Choose your model
Use the open model your product needs, from focused low-latency models to larger reasoning and multimodal systems. We validate the serving profile against your real workload.
Yolo-Auto Enterprise
Dedicated AI infrastructure, built around your business.
Run the model and serving architecture your workload actually needs on a dedicated GPU cluster. Get private, dependable inference without fitting your team into a shared platform.
Custom architecture and pricing. Contact hello@yolo-auto.com.
Private capacity
Dedicated to your workload
- Tenant-isolated GPU cluster
- Ephemeral request processing
- Custom models and runtime
Dedicated, not sharedReserved compute removes shared traffic from your inference path.
Ephemeral by designPrompt and response content is processed without content logging or retention.
Engineered for reliabilityCapacity, redundancy, and rollouts planned around your production targets.
Built with youWe add capabilities for your workflows, controls, and integrations.
Your workload sets the architecture
The stack should fit the use case, not the other way around.
Start with your quality, latency, context, throughput, and cost requirements. We will help select and operate the model, runtime, and cluster design that meets them.
Choose your architecture
Shape GPU topology, inference runtime, context limits, quantization, routing, and redundancy around the use case instead of accepting a generic endpoint.
Own your capacity
Your cluster is sized for your traffic and reserved for your team. Plan headroom for peaks, long contexts, batch work, or latency-sensitive production paths.
Private by architecture
Your prompts are processed, returned, and gone.
Prompt and response content is processed ephemerally. It is not logged, retained, or available for our team to browse or retrieve after the response, and it is never used to train models.
We retain non-content operational telemetry such as health, status, token counts, and latency to keep the cluster reliable, without turning your requests into a content archive. Tenant-isolated compute keeps the enterprise data path separate from shared inference capacity.
Discuss your privacy requirements →No content logsNo prompt or response bodies in application or operator logs.
No conversation storeNo browsable history of your enterprise requests.
No model trainingYour inputs and outputs do not enter training pipelines.
Clear deployment boundaryDedicated compute keeps your workload separate from shared inference capacity.
Production reliability
Capacity you can plan a product around.
Reliable inference starts with control of the hardware and serving path. We engineer the cluster around your traffic profile, then operate it with the headroom and deployment discipline production systems require.
Dedicated GPU clusters
No competition with unknown tenants. Capacity is reserved and tuned for your workload.
Resilient by design
Topology, redundancy, rollout strategy, and recovery plans are matched to your reliability targets.
SOC 2 compliant infrastructure
Your deployment runs on hardware within SOC 2 compliant infrastructure controls.
Direct technical ownership
Work with the engineers designing and operating the inference stack from evaluation through production.
A product partner, not just an endpoint
If your team needs it, we can build it with you.
Enterprise requirements rarely stop at model hosting. We will add features specifically for your deployment, from API behavior and routing to access controls, observability interfaces, and workflow integrations.
1Define the workload
Share your use case, traffic shape, model preferences, privacy requirements, and success criteria.
2Design the system
We propose the model, hardware, runtime, reliability plan, and custom capabilities.
3Launch together
Validate on representative traffic, move into production, and evolve the stack as your product grows.
Custom pricing
Tell us what you need to run.
Bring the use case. We will help turn it into a private, production-ready inference platform.
Contact hello@yolo-auto.com →
Dedicated clusters, custom architecture, and enterprise-specific features.