The enterprise AI landscape in 2026 is defined by one problem: getting models from experimentation to production without burning through six-figure cloud bills. The MLOps toolchain has matured significantly, but picking the right stack remains a minefield. Here’s the infrastructure guide for teams serious about production AI.

The Production AI Stack: Five Layers

Layer 1: Model Training and Fine-Tuning

Where you train determines everything downstream. Options range from managed platforms to self-hosted GPU clusters.

RunPod has emerged as the affordable GPU option for fine-tuning. Starting at $0.34/hour for a T4 GPU, it handles everything from LoRA fine-tuning to full model training without the AWS price shock. Their serverless GPU option auto-scales pods based on compute demand.

Pricing: Pay-per-use GPU instances starting at $0.34/hr. No long-term commitments.

Modal takes the serverless approach further — you write Python code and Modal handles GPU provisioning, caching, and auto-scaling. Perfect for teams that don’t want to manage infrastructure. Their GPU pricing starts around $0.62/hr for A100s.

Layer 2: Model Serving and Inference

Once your model is trained, serving it efficiently is the bottleneck.

vLLM has become the de facto standard for high-throughput LLM serving. It uses continuous batching and PagedAttention to serve models at 2-4x the throughput of Hugging Face endpoints, with up to 4x memory efficiency through quantization support (GGUF, GPTQ).

For managed serving, Replicate offers model-as-a-service with no GPU management required. Pay per second of inference time, scale to zero when idle. Great for prototypes and low-traffic production workloads.

Layer 3: Experiment Tracking and Monitoring

You can’t improve what you can’t measure.

Weights & Biases (W&B) remains the industry standard for experiment tracking. Log training runs, hyperparameters, metrics, and model artifacts. Their model registry integrates with your CI/CD pipeline. Free tier covers individual researchers; team plans start at $599/month.

For open-source alternatives, MLflow handles the same job without vendor lock-in. Self-host it on any cloud provider or on-prem.

Layer 4: Data Pipeline and Feature Store

Garbage in, garbage out — even with billion-parameter models.

Your data pipeline needs to handle ingestion, transformation, feature engineering, and versioning. Databricks has become the enterprise standard, but it’s expensive. For smaller teams, Prefect or Airflow on a managed cloud provider fills the gap at a fraction of the cost.

Layer 5: Orchestration and Automation

The glue that holds everything together.

This is where you automate retraining pipelines, model validation, deployment rollouts, and rollback procedures. Popular choices include Kubeflow for Kubernetes-native orchestration and Argo Workflows for CI/CD-style ML pipelines.

Solo developer / small startup:

  • Training: RunPod ($0.34/hr GPUs)
  • Serving: vLLM self-hosted on a DigitalOcean droplet
  • Tracking: W&B free tier
  • Orchestration: Prefect Cloud free tier

Mid-size team (5-20 engineers):

  • Training: Modal or RunPod serverless GPUs
  • Serving: Replicate for ease, or vLLM on AWS GPU instances
  • Tracking: W&B team plan
  • Data: Airflow on managed Kubernetes

Enterprise (20+ engineers):

  • Training: Databricks or custom GPU clusters
  • Serving: vLLM on Kubernetes with autoscaling
  • Tracking: W&B enterprise
  • Full observability with evidently AI for drift detection

Cost Optimization Tips

  1. Use spot/spot-like instances for non-time-critical training. RunPod offers preemptible GPUs at 40-60% off on-demand pricing.
  2. Quantize when you can. GGUF and GPTQ models run on cheaper hardware without meaningful quality loss for most inference workloads.
  3. Cache aggressively. Modal and vLLM both support request caching — hit it and you save real money.
  4. Right-size your GPU. You don’t need an A100 for every task. T4s handle inference for models under 13B parameters just fine.

The Bottom Line

The MLOps stack isn’t about picking the most expensive tool — it’s about picking tools that let your models reach production without a six-figure runway. Start light, measure everything, and invest in the layers that actually move your business metrics.