The landscape of AI development has shifted rapidly. Developers aren’t just building models; they’re deploying, scaling, and running them with minimal infrastructure overhead. Serverless inference has become a practical choice for many, removing the weight of manual server management and letting the code speak for itself.

With that shift, three new players have entered the field—Hyperbolic, Nebius AI Studio, and Novita. Each offers a fresh take on deploying machine learning models, focusing on speed, cost efficiency, and adaptability. This article examines what sets each apart and how they might fit into today's evolving workflows.

Hyperbolic: Flexible Inference for Custom Workloads

Hyperbolic steps in with a clear mission—to make serverless inference as adaptable as possible. It's built for teams that want control without spending their time managing servers. Rather than locking users into predefined compute configurations or specific cloud vendors, Hyperbolic offers a flexible deployment model. This includes support for multiple frameworks and custom containers, which makes it especially appealing for teams that train models in-house and need consistent behavior during inference.

One standout feature is its event-driven execution. Models spin up on request, scale with load, and shut down when idle. Billing is tied strictly to active usage, which keeps costs low for applications with unpredictable traffic. Hyperbolic’s dashboard is clean and focused. It shows memory use, execution time, and model input/output logs without clutter. That helps teams monitor performance and debug issues without going through layers of abstraction.

It also supports GPU-backed inference, but rather than leaving GPUs running; it relies on short bursts—models load, compute, and go back to sleep. This is useful for natural language tasks or image recognition models that need more power but not continuously. Hyperbolic's design encourages efficient use, which can cut cloud bills dramatically for many workloads.

Nebius AI Studio: Built-In Collaboration Meets Scalable Inference

Nebius AI Studio, backed by the cloud platform Nebius, takes a slightly different approach. While it provides serverless inference capabilities, its strength lies in combining model deployment with collaborative development. The studio combines notebooks, dataset versioning, and deployment tools in a single environment. That appeals to research teams or startups that want an all-in-one workspace.

Its inference service is integrated deeply into the studio. Once a model is trained, users can deploy it directly without exporting it to another environment. It handles versioning automatically, and developers can test endpoints within the same interface, speeding up the cycle from training to production.

Another strong point is its focus on security and compliance. Nebius AI Studio includes private endpoints, audit logs, and user role management—features often left out of lighter platforms. That makes it a good match for companies in regulated industries, such as healthcare or finance, where data control matters as much as speed.

Performance-wise, Nebius offers CPU and GPU inference, with autoscaling based on traffic. The serverless design removes the need for pre-provisioning resources. Their pricing is linear and based on request duration and memory usage. That simplicity makes it easier to predict costs as the workload scales.

Novita: Lightweight, Developer-Friendly, and Cost-Focused

Novita enters with a different pitch: keep inference light and developer-centric. It doesn’t aim to be a full AI platform. Instead, it offers a clean and minimal layer to serve models efficiently without overhead. Novita’s philosophy is that not every AI use case needs complex orchestration or enterprise-grade tooling. For many startups or indie developers, simplicity wins.

Setting up an endpoint on Novita takes minutes. Upload a model, select runtime options, and get a REST or RPC endpoint. The service handles cold starts well, keeping latency low even for less frequent calls. Novita also supports small pre-built runtimes tuned for popular frameworks like PyTorch, TensorFlow, and ONNX. That keeps the environment lightweight and fast to boot up.

What sets Novita apart is its cost control. It allows users to set hard usage limits, with detailed tracking of calls, errors, and resource use. That appeals to budget-conscious users who don't want surprises at the end of the month. Their free tier includes generous usage, making it a good playground for early-stage projects or proof-of-concept deployments.

Despite its simplicity, Novita supports multiple regions and edge deployment. Models can be served closer to the user, cutting latency for global applications. Its documentation is clear and example-heavy and assumes minimal prior setup knowledge, making it accessible to developers who might be new to serverless infrastructure.

Choosing the Right Tool for the Job

Each provider fills a different gap in the growing ecosystem of serverless inference. Hyperbolic is strong for custom models and users who want control without infrastructure overhead. It supports dynamic workloads and is well-suited to teams already building in-house pipelines.

Nebius AI Studio is better for integrated workflows, where training, testing, and deploying all happen under one roof. It appeals to organizations that care about collaboration, versioning, and governance—without sacrificing performance.

Novita is ideal for developers who want to move fast without high costs. It strips away complexity and focuses on low-latency, low-cost inference. Its edge deployment and strong developer experience make it attractive for smaller teams.

All three take advantage of the core promise of serverless inference: don't pay when you're not using it, and scale automatically when you are. They abstract away provisioning, scaling, and environment management, letting teams focus on building better models and shipping them faster. But how they approach that promise—through flexibility, integration, or simplicity—offers choices that didn't exist just a year ago.

Conclusion

Serverless inference has matured from a niche concept to a practical solution. Hyperbolic, Nebius AI Studio, and Novita each bring something different—adaptability, collaborative development, or developer-first design. As AI workloads diversify, these new platforms help fill efficiency, usability, and cost control gaps. Choosing the right one depends not just on features but on the shape of your workflow and the scale of your ambitions. With these options, teams can focus less on servers and more on serving better results.