How to Scale Enterprise AI Without Losing Control of GPU Costs and Agent Access

What Happened

Recent AWS deployment examples illustrate three distinct infrastructure challenges: sharing expensive compute, giving agents controlled access to business systems, and operating model-driven workflows reliably. The common lesson is that production AI needs more than a model endpoint—it needs explicit resource allocation, authorization and execution controls.

• Shared training capacity: AWS Batch quota management for SageMaker Training jobs supports reserved allocations, lending and borrowing, and preemption. Teams can share idle GPU capacity while preserving protected allocations. [5]
• Private agent tools: MoEngage used Amazon Bedrock AgentCore Gateway to expose roughly 400 private API operations as MCP tools, with IAM authentication, Cedar authorization and task-based tool discovery. Its deployment serves 10 internal agents across seven AWS Regions. [6]
• Managed browser execution: Epsilon added natural-language UI testing alongside Playwright, using Amazon Nova Act and AgentCore Browser Tool. A queued runner executes tests in managed browser sessions and captures step-level results and screenshots. [3]
• Self-hosted inference: NVIDIA’s Nemotron 3.5 Super VL reference architecture uses Dynamo and vLLM on EKS or SageMaker HyperPod EKS. Its tests illustrate how separating prefill from decode can improve time to first token under particular workloads. [7]

Why It Matters to Businesses

Utilization and availability are different objectives. Dedicated GPU allocations protect access but can leave expensive capacity idle. Borrowing improves utilization, but reclaimed capacity can interrupt work. The AWS example demonstrates both cross-team reclamation and priority-based preemption within a team; scheduling policy therefore becomes a business decision about which workloads may wait or restart. [5]

Agent integration can become a larger platform burden than inference. MoEngage sought to avoid operating more than 20 separate MCP servers across its engineering organization. It estimates that the gateway approach saved six months of platform engineering and reduced API onboarding from days to hours. These are customer-reported outcomes, not a general ROI guarantee, but they show why reusable integration infrastructure matters. [6]

Faster authoring does not establish reliable execution. In Epsilon’s internal benchmark, an 18-step workflow took 5–10 minutes to author with Nova Act versus 30–90 minutes with Playwright. Initial exploration still typically required four or five prompt iterations. Teams should measure maintenance effort, false passes and repeatability—not just test creation speed. [3]

Kimbodo Engineering Perspective

Separate model choice from platform control

We would keep authentication, authorization, quotas and audit records outside the model. A model can propose an action; trusted infrastructure must determine whether that action is permitted. MoEngage’s server-side policies and propagation of tenant context outside model-visible tool arguments provide a useful implementation pattern. [6]

Keep training scheduling separate from inference capacity planning

A training job that can checkpoint may tolerate interruption. An interactive inference service has a different obligation: bounded latency and sufficient available capacity. We would not treat training quota lending as an inference scaling strategy. For serving, capacity decisions should follow measured prompt lengths, output lengths, concurrency and latency targets.

Adopt self-hosting only when the economics support it

Nemotron 3.5 Super VL has 120 billion total parameters, despite activating 12 billion per token. Active parameter count alone does not determine deployment memory requirements. The reference was validated on eight-GPU B200 and H200 instances, making this a substantial infrastructure commitment. [7]

Managed inference is generally the starting point when demand is uncertain. Self-hosting deserves evaluation when sustained utilization, customization or deployment-control requirements justify GPU capacity and operational staffing. Disaggregated serving adds another trade-off: potentially better latency and utilization, but more scheduling, networking and failure-handling complexity.

How We Would Implement It

• Establish workload classes and budgets. Separate interactive inference, browser automation, batch processing and training. Define latency targets, queue limits, interruption tolerance and a cost ceiling per successful business task.
• Build a governed tool layer. Generate tool definitions from reviewed OpenAPI specifications in CI. Authenticate workloads with IAM, enforce operation-level permissions server-side, and connect privately to internal APIs. Filter discovery results by authorization before using semantic search to select relevant tools, following the MoEngage pattern. [6]
• Queue long-running agent work. Use bounded worker pools, execution timeouts and explicit completion states. For browser automation, retain step-level evidence and keep deterministic Playwright checks for critical assertions; add natural-language execution where its flexibility earns its cost. Epsilon’s deployment demonstrates the managed-browser and queued-runner approach. [3]
• Allocate training capacity by business priority. Reserve capacity for deadline-sensitive work and lend idle capacity to interruptible jobs. Test checkpoint recovery and retry behavior before enabling preemption; a job returning to RUNNABLE is not proof that its application state was preserved. [5]
• Benchmark inference before committing capacity. Compare managed endpoints with aggregated self-hosted serving, then test disaggregation if prefill contention is material. NVIDIA’s example reached approximately 320 tokens per second per replica at concurrency 32 under its stated latency limits; use that as a test reference, not a capacity promise. [7]
• Instrument business outcomes. Track cost per completed task, tool authorization failures, retries, queue time, GPU utilization and inference latency. Require approval and recovery procedures for consequential write operations.

Risks, Costs and Security

Private connectivity is not sufficient authorization. Every tool invocation needs enforceable permissions and trusted tenant identity. API specifications also need review: automatically publishing a new operation can expand an agent’s capabilities without a deliberate security decision.

Audit evidence creates sensitive data. Browser screenshots, recordings, prompts and tool responses can contain credentials or customer information. Apply encryption, restricted access, redaction where feasible and explicit retention limits.

Measure total cost rather than token price alone. Include idle GPU capacity, checkpoints, repeated execution, browser sessions, observability storage and platform engineering. Smaller discovered tool catalogs can reduce prompt overhead, while borrowing can improve training utilization—but neither eliminates the need for workload-level cost measurement. [5][6]

Adopt each component against a measured requirement. A gateway can reduce integration work; quota policies can improve resource sharing; disaggregated serving can improve particular latency profiles. None substitutes for authorization tests, representative load testing and an operational recovery plan.

Sources

1. [3] Building intent-driven UI testing with Amazon Nova Act
2. [5] Lend and borrow resources across teams with AWS Batch’s quota management for SageMaker Training jobs
3. [6] How MoEngage turned over 400 APIs into agent tools with AgentCore Gateway
4. [7] Accelerate agentic AI with NVIDIA Nemotron models on Amazon EKS and SageMaker HyperPod EKS