You've built a model. Now you need to run it in production — fast, reliably, and without signing your life away on a 12-month reservation you're not ready for. That's the reality for most AI teams right now: inference workloads are bursty, requirements shift month to month, and locking into a long-term contract before you know your actual usage patterns is a real financial risk.

Good news: the market has moved in your favor. The global AI inference PaaS market was valued at $18.84 billion in 2025 and is forecast to hit $105 billion by 2030, according to MarketsandMarkets — and the competition is driving pricing down fast. A March 2026 Gartner report predicts inference costs for a trillion-parameter LLM will fall over 90% by 2030. That means providers are racing to offer flexible access, and there are solid contract-free options right now.

So what should you actually look for, and where are the best places to find it?

The three contract-free models worth knowing

Not all "no contract" compute is the same. Before you start comparing providers, it helps to understand the three main formats:

On-demand GPU instances let you spin up a GPU at a per-hour (or per-minute) rate with no minimum term. You pay for what you use, scale up or down when you need, and walk away any time. This is the closest equivalent to what your team controls directly.

Model as a Service (MaaS) skips the infrastructure layer entirely. You call an API, get a response, and pay per token or per request. You never manage a GPU; you just consume inference. This is the right fit when your team doesn't need custom model control.

Serverless / spot GPU is the cheapest path, but comes with a caveat: capacity isn't guaranteed, and your workload can be preempted. Fine for batch jobs or non-critical inference; riskier for real-time production.

Most teams running production inference will mix these — on-demand GPUs for steady workloads, MaaS for rapid prototyping or secondary model calls, and serverless for cost-sensitive background tasks.

Where to find flexible AI compute in 2026

Providers built for developers

Platforms like RunPod, Vast.ai, and Together AI have built their entire business model around pay-as-you-go access. RunPod offers on-demand GPU pods and a serverless endpoint product with no hourly minimums. Vast.ai uses a real-time marketplace model with per-second billing, which can get H100 instances down to competitive rates depending on availability. Together AI leans into the MaaS side, offering serverless inference on dozens of popular open models with transparent per-token pricing.

These options work well for teams in the US or Europe with bursty workloads and no hard compliance requirements. But if you're running systems in Vietnam or Thailand — or need to keep data within a specific regional boundary — they don't really solve your problem. Latency from a US data center, plus data sovereignty questions, adds friction that technical pricing alone doesn't offset.

Hyperscalers: flexible but expensive

AWS, Azure, and Google Cloud all offer on-demand GPU access with no long-term contract required. The flexibility is real. The pricing isn't as competitive: as of early 2026, H100 8-GPU instances on AWS run roughly $55–60/hour, compared to $2–4/hour per GPU on specialized providers, according to CloudZero's April 2026 analysis. You're also paying for global infrastructure you may not need.

For teams in Southeast Asia, latency from US or EU regions on inference calls adds meaningful delay. That's not a problem you solve with pricing.

Regional GPU cloud: the case for staying closer to your users

This is where GreenNode stands apart for teams building in Vietnam and Thailand. GreenNode's GPU instances are available across 6 availability zones — Ho Chi Minh City, Hanoi, and Bangkok — with no long-term contract required. You go from signup to a running instance in under 5 minutes, paying only for what you use. GPU options range from the RTX 4090 (starting at $610/month, great for mid-scale inference and cost-sensitive tasks) to the NVIDIA H100 for production-scale LLM serving.

For teams that don't want to manage GPU infrastructure at all, GreenNode's Model as a Service gives you a library of 20+ ready-to-use models accessible via API — no reserved capacity, no idle cost. It's a practical path for running inference on popular models without provisioning anything. If you want more on why MaaS is gaining traction as a deployment pattern, the guide on accelerating AI value with MaaS is worth a read.

The compliance angle matters here too. If you're in financial services or e-commerce in Vietnam, keeping workloads on locally anchored infrastructure isn't just a preference — it's increasingly a requirement. GreenNode's regional presence directly addresses that. For a deeper look at what this means in practice, the blog on AI deployment and regulatory compliance covers the specifics of Vietnam's evolving AI legislation.

When to switch from on-demand to reserved

Contract-free doesn't mean you should never commit. If your team is running inference at high, predictable utilization — say, 60%+ of a GPU's capacity around the clock — reserved pricing will usually save you money. The break-even math isn't complicated: compare your average monthly on-demand spend against what a reserved commitment would cost, factor in your confidence that usage won't drop, and make the call.

The right time to consider a contract is after you've seen at least 60–90 days of real inference volume. Running on-demand first is how you get that data without overpaying for capacity you don't use.

What to check before picking a provider

A few things worth validating before you commit even to on-demand usage:

Region availability — can you deploy in or near your users? Inference latency is a user experience issue, not just a technical one.
GPU options for inference — not every workload needs an H100. For LLM inference, an A40 or L40S often delivers better cost-per-token.
Support when things go wrong — on-demand doesn't mean unsupported. 24/7 regional support matters when a production system goes down at 2am.
Compliance posture — check whether data residency, SLAs, and security certifications match your requirements.

GreenNode's AI Platform is also worth exploring if you want to consolidate model training, fine-tuning, and inference deployment in one place — still without a lock-in requirement.

The right provider for contract-free AI inference isn't necessarily the cheapest one on a per-hour basis. It's the one that eliminates the most friction for your specific workload, in your specific region, at the compliance standard your business needs. Start there, run a real workload, and scale what works.