-sonnet-4-5/invocations

PORT=8080
WORKSPACE_ROOT=/path/to/your/project
PROMPT_CACHE_ENABLED=true

Template 2: Ollama Only (100% Local)
bash# .env
MODEL_PROVIDER=ollama
OLLAMA_ENDPOINT=http://localhost:11434
OLLAMA_MODEL=qwen2.5-coder:latest
OLLAMA_TIMEOUT_MS=120000

PORT=8080
WORKSPACE_ROOT=/path/to/your/project
PROMPT_CACHE_ENABLED=true

Template 3: Hybrid Routing (Cost Optimized)
bash# .env
MODEL_PROVIDER=databricks
PREFER_OLLAMA=true
FALLBACK_ENABLED=true

# Ollama (Free Tier)
OLLAMA_ENDPOINT=http://localhost:11434
OLLAMA_MODEL=qwen2.5-coder:latest
OLLAMA_MAX_TOOLS_FOR_ROUTING=3

# OpenRouter (Mid Tier)
OPENROUTER_API_KEY=sk-or-v1-your-key-here
OPENROUTER_MODEL=openai/gpt-4o-mini
OPENROUTER_MAX_TOOLS_FOR_ROUTING=15

# Databricks (Heavy Tier)
DATABRICKS_API_BASE=https://your-workspace.cloud.databricks.com
DATABRICKS_API_KEY=dapi1234567890abcdef

PORT=8080
WORKSPACE_ROOT=/path/to/your/project

That's it. You're now running Claude Code CLI with:

Real-World Use Cases (AKA "Will This Actually Help Me?")

For Indie Developers

Use free Ollama models for 90% of your work. Only pay for complex tasks. Your $347/month bill becomes $35/month.

For Enterprise Teams

Route simple queries to on-premise llama.cpp servers. Complex queries go to your Databricks workspace. Data never leaves your network for simple requests.

For AI Researchers

Test your own fine-tuned models with Claude Code CLI. Compare them side-by-side with GPT-4, Claude, Gemini via OpenRouter.

For Privacy-Conscious Devs

Run Ollama or llama.cpp locally. Code never leaves your machine unless you explicitly need cloud capabilities.

The Part Where I Show You The Code

Okay fine, here's how the hybrid routing actually works under the hood:

javascript// Simplified version - actual code has more checks
async function routeRequest(request) {
  const toolCount = request.tools?.length || 0;

  // Tier 1: Local/Free (0-2 tools)
  if (toolCount <= 2 && config.PREFER_OLLAMA) {
    try {
      return await ollamaClient.send(request);
    } catch (err) {
      logger.warn('Ollama failed, falling back to cloud');
      // Fallback to next tier...
    }
  }

  // Tier 2: Mid-Tier (3-14 tools)
  if (toolCount <= 14 && config.OPENROUTER_API_KEY) {
    try {
      return await openRouterClient.send(request);
    } catch (err) {
      logger.warn('OpenRouter failed, falling back to Databricks');
      // Fallback to next tier...
    }
  }

  // Tier 3: Enterprise (15+ tools)
  return await databricksClient.send(request);
}

The circuit breaker wraps each client, so after 5 consecutive failures, requests fail fast (100ms instead of 30s timeout).

Models That Actually Work Well

Through extensive testing, here's what actually performs:

For Ollama (Local):

qwen2.5-coder:7b - Best for code generation
llama3.1:8b - Best for general tasks
mistral:7b - Fastest responses

For OpenRouter (Mid-Tier):

openai/gpt-5.1 - Best value ($0.15/1M tokens)
meta-llama/llama-3.1-8b-instruct:free - Actually free (rate limited)

For llama.cpp (Maximum Control):

Any GGUF model works
I use Qwen2.5-Coder-7B-Instruct-Q5_K_M.gguf
Point to your llama.cpp server's OpenAI-compatible endpoint

The Catches (Because Nothing's Perfect)

Ollama doesn't support all Claude features

No extended thinking mode
No prompt caching (Lynkr adds its own though)
Tool calling works but varies by model

You need to run local inference

Ollama = ~8GB RAM for 7B models
llama.cpp = ~6GB RAM with quantization
Not great for 4GB laptops

Initial setup requires some config

Environment variables for API keys
Workspace paths
Model selection

But the wizard handles 90% of this automatically.

Get Started Now

GitHub: https://github.com/Fast-Editor/Lynkr
Docs: fast-editor.github.io/Lynkr/
npm: npm install -g lynkr
Apache licensed. PRs welcome. Built with Node.js, SQLite, and determination.

The Future Roadmap

Things I'm working on:

[ ] Response caching layer (Redis-backed)
[ ] Per-file diff comments (like Claude's review UX)
[ ] Better LSP integration for more languages
[ ] Claude Skills compatibility layer
[ ] Historical metrics dashboard

Final Thoughts

Look, I'm not saying Anthropic's hosted service is bad. It's excellent. But for developers who want:

Control over their infrastructure
Cost optimization
Privacy for simple queries
Custom model integration

Lynkr gives you all of that while keeping the Claude Code CLI experience you already love.

Try it for a week. Track your costs. I bet you'll see similar savings.

And if you don't? Well, it's open source. Make it better and send a PR. 😉

Questions? Comments? Roasts? Drop them below. I'll answer everything except "why did you waste a weekend on this" (because I saved $225 already).

⭐ Star the repo if you found this useful: https://github.com/Fast-Editor/Lynkr

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