How does Terradev optimize GPU costs and performance for AI workloads?

Terradev uses Terraform for parallel GPU provisioning and provider arbitrage. It integrates advanced vLLM cost optimizations like LMCache, KV Cache Offloading, and Sleep Mode, along with an integrated MoE serving architecture, to ensure optimal resource utilization and reduced expenditure.

What is Terradev and what problem does it solve?

Terradev is an agentic AI infrastructure orchestration tool that provides imperative control over multi-cloud GPU resources. It solves the complexity of managing, optimizing, and deploying AI workloads across 20+ cloud providers, focusing on cost, performance, and lifecycle efficiency.

Can I manage Terradev from Claude.ai?

Yes, Terradev features a native Claude.ai Connector. This allows you to remotely manage GPU infrastructure and access all 192 tools directly from any Claude.ai conversation using an SSE connection, streamlining your workflow.

What types of AI-related tools and features does Terradev offer?

Terradev provides a comprehensive suite of 192 tools covering GPU provisioning, vLLM/SGLang inference, Arize Phoenix observability, NeMo Guardrails safety, Qdrant vector databases for RAG, Kubernetes management, MoE model deployment, and cost intelligence across multiple cloud providers.

Terradev: Multi-Cloud GPU Orchestration for Agentic AI Workloads

Terradev is an imperative DSL/MCP (Micro-Controller Program) server designed to manage and optimize complex AI workloads, particularly those developed with Claude Code. It offers a comprehensive suite of 192 tools covering every aspect of the GPU lifecycle, from provisioning and inference to observability, safety, and cost intelligence. Built on a Terraform core engine, Terradev enables true parallel GPU provisioning across 20+ cloud providers, ensuring state management, infrastructure-as-code, and cost optimization through provider arbitrage. It integrates seamlessly with Claude.ai for remote access and provides specialized support for advanced LLM architectures like Mixture-of-Experts (MoE) with a focus on vLLM cost efficiencies and robust monitoring.

Key Features

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02Advanced vLLM cost optimizations, including LMCache, KV Cache Offloading, MTP Speculative Decoding, Sleep Mode, and Multi-LoRA.

03Native Claude.ai Connector enabling remote GPU infrastructure management and tool access directly from any Claude.ai conversation via SSE.

04Terraform-powered parallel GPU provisioning and infrastructure management across 20+ cloud providers, ensuring optimal efficiency and state tracking.

05Comprehensive suite of 192 tools for GPU provisioning, vLLM/SGLang inference, observability (Arize Phoenix), safety (NeMo Guardrails), and vector databases (Qdrant).

06Integrated MoE serving architecture supporting Expert Parallelism (EP), Expert Parallelism Load Balancing (EPLB), Dual-Batch Overlap (DBO), and optimized all-to-all communication kernels.

Use Cases

01Implementing and managing sophisticated Mixture-of-Experts (MoE) model serving architectures with expert parallelism and dynamic rebalancing.

02Optimizing cost and performance of LLM inference and training by leveraging multi-cloud GPU arbitrage and advanced vLLM configurations.

03Automating the provisioning, deployment, and management of GPU infrastructure for agentic AI workloads, especially those built with Claude Code.