As more companies build AI-powered products, deploying models reliably, scalably, affordably and quickly has become a top priority. For most teams, the default choice is to reach for AWS or Google Cloud—but that decision can often come with hidden costs and unnecessary complexity. Whether you’re a lean startup or a growing AI-native company, it’s worth exploring the growing list of alternatives that better the type of AI workloads you are looking to run.

The High Cost of Traditional Cloud AI Deployments

While AWS and GCP provide robust infrastructure, a variety of services such as databases, storage buckets and global reach, they require a large amount of setup. You have to stitch together multiple services, setup observability, deal with limited supply and quota limits on GPUs as well as very slow iteration cycles for your engineering team!

Below we summarise some of the hidden costs that you can expect with traditional clouds that you should consider before making a decision:

• Idle GPU Time: Since GPU instances take a long time to startup (~2 minutes) you are forced to run them 24/7 in latency sensitive applications, meaning you are billed for the entire GPU instance even when it’s not in use.

• Over-Provisioning: Without smart autoscaling, you often need to reserve extra capacity “just in case.” leading to idle GPU costs again and low system utilisation.

• GPU Supply: There is a limited supply of high end GPU chips (H200, H100, etc) leading you to reserve more than you need or you might not be able to handle spikes in demand because you run out of capacity.

• DevOps Overhead: Setting up Kubernetes clusters, autoscaling, routing, observability, and security layers can take weeks as well as the consistent maintenance that is required. Additionally, the cost of having a team manage and maintenance this is is expensive when you could rather spend that money on moving your product forward.

• Slow Debugging Cycles: Experimenting and testing on GPU’s typically requires pushing docker images which can take ~10 minutes for every change you do!

Of course, there are times where AWS and GCP are a suitable choice:

• When you have consistent, predictable traffic

• Workloads that are not latency sensitive

• You have a committed cloud spend or startup credits

What to Consider When Choosing an AI Infrastructure Provider

No single platform fits every AI workload. Your optimal setup depends on the use case—whether you’re serving LLMs in production, running image classification at the edge, or enabling real-time voice agents.

Here are the four key factors to weigh:

🔒 Security & Compliance

• Is customer data sensitive (healthcare, finance)?

• Do you need deployment within a customer’s VPC or in a specific geographic region?

⚡ Performance & Latency

• Do users expect sub-second responses?

• Will your workload spike unpredictably?

💰 Cost Efficiency

• Can you benefit from spot or serverless GPU pricing?

• Do you need to scale to zero when idle?

🛠️ Ease of Use

• Does your team want to avoid infrastructure management?

• How fast can you iterate and debug?

Notable Alternatives to AWS and GCP for AI Model Deployment

Serverless AI Infrastructure (Cerebrium, Baseten, Runpod)

These platforms abstract away infrastructure management but still give you complete control over the models you deploy.

• Why it matters: You get serverless scalability and ease of use, combined with the flexibility to bring your own models, optimize performance, and tune deployments to your needs. Additionally, you only pay for the compute you use!

• Ideal for: Teams building production-grade AI applications that need both customization and operational simplicity, such as real-time agents, LLM apps, or image generation tools.

NEO Clouds (Nebius, CoreWeave, Lambda Labs)

These are modern cloud platforms that offer infrastructure similar to AWS and GCP, but with pricing and performance optimized for AI workloads.

• Why it matters: You get access to powerful GPUs (like A100s and H100s) with more transparent pricing as well as optimisations when it comes to storage and running Kubernetes.

• Ideal for: Teams running large-scale training or inference workloads that still want full control over their infrastructure but without the premium pricing of legacy cloud providers. I would recommend this approach if you have very predictable traffic or don’t running a latency sensitive application.

API-Based Model Hosting (Replicate, Fal, OpenRouter)

These platforms let you call open-source models via simple APIs—no infrastructure setup required.

• Why it matters: You can experiment and build prototypes in minutes without worrying about deployments or GPUs.

• Ideal for: Use cases where customization isn’t critical, and low cost and speed to launch are the priorities—e.g., using a pre-trained SDXL, Whisper, or LLaMA model.

Why Cerebrium is Emerging as a Top Choice

Cerebrium is a serverless infrastructure platform purpose-built for running high-performance data and AI workloads—whether it’s powering globally distributed voice agents, fine-tuning models, or executing large-scale data processing.

Built out of frustration with slow performance, high cloud costs, and heavy DevOps overhead, Cerebrium was created by engineers for engineers who want to create AI products. It strikes the perfect balance between flexibility and abstraction: giving developers full control over how their models run, while abstracting away the complexity they don’t want to manage.

Cerebrium excels for applications with high performance, low-latency and volatile traffic use cases which is why it is used by fast-growing companies from Seed stage to Series C such as Tavus, Vapi, Daily and many more.

🚀 Cerebrium’s Advantages:

• Serverless CPU/GPU Inference: With cold start times of 2-4 seconds, its the most performant serverless platform on the market.

• Global Footprint: Route inference through the closest region with native data residency in the US, EU and South-East Asia with more regions coming soon.

• GPU variety: Pick from over 12 GPU chips such as H200, H100, A100, L40s, and many more.

• Pay only for what you use: Cerebrium charges you only for the resources you use.

• Developer Experience: Cerebrium pushes your code live on CPU/GPUs within 2-3 seconds making debugging and iteration extremely quick. Additionally, our response time to support tickets is <1 hour.

• Other: Storage, real-time logging, REST API’s

When to Use Cerebrium:

• You’re running high performance, low latency applications

• You want to avoid provisioning and DevOps and want your engineering team to move on product.

• You need global deployments for low latency and data residency compliance.

• Your traffic is highly volatile and you need are cost sensitive.

Conclusion

AWS and GCP will always be part of the AI ecosystem and are a great choice for a very specific set of requirements—but they are no longer the only option. Today, more focused, efficient, and developer-friendly platforms like Cerebrium offer compelling alternatives that help teams move faster, scale smarter, and cut infrastructure costs without compromise.

If you’re building the next wave of AI products, it’s worth taking a serious look at what modern AI-first infrastructure can do for you.