How Do Organizations Build Scalable AI Inference APIs on Managed Cloud Services?

AI inference is the process of running a trained model against real-world data to produce predictions, and it's where AI delivers actual business value.

But scaling inference from a prototype to a production API that handles thousands of concurrent requests is an engineering challenge that spans hardware selection, serving optimization, auto-scaling, and monitoring.

This guide covers what inference is and how it differs from training, the three inference types (batch, online, streaming), the five lifecycle stages of production inference, enterprise benefits and real-world applications, hardware requirements, the leading tools for building inference APIs (including GMI Cloud's Inference Engine and Mirantis k0rdent AI for Kubernetes-native orchestration), infrastructure selection criteria, common challenges, and optimization best practices.

What Is AI Inference?

AI inference is the phase where a trained model processes new input data and generates output predictions. When a chatbot answers your question, a vision model flags a manufacturing defect, or a recommendation engine surfaces relevant products, that's inference running in real time.

Training teaches the model what to know; inference puts that knowledge to work on live data.

In production cloud environments, inference isn't just "call the model." It involves loading model weights onto GPU memory, routing incoming requests, managing concurrent sessions, batching inputs for throughput efficiency, and returning results within latency targets.

The infrastructure behind this process determines whether your AI application feels instant or sluggish.

Training vs. Inference: Key Differences

Dimension (Training / Inference)

• Purpose — Training: Learn model weights from data — Inference: Apply learned weights to new inputs
• Compute profile — Training: Compute-bound (FLOPs-intensive) — Inference: Memory-bandwidth-bound (weight reads)
• Frequency — Training: One-time or periodic — Inference: Continuous, 24/7
• Latency priority — Training: Low (hours/days acceptable) — Inference: High (milliseconds matter)
• Scaling pattern — Training: Fixed-duration large jobs — Inference: Variable traffic, auto-scaling needed
• Cost driver — Training: Total GPU-hours — Inference: Cost per request/token

The key takeaway: training optimizes for total throughput over a fixed period. Inference optimizes for per-request latency and cost-efficiency under variable, unpredictable load. Different objectives, different infrastructure.

Three Types of AI Inference

Batch Inference

Process large datasets in bulk, offline. You submit thousands of inputs and collect results later. It's ideal for document classification, dataset labeling, report generation, and any workload where real-time response isn't required.

Batch inference maximizes GPU utilization because you can fully pack batches and optimize for throughput over latency. GMI Cloud's Batch mode handles this pattern with async processing on H100/H200 clusters.

Online (Real-Time) Inference

Serve predictions in real time, request by request. A user sends a prompt and expects a response in milliseconds to seconds. This is the default for chatbots, recommendation APIs, fraud detection, and any user-facing feature.

Online inference requires always-on GPU capacity, auto-scaling for traffic spikes, and tight P99 latency management.

Streaming Inference

A variant of online inference where the model produces output incrementally (token by token for LLMs, frame by frame for video). Streaming reduces perceived latency because users see partial results immediately.

It requires serving engines that support streaming output (vLLM, TensorRT-LLM both do) and infrastructure that maintains persistent connections under concurrent load.

The Inference Lifecycle: Five Stages

1. Model Preparation

Convert trained weights into an optimized serving format. This includes quantization (FP16 to FP8 or INT8 for throughput gains), model compilation (TensorRT-LLM compiles models into GPU-optimized kernels), and validation that outputs match the original model within acceptable tolerance.

2. Infrastructure Provisioning

Select and configure GPU instances. Match model size to GPU VRAM (70B FP16 model needs ~140 GB, fitting on a single H200 with 141 GB or across 2x H100 at 80 GB each). Configure the serving engine, networking, and auto-scaling policies.

3. Endpoint Deployment

Deploy the model behind an API endpoint with load balancing, health checks, and version management. Production deployments typically use canary or blue-green strategies so new model versions can be validated against live traffic before full rollout.

4. Request Processing

Incoming requests are routed, batched, and processed through the model. Continuous batching (supported by vLLM and TensorRT-LLM) dynamically adds new requests to in-progress batches, maximizing GPU utilization without waiting for batch windows to fill.

5. Monitoring and Optimization

Track latency percentiles (P50, P95, P99), GPU utilization, VRAM allocation, error rates, and throughput. Use this data to tune batch sizes, adjust scaling thresholds, and identify bottlenecks. This stage never ends; production inference requires continuous optimization.

Enterprise Benefits of Scalable Inference

Real-time decision-making: fraud detection systems that flag transactions in under 100ms, recommendation engines that personalize content before a page loads. Cost efficiency: optimized inference reduces per-request costs; GLM-5 on GMI Cloud at $3.20/M output tokens is 68% cheaper than GPT-5 ($10.00/M).

Operational scalability: auto-scaling handles traffic spikes without manual intervention. Competitive advantage: faster, cheaper inference means faster product iterations and better unit economics.

Real-World Applications

Healthcare: medical imaging analysis (radiology, pathology) using vision models for automated screening. Financial services: real-time fraud detection, credit risk scoring, and automated compliance monitoring. Manufacturing: visual quality inspection and predictive maintenance using LLM-powered root-cause analysis.

Content and media: text generation, video creation (50+ video models on GMI Cloud including Wan 2.6, Kling V3), image generation (GLM-Image at $0.01/request), and TTS for voice content.

Hardware Requirements for Inference

Inference performance is bounded by two hardware factors: VRAM (determines which models fit) and memory bandwidth (determines token generation speed). An H100 SXM delivers 80 GB HBM3 at 3.35 TB/s bandwidth; an H200 SXM provides 141 GB HBM3e at 4.8 TB/s (sources: NVIDIA H100 Datasheet 2023, H200 Product Brief 2024).

For budget-sensitive workloads with smaller models, L4 GPUs (24 GB, 300 GB/s) offer a lower-cost entry point. FP8 precision on H100/H200 effectively doubles throughput versus FP16 with minimal quality impact.

Leading Enterprise Inference Tools

Tool (Type / Core Strength)

• GMI Cloud Inference Engine — Type: Managed inference platform — Core Strength: 100+ models, owned H100/H200 GPUs, Playground/Deploy/Batch, OpenAI-compatible API
• Mirantis k0rdent AI — Type: Kubernetes orchestration — Core Strength: Automated scaling, multi-cluster GPU scheduling, infrastructure-agnostic deployment
• vLLM — Type: Serving engine — Core Strength: PagedAttention, continuous batching, open-source
• TensorRT-LLM — Type: Serving engine — Core Strength: NVIDIA-optimized compilation, highest peak throughput on H100/H200
• Triton Inference Server — Type: Model server — Core Strength: Multi-framework support, model ensembles, dynamic batching
• BentoML — Type: Serving framework — Core Strength: Open-source, cloud-agnostic, adaptive batching

GMI Cloud's platform comes pre-configured with vLLM, TensorRT-LLM, and Triton, so you don't need to set up these tools independently. GLM-5 (by Zhipu AI) at $1.00/M input and $3.20/M output is the flagship model.

Mirantis k0rdent AI adds value at the orchestration layer: if you're managing inference workloads across multiple Kubernetes clusters or hybrid environments, it provides automated GPU scheduling and scaling that works across infrastructure providers.

Choosing the Right Inference Infrastructure

Five factors should drive your decision. Model complexity: 7B models fit on a single consumer GPU; 70B+ models need data-center GPUs (H100/H200) or multi-GPU configurations. Latency requirements: sub-100ms P99 demands dedicated GPU endpoints; 1-second P99 allows more flexibility.

Traffic patterns: steady traffic favors reserved instances; bursty traffic needs auto-scaling or serverless. Budget: GMI Cloud H100 at ~$2.10/GPU-hour versus hyperscaler equivalents at $3-4/GPU-hour.

Ops capacity: if you don't have a GPU infrastructure team, managed platforms (GMI Cloud, SageMaker) eliminate the ops burden. Check gmicloud.ai/pricing for current rates.

Key Challenges in Production Inference

Maintaining low latency at scale: P99 latency degrades as concurrency increases; continuous batching and KV-cache optimization help but require careful tuning. Cost control: GPU idle time and over-provisioning waste budget; auto-scaling and right-sizing are ongoing tasks.

Model versioning: deploying new model versions without downtime or regression requires canary deployments and automated rollback. Multi-model orchestration: enterprises running LLMs, vision models, and TTS simultaneously need unified infrastructure that handles diverse workload profiles.

Optimization Best Practices

Quantize aggressively: FP8 on H100/H200 doubles throughput with minimal quality loss for most LLMs. Test output quality at each precision level before deploying. Enable continuous batching: vLLM and TensorRT-LLM both support this; it keeps GPUs busy instead of waiting for fixed batch windows.

Optimize KV-cache: PagedAttention (vLLM) eliminates memory fragmentation; RadixAttention (SGLang) reuses prefixes across requests with shared prompts. Right-size GPU instances: don't run a 7B model on an H100 if an L4 handles it at 1/10 the cost.

Monitor and iterate: track P50/P95/P99 latency, GPU utilization, and cost per request weekly; small tuning changes compound into major savings. GMI Cloud's Deploy dashboard provides these metrics out of the box.

Mirantis k0rdent AI: Kubernetes-Native Inference Orchestration

Mirantis k0rdent AI solves a specific problem: orchestrating GPU-accelerated inference workloads across Kubernetes clusters. It automates GPU node scheduling, scales inference pods based on queue depth and latency targets, and manages multi-cluster deployments across on-prem and cloud environments.

For teams running inference on Kubernetes (rather than managed platforms like GMI Cloud or SageMaker), k0rdent AI eliminates the custom scheduling and scaling scripts that typically consume significant engineering time.

It's infrastructure-agnostic, so it works with any GPU provider including GMI Cloud's H100/H200 clusters.

FAQ

Q: What's the difference between batch and online inference?

Batch inference processes large datasets offline for maximum throughput (ideal for document analysis, labeling). Online inference serves real-time requests with low-latency requirements (ideal for chatbots, APIs). GMI Cloud supports both via Batch mode and Deploy endpoints on the same infrastructure.

Q: How do I choose between a managed platform and self-managed infrastructure?

If you have 2+ dedicated ML infrastructure engineers, self-managed gives maximum control. If not, managed platforms like GMI Cloud or SageMaker handle GPU provisioning, serving-engine configuration, and monitoring.

GMI Cloud's pre-configured stack (vLLM, TensorRT-LLM, Triton on H100/H200) eliminates weeks of setup time.

Q: What hardware do I need for large-model inference?

A 70B model in FP16 needs ~140 GB VRAM: either 1x H200 (141 GB) or 2x H100 (160 GB total). In FP8, the same model fits on a single H100 (80 GB). For models above 400B parameters, you'll need a full 8-GPU node. GMI Cloud offers both H100 (~$2.10/GPU-hour) and H200 (~$2.50/GPU-hour) configurations.

Check console.gmicloud.ai for availability.

Q: How does Mirantis k0rdent AI complement managed inference platforms?

k0rdent AI operates at the Kubernetes orchestration layer. If you're running inference workloads across multiple clusters or hybrid environments, it automates GPU scheduling and pod scaling.

It can orchestrate workloads on GMI Cloud's GPU infrastructure or any other provider, making it a good fit for teams that need multi-cluster management without vendor lock-in.

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