Cut eligible AI API spend up to 90% and run agents, coding tools, image/video generation, and other suitable workloads on private GPU throughput.
LighterHub
Enterprise AI API cost optimization

Reduce AI API spend up to 90% without giving up privacy or model choice.

LighterHub analyzes your workload, benchmarks suitable open-source and open-weight model families, keeps inference private by default, and recommends a path for AI agents, coding assistants, image/video generation, or other GPU-backed workflows.

1Up to 90% savings 2Private inference by default 3Agents, code, media 4Configurable policy profiles
Free first pass

Submit your workload for a fit review.

LighterHub reviews savings potential, privacy constraints, model requirements, quality bar, workload type, and traffic profile before recommending an inference path.

You get a savings range, privacy-fit read, candidate model family, and first benchmark plan.

Email instead
1. Fit read: whether the workload is worth benchmarking. 2. Benchmark path: what to test, what stays premium, and fallback rules. 3. Written quote if fit: quote-only after review, no commitment from this form.

This starts a workload and model-fit assessment. It does not create a purchase or capacity commitment.

No secrets or private customer data. Payloads are processed for inference, not training.

How it works

A clear path from workload review to private GPU inference.

Visitors should not have to decode infrastructure. LighterHub turns the decision into a short sequence: submit the workload, identify what can move, benchmark candidate routes, define policy boundaries, then quote only if the fit is real.

1 Workload intake Provider, spend, traffic shape, quality bar, privacy needs, and policy constraints.
2 Fit review Separate repeatable calls from tasks that should stay on a premium route.
3 Benchmark plan Pick candidate open-weight models, eval prompts, latency targets, and fallback rules.
4 Privacy + policy Define payload handling, system prompts, moderation layers, and operational metadata.
5 Quote if fit Written scope for reserved throughput, model route, support expectations, and launch path.
Clients

Apps, Cursor/Cline-style tools, agents, media jobs, embeddings, and batch workflows.

API

OpenAI-compatible route so suitable clients do not need a custom integration.

Control

Policy profile, routing, fallback, usage evidence, and privacy defaults.

Capacity

Private GPU throughput for benchmarked workloads with written scope after review.

The diagram is the sales motion: review first, benchmark second, quote only when the workload, model, privacy, and policy requirements fit.

Use cases

Your GPU lane can support more than chat.

The 90% number is not universal. The strongest fit is repeatable, high-volume work where quality can be tested against representative prompts, tool calls, media requests, or eval sets.

AI agents
Hermes Agent logoHermes Agent OpenClaw logoOpenClaw

Agent backends and tool loops

Run suitable agent backends, tool-calling workflows, repo automation, and multi-step tasks when the client can use a compatible OpenAI-style route.

AI agent GPU API
Coding assistance
Cursor logoCursor Cline logoCline Continue logoContinue Aider logoAider OpenHands logoOpenHands

IDE and coding-agent capacity

Serve compatible IDE and coding-agent use cases such as repo Q&A, code search, edit loops, migration helpers, Aider-style automation, and OpenHands-style workflows.

Coding assistant API
Image and video
FLUX.1 Qwen-Image Stable Diffusion Wan 2.2 LTX-Video HunyuanVideo

Hugging Face media models

Evaluate FLUX.1, Qwen-Image, Stable Diffusion XL/3.5, Wan 2.2, LTX-Video, HunyuanVideo, and similar open-source or open-weight media models when license and VRAM fit.

Image/video GPU page

Named tools and models are examples of compatible-client or model-family review, not partnership claims or guaranteed availability. Media models are evaluated only when license, VRAM, latency, and throughput fit.

Example model families

Model names are examples, not a fixed catalog.

LighterHub can evaluate suitable open-source and open-weight families such as Llama, Qwen, Mistral, DeepSeek, FLUX.1, Qwen-Image, Stable Diffusion, Wan, LTX-Video, HunyuanVideo, embedding models, rerankers, and similar releases when VRAM, license, latency, and throughput fit. Agent and coding platforms are evaluated by API compatibility, context needs, tool behavior, and concurrency.

Client pitch

Savings, privacy, policy control, and workload flexibility in one operating path.

You receive a short readout that says what can move, what should stay premium, what privacy and policy boundaries apply, and which model families are worth benchmarking first.

Cost

Savings up to 90%

High-volume, repeatable calls are mapped away from expensive frontier defaults when a cheaper route can meet the same quality bar.

Privacy

Private inference by default

Prompts and completions are processed to serve the request, not train models. By default, request/response payloads are discarded; operational metadata is retained for billing and abuse prevention.

Workload flexibility

One GPU lane, many AI workloads

LLM inference is one use case. LighterHub can evaluate suitable agent backends, coding assistants, image/video generation, embeddings, rerankers, and batch inference when capacity and license fit.

Policy control

Configurable open-weight inference

Evaluate customer-controlled system prompts, moderation layers, routing rules, and fallback behavior for lawful workloads where default frontier-model policies are too rigid.

Policy flexibility is for legitimate business, research, creative, and internal automation workflows. LighterHub does not support illegal, abusive, or unsafe use.

View sample assessment Read cost-reduction method
Market context

Volatile GPU rental prices make AI cost planning an operating issue.

Public AI infrastructure commentary in May 2026 has reported GPU rental prices rising sharply, including 2x+ increases since January 2026. In that environment, teams need more than a single default model choice for every request.

Source context: public market commentary. LighterHub does not reproduce third-party charts; the assessment is based on your own workload, quality requirements, and usage profile.

Budget pressure

Know what must stay premium

Separate high-risk reasoning, safety-sensitive decisions, and quality-critical tasks from routine calls that can be evaluated against fit-for-purpose models.

Workflow controls

Reduce unmanaged usage

Review prompts, retries, batch jobs, and background automation so expensive frontier usage is intentional rather than accidental.

Deployment plan

Benchmark before committing

Test candidate models, fallback rules, latency, and cost ranges before shifting production traffic or reserving capacity.

Where savings come from

Most AI cost waste comes from using frontier models where fit-for-purpose models are sufficient.

Frontier models remain essential for high-risk tasks. LighterHub identifies where specialized open-source or open-weight models can meet the same quality standard at lower unit cost.

Workflow review

Define requirements

Map inputs, outputs, quality thresholds, latency targets, privacy constraints, usage volume, and failure cases.

Model evaluation

Benchmark candidates

Test fit-for-purpose open-source and open-weight options against representative prompts, jobs, media requests, and production success criteria.

Deployment guidance

Preserve frontier coverage

The assessment shows what can move, what needs fallback, and what should remain on OpenAI, Anthropic, or another premium route.

Next step

Submit a workload before expanding frontier-model spend.

Send the current provider, monthly spend, and workload type. LighterHub will reply with the first benchmark to run, the privacy boundaries, and the realistic savings range.

Get workload savings assessment