Zero-Hassle
Simple APIs, guided pilot setup, and workload routing that reduces operational drag.
Zero-Hassle Yield for AI compute
Lower-cost inference and batch compute for global AI teams
ZHY Compute helps AI teams get more usable compute yield with less operational burden through OpenAI-compatible APIs, private model endpoints, and cost-optimized batch workloads.
- 30-60%
- target cost reduction
- 24/7
- capacity monitoring
- API
- OpenAI-compatible
What ZHY means
Stop overpaying for workloads that do not need premium cloud margins.
ZHY stands for Zero-Hassle Yield: more usable AI compute, lower operational burden, and better economics for high-throughput workloads. Many AI products need dependable throughput more than ultra-low latency, so ZHY routes the right jobs to efficient GPU capacity with clear controls for data handling, logs, model access, and usage reporting.
Brand promise
More compute yield. Less infrastructure work.
High-Throughput
Capacity designed for batch inference, embeddings, evaluations, and async AI workloads.
Yield
Better unit economics from every token, job, endpoint, and GPU hour you put to work.
Use cases
Start with workloads that reward lower unit economics.
Batch inference
Process millions of prompts, documents, transcripts, or product listings with predictable throughput.
Embeddings at scale
Create search, recommendation, and retrieval indexes without burning budget on premium API calls.
Private model endpoints
Deploy open-source or custom models behind stable APIs for your product, team, or customer workflow.
Model evaluation
Run large evaluation suites, prompt tests, and regression checks without waiting on shared quotas.
Platform
A compute broker, not just another API reseller.
Route each workload to the right model, region, and capacity pool. Keep your application simple while the platform handles scheduling, failover, observability, usage limits, and cost reporting.
View OpenAI-compatible API examples on GitHub.
OpenAI-compatible gateway
Drop into existing products with familiar request formats and minimal engineering changes.
Hybrid capacity routing
Use overseas endpoints for latency-sensitive traffic and lower-cost pools for async compute.
Workload-level reporting
Track cost, latency, error rate, tokens, batches, and model usage by project or customer.
Trust
Designed for buyers who ask hard questions before they send data.
No training on customer data
Customer prompts, files, outputs, and logs are not used to train models.
Clear data controls
Configurable log retention, encryption in transit, access controls, and deletion workflows.
Compliance screening
KYC, KYB, sanctions checks, abuse controls, and acceptable-use review for pilot customers.
Region-aware architecture
Match workloads with appropriate regions based on latency, sensitivity, and customer requirements.
Pilot pricing
Bring one expensive workload. We will benchmark it.
Pilot customers receive a workload review, model recommendation, cost estimate, and private benchmark before committing volume.
Pilot package
Cost audit + test endpoint
- OpenAI-compatible endpoint
- Batch inference or embeddings workload
- Usage dashboard and exportable report
- Target savings estimate before scale-up
FAQ
Questions AI teams usually ask before a pilot.
What is ZHY Compute?
ZHY Compute is a high-throughput AI compute partner for teams that need lower-cost inference, batch processing, embeddings, model evaluation, and private model endpoints.
Who is ZHY Compute for?
ZHY Compute is built for AI SaaS companies, developer tool builders, data teams, automation platforms, content AI products, and teams with recurring inference or batch compute demand.
Does ZHY Compute provide OpenAI-compatible APIs?
Yes. The API gateway supports familiar request patterns so teams can run a pilot with minimal engineering changes.
What workloads are best suited for ZHY Compute?
The best starting points are high-volume, recurring, and benchmarkable jobs such as batch inference, embeddings at scale, private endpoints, and model evaluation suites.
Does ZHY Compute use customer data for model training?
Customer prompts, files, outputs, and logs are not used to train models unless a customer explicitly requests and contracts for that work.
How does a ZHY Compute pilot work?
A pilot starts with workload review, then a benchmark endpoint or batch pipeline, followed by a cost, latency, throughput, reliability, and quality comparison before scale-up planning.
Get started
Tell us what you are running today.
Share your current model, monthly token or job volume, latency needs, and target region. We will reply with a practical pilot plan.
GitHub examples: github.com/ZHYCOMPUTE/zhy-compute-examples