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AI Infrastructure & Token Cost Reduction

Cost-Controlled Open-Weights Inference Models

Reduce proprietary LLM token expenses by serving open-weights foundation models on infrastructure you control, at a quality and latency bar you set.

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Reduce reliance on proprietary, per-token LLM pricing by serving open-weights foundation models on infrastructure you control — your own cloud VPC or on-premises hardware, under your own access controls — rather than a third-party API. Actual cost savings depend on your workload volume, model size, and achievable GPU utilization; we size this with you rather than quote a blanket percentage.

AI inference routing diagram: a client request passes through a request router to vLLM or TGI serving, which draws on a GPU autoscaling pool and feeds cost and quality monitoring.
Illustrative serving path — actual GPU sizing and quantization choices are workload-specific.

Who this is for

Teams with high enough, steady enough LLM usage that per-token API pricing has become a meaningful line item, and who have (or are willing to build) the operational capacity to run inference infrastructure rather than call an API.

Expected outcomes

A deployed, right-sized inference service — not just “a model running somewhere” — with GPU autoscaling matched to real traffic, quantization decisions that are explicit trade-offs against measured output quality (not just applied by default), and clear visibility into cost per request versus your previous API spend.

Scope & deliverables

We deploy and tune serving infrastructure (vLLM, TGI) on Kubernetes with GPU autoscaling, evaluate quantization options against your actual quality bar, and build token/request-level cost tracking so the economics are visible, not assumed. We’ll also tell you when a workload is a poor fit for self-hosting — low, spiky volume often doesn’t clear the break-even point against a managed API.

Our approach

We start by establishing your current usage pattern and baseline cost, evaluate candidate open-weights models against your quality and latency requirements, and run a cost/quality comparison before committing to a production deployment. We size GPU capacity and autoscaling policy against that evaluation, not a generic reference architecture.

Client responsibilities & exclusions

You’ll need to provide representative traffic and example prompts/outputs so quality evaluation reflects your actual use case, and sign off on the quality bar before production cutover. This engagement covers model serving infrastructure; licensing review for the specific model(s) you choose to deploy is your responsibility, and we’ll flag licensing terms we notice but won’t provide legal advice on them.

Related: Kubernetes & CNCF Ecosystem, Cloud FinOps & Cost Optimization

FAQs

Will this definitely be cheaper than our current API costs? It depends on your volume and utilization — we'll model this with your real numbers before you commit, rather than promise a fixed percentage.

Which workloads are a bad fit for this? Low-volume, highly bursty workloads, and use cases that need the absolute largest frontier models' quality, where self-hosting an open-weights alternative may not clear the bar.

Key Deliverables

  • vLLM and TGI inference deployment
  • GPU autoscaling on Kubernetes
  • Quantized model serving
  • Token usage optimization

Ready to talk architecture?

Request a technical discovery call with our engineering team.

Request Discovery Call