Designing and deploying AI infrastructure in the cloud is no longer a niche challenge. Developers, startups, and enterprises all face the same questions: which cloud, which GPUs, and how do you keep it reliable without burning budget?
This guide breaks down what a modern AI infrastructure stack needs, compares cloud options, and outlines reference architectures that scale.
What does good AI infrastructure look like?
A robust setup should cover:
- Compute: managed foundation models or self-hosted open models
- Networking: private connectivity and strong IAM controls
- Inference: servers that autoscale under load
- Observability: track latency, tokens, and cost per request
- Data layer: secure storage and vector databases with governance
- MLOps: CI/CD for models, rollback paths, and experiment tracking
Hyperscalers vs GPU-specialist clouds
Hyperscalers (AWS, GCP, Azure)
Pros:
- Tight integration with identity, networking, and compliance
- Managed model catalogs and private access endpoints
- Built-in safety and governance features
Good fit if you need enterprise governance and don’t want to manage runtimes.
Specialist GPU Clouds (RunPod, CoreWeave, Lambda)
Pros:
- Lower GPU cost per hour
- Direct control over kernels, libraries, and serving stack
- Bring your own container with vLLM, Triton, or custom stack
Good fit if you want control, flexibility, and cost efficiency.
Also See: Deploying Hugging Face LLMs on RunPod
The GPU cost reality check
- Capex model: H100 cards and DGX servers are prohibitively expensive for most teams.
- Cloud model: On-demand GPU pricing is more accessible, especially with spot or burst capacity.
- Mix model: Combine reserved capacity for steady workloads with on-demand pools for bursts.
Always measure in $/token instead of GPU hours. Track tokens in/out and optimize per workload.
Reference Architectures
1. Managed-model, private access
- Hyperscaler models served in your VPC
- Autoscaling handled by the provider
- Safety and governance included
Why: fastest time-to-value with enterprise networking.
2. Self-hosted open models
- RunPod or similar GPU cloud
- Inference stack with vLLM or Triton
- Private endpoints and VPN back to your network
- Your own observability with Prometheus/OpenTelemetry
Why: maximum flexibility and performance tuning.
3. Hybrid approach
- Control plane in a hyperscaler
- Data plane across hyperscaler endpoints and GPU specialist clusters
- Policy-based routing to pick best cost/performance option
Why: keeps optionality as models, prices, and features evolve.
Decision Framework
- Workload shape: latency-critical chat vs batch summarization
- Data sensitivity: regulated workloads → private endpoints, customer-managed keys
- Model strategy: managed models vs open weights for portability
- Cost posture: opex-only (on-demand) vs steady scale (reserved + mix)
Building Blocks
- Serving: vLLM, Triton, TensorRT-LLM
- Retrieval: vector DB + cache hot embeddings
- Pipelines: queues for batch, orchestrators for agents
- Networking: VPC peering, segmentation
- Safety: PII filters, jailbreak detection, content guardrails
Recommended by maturity
Pilot
- Use managed models with private endpoints
- Minimal code, built-in safety
Production v1
- Add a dedicated inference cluster on GPU cloud
- Secure data with private networking and encryption
Scale-out
- Policy-based routing across multiple providers
- Mix reserved and on-demand GPU pools
- Continuous evaluation of new models
Key Takeaways
- Start with managed models if you need speed and compliance.
- Use GPU-specialist clouds if you need cost control and flexibility.
- Keep a hybrid option ready to hedge against rapid vendor and model shifts.
At Scalevise, we help companies design and implement cloud AI infrastructures that scale without waste. From IAM and VPC design to GPU orchestration and observability dashboards, we blueprint the entire path to production.
👉 Looking to set up AI infrastructure for your team? Get in touch with Scalevise.
Top comments (12)
Why even bother with RunPod or CoreWeave when AWS gives you everything in one place?
If you’re fine with hyperscaler pricing and lock-in, then sure, AWS covers it all. But once workloads scale, specialist GPU clouds can cut costs by 30–50%. For teams with budget pressure, that difference matters.
On-prem is still the only sane option for regulated industries. Clouds change APIs every year.
On-prem makes sense for some, but it’s not always realistic. Hardware refresh, cooling, and ops staff add up fast. For many, a private cloud setup with strict networking and customer-managed keys achieves compliance without owning racks.
I get that, but regulators don’t care about “customer-managed keys” if the infrastructure is still outside your control. Once auditors step in, they’ll push for physical data residency. How do you convince them a GPU cloud is compliant?
That’s exactly where governance comes in. You need documented controls: where data is stored, how it’s encrypted, who has access, and how logs prove that. In practice, we’ve seen regulators accept GPU cloud setups if workloads run in-region, data never leaves the VPC, and compliance frameworks (ISO, SOC, GDPR) are mapped. It’s not trivial, but it’s possible with the right architecture.
Our team started with managed models on Vertex AI, then moved some heavy batch jobs to a GPU cloud. The hybrid approach really does make sense once traffic grows.
That’s the sweet spot: start managed, then offload heavy jobs where it’s cheaper. Keeps both compliance and cost under control.
Great article, thank you!
You're welcome!
We tested L40S for background jobs and it was perfect. Way cheaper than H100s for workloads that don’t need low latency.
Exactly!! not every task needs the top GPU. Mixing tiers is one of the simplest ways to save costs without hurting performance where it matters.