platoseedplatoseed
Inference for real-time agents
Pipeshift helps engineering teams run real-time inference in production. We offer optimized runtimes to meet latency/throughput SLAs, paired with infrastructure orchestration that auto-scales and routes workloads across clusters and regions at cost-effective rates.
Pipeshift provides a production inference platform and infrastructure to deploy AI models and real-time agents with low latency across clouds and regions. It combines managed inference clusters, optimized runtimes, and a custom framework (MAGIC) to scale real-time AI workloads while offering SLA-based deployments and observability.
The platform offers managed inference clusters with single-tenant deployments, SLA-defined API endpoints, and auto-scaling to handle real-time workloads. It provides a platform for serving open-source, custom, and fine-tuned models with high throughput and low latency, including a Model API Sandbox for testing, infrastructure observability for metrics and costs, and Forward Deployed Engineers to assist with optimization and scaling. It features a proprietary Modular Architecture for GPU Inference Clusters (MAGIC) to customize inference infrastructure, production-ready orchestration for load balancing, schedulers, and auto-scalers, and SLA-based auto-scaling to manage GPU resources, along with fast cold-starts and high uptime guarantees.
Who it’s for: AI/ML production infrastructure for enterprises deploying real-time AI agents and models
Hiring/traction indicated by enterprise-focused features and FDE support; product-market fit signals via SLA-focused, multi-region deployment capabilities
CEO @ Pipeshift. Building scalable infrastructure for open source AI workloads.
CTO @ Pipeshift. Focused on squeezing out max LLM performance from GPUs
Replace GPT/Claude in production with specialized LLMs that are fine-tuned on your context, offering higher accuracy, lower latencies and model ownership.
Pipeshift provides a cloud platform for fine-tuning and serving open-source LLMs, enabling teams to productionize their own specialized models with faster inference and ownership. It targets companies with high usage on frontier LLMs, offering LoRA fine-tuning, serverless APIs, and dedicated GPU-optimized instances to replace generic models like GPT/Claude with context-specific LLMs.
Formerly “Xylem AI”, “Pipeshift AI”
Inference at Light Speed
AI for AI Infrastructure
