Continuous AI Workflow Execution: What 'Managed' Actually Manages

Every cloud platform claims to be "managed." The word appears on every pricing page and every feature comparison. But "managed" covers a spectrum from "we provision the GPU" to "we handle everything including model deployment, scaling, and monitoring."

Teams that don't clarify what they need managed end up either over-paying for services they don't use or under-buying and building infrastructure they expected the platform to handle. This article breaks down the three layers of "managed," maps them to platform types, and shows where GMI Cloud fits on the spectrum.

Three Layers of 'Managed'

The word "managed" can refer to any combination of three distinct layers. Each layer removes a different operational burden.

Layer 1: GPU management. The platform provisions hardware, installs drivers, maintains uptime, and replaces failed GPUs. You handle everything above the hardware: runtimes, models, scaling, and monitoring. This is the minimum definition of "managed."

Layer 2: Runtime management. The platform also installs and maintains inference frameworks (vLLM, TensorRT-LLM, Triton), CUDA versions, and dependencies. You deploy models and configure serving parameters. Model deployment and scaling are your responsibility.

Layer 3: Full orchestration. The platform handles model deployment, auto-scaling, health checks, monitoring, and endpoint routing. You make API calls. The platform manages everything between your request and the GPU.

Layer	Platform Manages	You Manage	Examples
GPU only	Hardware, drivers, uptime	Runtime, models, scaling, monitoring	Vast.ai, CoreWeave bare-metal
GPU + runtime	Hardware + inference stack	Models, scaling, monitoring	GMI Cloud GPU, Lambda Labs
Full orchestration	Everything	API integration only	GMI Cloud Inference Engine, AWS Bedrock, Vertex AI

Most misunderstandings happen because teams assume "managed" means Layer 3 when the platform only delivers Layer 1.

What Continuous Execution Actually Requires

Running AI workflows continuously (24/7 or on recurring schedules) adds requirements beyond what single-request inference demands.

Process supervision. A process that runs for days will eventually encounter a transient error: a network timeout, a corrupted input, a GPU driver hiccup. Continuous execution needs a supervisor that detects failures, logs the context, and restarts the process automatically.

State persistence. Workflows that maintain context across sessions (conversation history, accumulated results, running aggregates) need persistent storage that survives process restarts. In-memory state disappears when the process crashes.

Scheduled triggers. Many continuous workflows aren't truly continuous; they run on schedules. A daily batch job, an hourly data refresh, or a weekly report generation all need reliable scheduling with missed-execution handling.

Resource right-sizing over time. Traffic patterns change. A workflow that needed 4 GPUs in January might need 8 in March. Continuous execution benefits from monitoring that tracks resource utilization trends and recommends scaling adjustments.

Matching 'Managed' Level to Workflow Type

Different continuous workflows need different levels of management.

Always-on inference endpoint. Serves user requests 24/7. Needs Layer 3: auto-scaling, health checks, and zero-downtime model updates. Building this yourself requires load balancers, health check endpoints, rolling deployment logic, and monitoring. Buying it from a fully managed platform eliminates 2-4 weeks of engineering.

Scheduled batch pipeline. Runs on a cron schedule, processes data, and terminates. Needs Layer 2: pre-configured runtime so the job starts fast. Layer 3 is optional since the workflow has predictable start/stop times. Scheduling can be handled by external tools (cron, Airflow, Prefect).

Event-driven automation. Triggered by external events (new data arrival, webhook, queue message). Needs Layer 3 for fast cold start and automatic scaling. Serverless GPU platforms or MaaS endpoints handle this pattern best.

Multi-model orchestration. Chains multiple models in sequence. Needs Layer 2 at minimum (all runtimes pre-installed) and ideally Layer 3 for routing between models. Orchestration frameworks (LangChain, Temporal) can add the coordination layer on top of a Layer 2 platform.

How Platforms Map to Management Layers

Layer 1 (GPU only): Vast.ai provides raw GPU access at low cost. CoreWeave offers Kubernetes-managed bare-metal. Both require you to install runtimes, deploy models, and build scaling logic. Lowest cost, highest engineering effort.

Layer 2 (GPU + runtime): Lambda Labs provides pre-configured development environments. GMI Cloud GPU instances include TensorRT-LLM, vLLM, Triton, and CUDA 12.x pre-installed. RunPod offers template-based deployments. You deploy models and manage scaling, but skip runtime setup.

Layer 3 (full orchestration): AWS Bedrock and Google Vertex AI provide fully managed model serving with auto-scaling, monitoring, and endpoint management. GMI Cloud's Inference Engine offers 100+ pre-deployed models with per-request pricing and no infrastructure management. Trade-off: less control over model configuration and hardware selection.

Building vs. Buying Each Layer

The build-vs-buy decision should be made layer by layer, not all-or-nothing.

Layer 1: Almost always buy. Operating physical GPUs or managing virtual GPU provisioning rarely makes sense unless you're at hyperscaler scale. The engineering effort to maintain hardware, handle failures, and manage firmware updates exceeds the cost of renting.

Layer 2: Buy if inference is not your core product. Installing and maintaining CUDA, cuDNN, NCCL, and inference frameworks takes 1-2 engineering days and requires periodic updates. Pre-configured platforms like GMI Cloud or Lambda Labs eliminate this ongoing cost.

Layer 3: Build if you need fine-grained control. Buy if you need speed. Custom auto-scaling logic, canary deployments, and specialized health checks require building orchestration. If standard scaling and monitoring suffice, fully managed platforms save weeks of engineering.

GMI Cloud Across Management Layers

GMI Cloud is worth evaluating because it covers Layer 2 and Layer 3 on one platform, letting teams choose the right management level per workload.

Layer 2 (GPU + runtime): H100 SXM (80 GB HBM3, 3.35 TB/s, ~$2.10/GPU-hour) and H200 SXM (141 GB HBM3e, 4.8 TB/s, ~$2.50/GPU-hour). Pre-installed: TensorRT-LLM, vLLM, Triton, CUDA 12.x, NCCL. 8-GPU nodes with NVLink 4.0 (900 GB/s bidirectional per GPU on HGX/DGX platforms) and 3.2 Tbps InfiniBand.

Layer 3 (full orchestration): Inference Engine with 100+ pre-deployed models. Per-request pricing ($0.000001-$0.50/request). No GPU provisioning, no runtime management, no scaling logic required.

Teams should match management level to workflow type and verify auto-scaling behavior, monitoring capabilities, and scheduling support against their specific requirements. Check gmicloud.ai for current details.