Every AI infrastructure guide seems to assume you have a dedicated platform team, a seven-figure cloud budget, and a direct line to your GPU vendor. That's great if you're OpenAI. It's less helpful if you're a 20-person startup trying to ship your first ML feature before your Series A runway disappears.
The conversation around AI infrastructure has become dominated by hyperscale thinking. We hear about massive training clusters, petabytes of data, and infrastructure teams with headcounts larger than most startups. Meanwhile, the rest of us are left wondering how to move a model from a Jupyter notebook to production without burning through our entire cloud budget in a month.
Here's the thing: you don't need Google-scale infrastructure to deploy production ML workloads. You need smart infrastructure choices, a willingness to challenge assumptions, and a clear understanding of where your money actually goes.
AI infrastructure spending surged 166% year-over-year in 2025 as organizations moved from experimental pilots to production deployments. The market is projected to reach $87.6 billion this year alone, growing to nearly $200 billion by 2030. These numbers tell a story of explosive growth, but they obscure a more important reality: most teams aren't prepared for where the money actually goes.
When people think about AI infrastructure costs, they think about GPUs. And yes, compute is expensive. But the real budget killers are often hiding in plain sight. Data preparation consumes enormous resources before a single model is trained. Model maintenance and retraining create ongoing operational overhead that compounds over time. Integration work, change management, and the engineering effort required to make ML systems play nicely with existing infrastructure often dwarf the compute costs that get all the attention.
Many teams discover their existing systems can't keep up only when transitioning from pilot to production. That proof-of-concept that ran beautifully on a single GPU suddenly needs to handle real traffic, real data volumes, and real availability requirements. Bandwidth-related issues jumped from affecting 32% of AI teams to 53% in just one year, and 82% of AI teams now report performance slowdowns due to infrastructure bottlenecks.
The traditional approaches don't help much here. Hyperscaler ML platforms like SageMaker and Vertex AI offer powerful capabilities, but they come with complexity and cost structures designed for enterprise budgets. Building everything yourself gives you control but demands expertise most teams don't have. By 2026, the most in-demand AI jobs won't be model developers; they'll be infrastructure experts. That talent gap is real, and it's expensive.
One of the most expensive assumptions in AI infrastructure is that you need massive resources from day one. This thinking conflates two very different workloads: training and inference.
Training is where the GPU hunger comes from. It's compute-intensive, memory-hungry, and often benefits from parallelization across multiple accelerators. But training is also inherently bursty. You train a model, evaluate it, iterate, and train again. Unless you're running continuous retraining pipelines, those expensive GPU instances sit idle most of the time.
Inference is different. When you're serving predictions, the computational profile changes dramatically. Many models run efficiently on CPUs, especially with optimizations like quantization or distillation. Even models that benefit from GPU acceleration often don't need the same hardware you used for training. A model trained on an A100 might serve inference beautifully on a much cheaper T4.
The right-sizing mindset means treating these workloads differently. For training, consider spot instances or reserved capacity that you can spin up when needed and release when you're done. Cloud providers offer significant discounts for interruptible workloads, and training jobs that checkpoint properly can tolerate interruptions gracefully. For inference, start lean. Profile your actual latency and throughput requirements before assuming you need dedicated GPUs. Many production ML features run on standard compute with response times that users never notice. This approach requires discipline. It's tempting to over-provision because debugging resource constraints is painful. But the teams that ship AI features sustainably are the ones that right-size from the start and scale intentionally based on actual demand.
Containerization has become the foundation of modern ML deployment for good reason. Containers give you reproducibility (the model that worked in development works the same way in production), portability (you're not locked into a specific cloud provider's ML service), and scalability (orchestration platforms handle the complexity of running multiple instances).
The practical architecture for most teams separates training infrastructure from inference infrastructure. Training environments can be ephemeral, spun up when needed with access to appropriate GPU resources, then torn down when the job completes. Inference infrastructure needs to be durable, scalable, and integrated with your application's existing deployment patterns.
For inference workloads, raw Kubernetes offers maximum flexibility but demands significant expertise to operate well. Most teams don't need to manage ingress controllers, service meshes, and node pools directly. They need to deploy a containerized model, expose an endpoint, and scale based on traffic. This is where platform abstractions earn their keep.
A PaaS that supports custom node groups (including GPU instances when you actually need them) can be the sweet spot between managed ML platforms and raw infrastructure. Platforms like Convox, Render, or Railway abstract away Kubernetes complexity while still giving you container-based deployment with the ability to specify resource requirements, configure auto-scaling, and manage environment variables without writing Helm charts. The key question isn't whether to use abstraction; it's finding the right level of abstraction for your team's capabilities and requirements. If you have a dedicated platform team with deep Kubernetes expertise, raw infrastructure might make sense. If your ML engineers should be focused on models rather than infrastructure, a platform that handles the operational complexity is probably the better investment.
Building an internal ML platform is a common aspiration and a common failure mode. The dream is appealing: a custom platform tailored to your specific needs, with exactly the features your team requires and none of the compromises of off-the-shelf solutions. The reality is that platform engineering is expensive, time-consuming, and requires ongoing investment to maintain.
Organizations increasingly recognize that platform-as-a-product thinking applies to their infrastructure choices. You don't build your own database; you use Postgres. You don't build your own message queue; you use Redis or RabbitMQ. The same logic applies to deployment platforms. Unless deployment infrastructure is your core competency, buying (or using open-source solutions) beats building.
The goal is getting 80% of the benefit with 20% of the effort. A good platform abstraction handles the operational complexity of running containers in production: health checks, rolling deployments, auto-scaling, log aggregation, and SSL termination. These are solved problems. Solving them again for your specific use case rarely creates competitive advantage.
What matters is having a migration path. Starting with managed infrastructure makes sense when you're moving fast and validating product-market fit. But you want the option to graduate to self-hosted infrastructure when your scale, compliance requirements, or cost structure demands it. The BYOC (Bring Your Own Cloud) model, where a platform deploys into your own cloud account rather than running on shared infrastructure, offers this flexibility. You get operational simplicity today with the option to take more control tomorrow.
72% of IT leaders cite AI skills as a crucial gap needing urgent attention. Until that gap closes, leaning on platforms that encode operational best practices into their abstractions is a pragmatic choice.
If you're moving a model from prototype to production, here's a practical path forward.
First, containerize your model. This means writing a Dockerfile that packages your model, its dependencies, and a serving layer (Flask, FastAPI, or a dedicated serving framework like TensorFlow Serving or Triton). The container should expose an HTTP endpoint that accepts prediction requests and returns results. This is your deployment unit, and it should work identically on your laptop and in production.
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY model/ ./model/
COPY serve.py .
EXPOSE 8000
CMD ["uvicorn", "serve:app", "--host", "0.0.0.0", "--port", "8000"]Second, choose inference-appropriate compute. Profile your model's resource requirements under realistic load. Many teams default to GPU instances because that's what they used for training, only to discover that CPU inference meets their latency requirements at a fraction of the cost. Start with the smallest instance that works and scale up based on measured performance, not assumptions.
Third, implement auto-scaling tied to actual demand. Your inference service should scale horizontally based on request volume or resource utilization. Configure scaling policies that add capacity when needed and, equally important, remove capacity when demand drops. The ability to scale to zero during low-traffic periods can dramatically reduce costs for services with variable load patterns.
services:
inference:
build: .
port: 8000
scale:
count: 1-10
cpu: 256
memory: 512
targets:
cpu: 70Fourth, build observability from day one. You need to know how your model is performing in production: latency distributions, error rates, and resource utilization. You also need to track model-specific metrics like prediction distributions and feature drift. Instrumenting these early saves painful debugging later when something goes wrong.
Fifth, plan for cost visibility and optimization. Tag your resources, set up billing alerts, and review your infrastructure spend regularly. The teams that control AI infrastructure costs aren't the ones with the biggest budgets; they're the ones that understand where their money goes and make intentional tradeoffs.
The goal of AI infrastructure isn't to compete with Big Tech's compute capacity. It's to ship AI features that create value for your users and your business. The teams winning in this space aren't those with the biggest budgets or the most sophisticated infrastructure. They're the teams making smart choices about where to invest their limited resources.
Start small. A single containerized model serving predictions through a simple API is a real ML feature in production. It might not be glamorous, but it's shipping value while you learn what your actual infrastructure requirements look like under real conditions.
Stay lean. Question every assumption about what you need. That expensive GPU instance, the complex orchestration layer, the managed ML platform with features you'll never use: each one should earn its place in your architecture based on demonstrated need, not anticipated requirements.
Scale intentionally. When you need more capacity, add it deliberately based on measured demand. When you need specialized hardware, provision it for the specific workloads that require it. The infrastructure that serves you well at 1,000 predictions per day should evolve gracefully to serve you at 1,000,000, not be replaced wholesale because you over-built from the start.
AI infrastructure for the 99% isn't about cutting corners or accepting inferior capabilities. It's about recognizing that the path to production ML doesn't require Google-scale resources. It requires clear thinking, practical architecture choices, and the discipline to right-size your infrastructure to your actual needs. The tools exist. The platforms exist. The only question is whether you'll use them to ship something that matters.
If you want to see what deploying ML workloads without the infrastructure overhead looks like in practice, Convox offers a Getting Started Guide that walks through installation and your first deployment. There's also a video series if you prefer to follow along visually.
For teams evaluating infrastructure options, the scaling documentation covers auto-scaling configuration and GPU support for services that need it. If you're running mixed workloads or need dedicated GPU nodes for specific services, the workload placement guide explains how to configure custom node groups and direct ML workloads to appropriate infrastructure. You can also explore example applications in various frameworks to see how containerized deployments fit your stack.
Console accounts are free, and you can create your first Rack in your own cloud account in minutes. Questions? Connect with other developers at community.convox.com.
