Your AI bills are too high.
Here's how to fix that.
Laghav controls your AI spend — compressing prompts, routing to the cheapest capable model, enforcing budgets, and showing you exactly where every dollar went. One endpoint change. No refactoring.
500+ developers · avg 61% cost reduction · quality score 94/100
Code example: replace anthropic.messages.create with laghav.complete, set model to auto, and access laghav_meta.saved_usd and laghav_meta.quality_score on the response.
Three ways you're wasting AI budget right now
Without prompt infrastructure, you're bleeding capital on every single call.
Expensive model for everything
Your FAQ bot runs on Claude Opus ($15/M). It should run on Haiku ($0.25/M). Laghav routes it automatically.
98% savings
Verbose prompts waste tokens
"Hey I wanted to ask..." → "Explain". Prompt compression: 62%. Quality score: 94/100. Every single call.
62% reduction
Zero visibility into AI spend
Which team spent the most? Which app has the worst ROI? Laghav answers with granular charts before your CFO asks.
Real-time dashboard
Try it right now. No sign-up.
Paste any prompt, log file, or code snippet and see compression in real time.
89% savings vs paying list price
Compression + routing together cuts your effective cost per 1M tokens from $15 to $1.65.
Cost per 1M input tokens. Routing to Haiku for eligible requests. Compression ratio 62%.
Granular visibility into every dollar
Real-time cost breakdown by app, model, team, and compression rule — so you know exactly where savings come from.
Calls Today
12,847
Tokens Saved
2.1M
Cost Saved
$284.42
Quality Avg
94/100
Hourly Calls vs Savings
Built for scale. Engineered for simplicity.
The AI control plane for your team — one endpoint, full visibility.
1. Compress
Strips filler, preamble, and duplicates. LLMLingua-2 for deep linguistic compression.
2. Route
Dynamically redirects simple requests to cheap models. FAQ → Haiku. Reasoning → Opus.
3. Cache
Serve repeat semantic queries from memory. Zero LLM cost for identical calls.
4. Score
Quality scorer gives 0-100 confidence before every response is returned.
5. Govern
Team budget caps, PII masking, audit logs, and per-app access policies.
6. Protocol
Apply consistent prompt engineering templates across all gateway calls.
Works for every AI use case
Pick your workload and see exactly how Laghav cuts costs without cutting quality.
Agent loops feed thousands of INFO lines into the context window. Laghav's log_slicer strips them, keeping only ERROR, WARN, and 2-line context. Your debugging agent stays sharp; your bill collapses.
on this pattern
Before
2024-01-15 10:00:00 INFO [heartbeat] healthy 2024-01-15 10:00:01 INFO [heartbeat] healthy ... (490 identical lines) ... 2024-01-15 10:20:00 ERROR [db] connection refused
After Laghav
2024-01-15 10:20:00 ERROR [db] connection refused
Developers love the ROI
“We were spending ₹2.4L/month on GPT-4. After one afternoon integrating Laghav, we're at ₹26K. Same quality.”
Arjun Mehta
CTO, YC-backed startup
“The quality score feature is game-changing. I can be aggressive with compression and the scorer catches when it goes too far.”
Sarah Kim
Staff Eng, Series B SaaS
“Dropped our log analysis agent's token usage by 94%. The log_slicer just works — extracts ERRORs with context and nothing else.”
Pedro Alves
ML Platform Lead
Simple, predictable pricing
Flat subscription. No per-call surprises. Saves more than it costs from day one.
Sandbox
Free
10K calls/month
Builder
₹2,999/mo
200K calls/month
Scale
₹9,999/mo
2M calls/month
Business
₹24,999/mo
15M calls/month
Works with every model & framework
Take control of your AI spend today.
Free tier · No credit card required · Live in less than 10 minutes.