Stop LLM bill spikes.
Automatically.
Plarix is a BYOK gateway that attributes GenAI spend per customer/feature and enforces budgets across providers.
The Problem
Context replay makes 'cheap' tokens compound.
Agent loops can burn spend silently.
No attribution: you can't see cost per customer or feature.
Provider limits aren't reliable enforcement.
The Solution
Attribution
See spend per customer/feature/route/env.
Tenant A: $412/mo; Feature 'RAG': $0.09/user/day.
Budgets + Kill-Switch
Hard caps that actually stop spend.
Cap Tenant A at $500/mo → throttle then block.
Smart Downgrade
Auto-switch models near limits.
GPT-4 → GPT-4o-mini when budget < 10%.
Alerts
Get warned before incidents.
Slack alert when spend spikes 3× in 15 min.
Ledger + Exports
Audit trail for finance/security.
Export cost by tenant; retention policies.
How It Works
Deploy Plarix in your VPC
Docker/Kubernetes deployment with your keys.
Point your app to Plarix
Update base URL + add tags (customer_id, feature, env).
Set budgets & policies
Define limits → Plarix enforces + reports.
48-hour LLM Spend Autopsy + Kill-Switch Plan
We find the leaks, design the policies, and give you an execution plan.
Deliverables:
Spend breakdown (provider/model/feature/customer)
Top 3–5 cost leaks + root cause
Budget + policy design (throttle / downgrade / block)
Integration plan + tagging schema
Exec-ready report + next steps
For teams with: >$3k/mo spend OR a bill-spike incident OR multiple providers.
Design partners: limited slots. First cohort discounted.
Early access slots available (3 teams this month)
(coming soon)
(coming soon)
(coming soon)
FAQ
By default, Plarix logs metadata (model, cost, tokens) but not prompts. It runs in your VPC (BYOK data plane), so you control what gets logged. Prompt logging is optional if you need it for debugging.
Yes. Plarix is designed to run entirely in your infrastructure (Docker/Kubernetes). You bring your own keys (BYOK) and maintain full control over data and deployment.
Currently OpenAI-compatible providers. We're expanding to Anthropic, AWS Bedrock, and local models. The architecture supports any provider with an OpenAI-compatible API.
You define the policy: throttle (rate-limit), downgrade (switch to cheaper model), or block. All enforcement is deterministic and happens in real-time at the gateway level.
Typically 1 day. You change your base URL to point to Plarix and add metadata tags (customer_id, feature, env). No major code refactoring needed.
Primary focus is enforcement and cost control. It provides observability (attribution, monitoring) and security benefits (rate limiting, budget caps), but the core value is preventing runaway spend.
Get Started
Start your free trial