Pipeshift (YC S24)

Founded 2024

Deploy and run open-source AI models on dedicated GPU infrastructure with custom latency SLAs

Pricing model

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You can use Pipeshift to deploy, run, and scale custom or open-source machine learning models on dedicated GPU clusters. It allows your engineering team to define custom latency and uptime requirements instead of sharing multi-tenant endpoints. You can test your models inside an API sandbox, monitor compute costs, track GPU metrics, and configure auto-scaling rules that scale workloads down to zero when idle. Pipeshift also provisions dedicated single-tenant environments across multiple cloud regions to keep your inference response times fast and consistent.

What Pipeshift (YC S24) does

Deploy open-source and fine-tuned AI models to private clustersConfigure auto-scaling thresholds and scale-to-zero settings for GPU nodesMonitor real-time latency, throughput, and hardware utilization metricsTest model endpoints inside an interactive API sandboxManage team permissions and access controls across deployment environmentsOrchestrate compound AI pipelines on shared compute podsMigrate inference workloads from shared public APIs to dedicated instancesDedicated single-tenant GPU deployments to prevent noisy-neighbor latency spikesCustom SLA-driven auto-scaling that scales workloads down to zero when inactiveInteractive API sandbox for testing and prototyping machine learning modelsInfrastructure observability dashboards covering API latency, compute costs, and GPU metricsWorkload orchestration with custom bin-packing pipelines to maximize GPU utilizationSupport for multi-cloud deployments across more than 10 global regionsFine-tuned kernel optimizations and advanced caching for reduced time-to-first-token

Pricing breakdown

Pricing model: Book a demo

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