A single architectural layer eliminates up to 68% of your LLM token spend without touching a single model.
Average annual LLM spend for a Series-B company running GPT-4o at scale
Of prompt tokens are redundant context — repeated, cached-but-billed, or compressible
What you should pay for tokens that never needed to reach the model
Every API call ships your entire conversation history. At 128K context windows, you're paying for kilobytes of noise on every single request.
Your users ask the same questions rephrased. Without vector-aware caching, you pay OpenAI full price — every time — for identical intent.
Provider dashboards show spend, not waste. You have no visibility into which tokens are earning their cost.
Your user growth directly multiplies your LLM bill — a linear trap with no ceiling until you fix the architecture.
Three things happen between your app and OpenAI. Each one saves you money. Here's the plain-English version.
Your app sends a massive prompt full of context, past chat history, and redundant code. Every conversation turn balloons the payload further. This is what OpenAI bills you for.
Kinetic catches the prompt on your server. If a user has asked this (or something semantically identical) before, the local Redis cache serves the answer instantly. Cost: $0. Latency: ~15ms.
If it's a new question, our local AI strips out the filler words and redundant fluff, sending a tiny "skeleton" prompt to OpenAI. You get the exact same answer, but OpenAI bills you for 70% fewer tokens.
Your token data and API keys never leave your infrastructure. Zero telemetry. Zero external servers. Complete VPC isolation.
Every routing decision, cache threshold, and compression limit is yours to configure. No black box. No trust required.
Same application. Same users. Same quality responses. Radically different bill. Here's the before and after on a real-world RAG workload.
Kinetic Gateway is OpenAI SDK-compatible. You change a base URL. That's it. No refactoring. No new abstractions. Ships in an afternoon.
Engineers trust code, not marketing. Review the exact environment variables and deployment scripts you will receive.
If the container fails to deploy in your environment, or fails to measurably reduce your OpenAI inference bill within the first 30 days, we will refund your perpetual license in full. No questions asked.
No subscriptions. No per-seat tax. No usage fees. You buy it once, you own it forever.
Docker image + Terraform module included. Up in under an hour.
You pay your cloud provider directly. We take nothing after day one.
Your prompts never touch our infrastructure. BYOC by architecture.
Private GitHub repo access. You own it. Modify it. Extend it.
Live integration review with the founding engineer. Not a support ticket.
Straight answers to the questions your engineering team will ask.
No. This is a one-time, perpetual developer license. You own the deployment and pay zero recurring fees to us.
Cache hits return in ~15ms, completely eliminating the standard 1-3 second OpenAI generation delay. Cache misses add negligible overhead (<5ms) to the routing process.
Any model compatible with the OpenAI API specification, including Anthropic, open-source models via vLLM, and local deployments.
Your license includes a live, 90-minute technical onboarding call where I personally help your engineering team deploy the container into your staging environment.