When it came to adding AI to a product a year ago, it was a matter of calling an API and hoping latency wouldn’t be an issue. It’s not just that anymore. AI workloads have changed the way developers consider cloud infrastructure itself, whether it’s in terms of billing, deployment or monitoring. So if you are creating a chatbot, recommendation engine, or autonomous agent, your choices about the cloud will impact your costs and architecture for years to come.
Compute is no longer simply CPU and RAM
Until recently, provisioning meant choosing an instance size and that was it. Not now with AI. Now, GPU access is a first-class infrastructure decision and it’s not an easy one to make. Because supply is tight. High-end chips such as the H100 and newer B200 have been waitlisted at various providers for weeks, with prices fluctuating based on who has chips available that day.
For developers, this means that they can either deploy raw GPU clouds to run their own serving stack or use a managed inference platform and simply send a request and receive a response without touching the hardware. Now, there is no one-size-fits-all answer. Raw compute is low cost at scale, but requires engineering to make it run well. Managed inference will get you to production quicker, but is more expensive per token when you scale up. In the end, most teams compromise and use managed APIs for prototyping and raw GPU access once traffic has stabilized to the point where the overhead is justified.
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Serverless Has a New Job
Until now, serverless has been synonymous with web functions and basic backend logic. Now, it’s also how many AI inferences are served. Serverless GPU platforms automatically scale compute resources up and down as requests are received and when there is no activity. Thus, there is no need to pay for an idle H100 sitting between requests.
This is crucial as the AI traffic might be spikey, different from normal web traffic. A feature could get 5 requests per hour during the day and 500 requests in 10 minutes when the marketing mail comes out. Serverless inference is good for such traffic. The compromise is cold start time. However, when users are waiting for a response from your model, the delay will be noticeable if your model takes a few seconds to load on a new GPU. Testing for cold starts in realistic load conditions and not just checking off a spec sheet number, should be a key consideration when developing serverless inference.
Cost Tracking Needs a Different Viewpoint
Cloud cost optimization used to be about right-sizing instances. Pick the smallest virtual machine to do the job, reserve capacity if you can foresee it and move on. AI inferences don’t work like that. A single pipeline can spin up hundreds of GPU instances in an hour and tear them back down quickly. So the tag-based cost allocation that worked well for a web app can break down when applied to more workload.
The workaround is to consider the cost of inferences as a separate line item from the cost of infrastructure. Record all the inference calls with sufficient metadata (model version, number of tokens, latency) to gain insights into where money is being spent. When teams do not do this, they often discover that their AI feature is not profitable after it has been scaled.
Agentic Workloads Change the Shape of Your Architecture
A basic chatbot makes one call to a model and returns a response. An agent does not do things like that. It may call a model, use a tool, call the model again with new context, check a database and repeat that process several times until it’s finished. It’s a long-running, stateful process and it doesn’t fit nicely in the infrastructure that is optimized for fast request-response cycles.
When creating agentic functionality, expect some latency (a simple query could take 2 seconds or a complex one could take 30 seconds), increased memory consumption with longer context windows and strict cost management. If there is no limit on how many times an agent can run, then one runaway agent loop can quickly exhaust a GPU budget. This is not a theoretical risk, but an actual one that should be put in place before shipping the feature, not after that.
Platform Engineering is Doing More Heavy Lifting
With increasingly complex AI infrastructure, it’s becoming unrealistic for every application developer to be an expert in GPU provisioning, inference routing and observability apart from their primary responsibilities. This is one of the reasons for the increase in platform engineering. Data access and observability provisioning and deployment is now a self-service tool on internal developer platforms. This enables application teams to make infrastructure requests without needing to be infrastructure experts themselves.
As AI infrastructure becomes more complex, developers also need to strengthen the broader IT and software skills that help them work effectively across cloud platforms, deployment tools, and emerging technologies.
Infrastructure decisions for AI should be made by the platform team, not the product teams. It will make ad hoc decisions on which GPU provider to use. What this means for how you build
There are some handy “tips” to get into your system, no matter what cloud stack you choose.
- Do not choose an architecture based on test results from inference latency in an artificial traffic pattern.
- Break out AI cost tracking from overall cloud spend to see the ROI of what you’re creating.
- From the beginning, create cost and rate restrictions in anything agentic.
- Don’t consider the selection of the GPU provider or inference platform a one-time decision.
- Prices and availability in this space fluctuate quite frequently and what may have been a great deal six months ago may not be so great today.
FAQs:
- Do I have to work with GPUs directly or can I continue to use managed inference APIs?
Managed APIs typically are the best place to start. They will help you get to production more quickly and eliminate infrastructure work. Only migrate to raw GPU access when your traffic is stable enough to justify the cost savings over the engineering time required to operate your own serving stack.
- Why are the costs for serverless GPUs so different among providers?
The answer is the availability of GPUs, what GPUs the provider has and what they do with idle GPUs. Some charge per second and scale to zero and others charge in longer minimum increments. Always verify actual rates with expected load. Don’t rely on the headline rate.
- What are the largest pitfall teams can fall into with AI features in their infrastructure?
The largest pitfalls are failure to distinguish between AI inference expenses and overall cloud expenses. When a team realizes a feature is costly, it’s usually already in production and is difficult to back out without a redesign.
Conclusion
This doesn’t mean that every developer should be an infrastructure specialist. But when it comes to AI, the difference between “developing a feature” and “making an infrastructure decision” is becoming more uncertain. GPU access, serverless inferences and cost tracking are all part of the job now, not an optional context.
Many organizations also rely on cloud consulting services to evaluate GPU providers, streamline AI deployments, and implement governance without overburdening internal development teams. The teams here aren’t successful because they are looking for the latest chip or the lowest hourly wage. They’re the ones that test under real conditions, track spend honestly and build guardrails before something breaks in production, not after.
