Key Takeaways
- Match infrastructure to the training method: LoRA and QLoRA can fit on smaller GPUs than full-parameter fine-tuning, while large models and distributed runs require more memory, faster interconnects, and durable checkpoints.
- Developer experience affects iteration speed: Code-first SDKs, reproducible images, persistent volumes, and integrated secrets reduce the operational work between a local experiment and a remote GPU run.
- Serverless economics suit variable workloads: On-demand compute avoids paying for idle GPUs between experiments, but teams should compare storage, egress, startup, and minimum-worker charges as well as the GPU-second rate.
- Training and serving belong in one workflow: The strongest platforms let teams fine-tune, save adapters or checkpoints, evaluate results, and deploy the custom model without rebuilding infrastructure.
- Security is part of platform selection: Regulated teams should assess isolation, encryption, access controls, auditability, data residency, and relevant assurances such as SOC 2 Type II and HIPAA support.
1. Modal: Code-First Serverless GPUs for AI Workloads and LLM Fine-Tuning
Modal provides code-first infrastructure for training and fine-tuning open and custom models. Developers define the image, GPU, storage, secrets, and execution behavior alongside Python code, while Modal handles container builds, scheduling, and capacity across a multi-cloud GPU pool. This makes it practical to move from a local training script to an on-demand job without maintaining a cluster or YAML-based infrastructure definitions.
Why Modal Excels for Fine-Tuning LLMs
Fine-tuning is iterative: teams repeatedly adjust datasets, hyperparameters, adapters, and evaluation logic. Modal supports that loop with Volumes for datasets and checkpoints, Secrets for protected credentials, and functions that request the required GPU directly in code. The same project can run LoRA or QLoRA experiments, full fine-tunes, evaluation jobs, and production inference, reducing handoffs between separate training and serving systems.
Modal's GPU Offerings and Developer Experience
Modal offers elastic access to B300, B200, H200, H100, RTX PRO 6000, A100, L40S, A10, L4, T4, and other GPU types. Teams can select hardware based on model size and memory needs, use multiple GPUs, or build multi-node training jobs with high-performance networking. The Python decorator model, including @app.function, keeps resource definitions close to training code, and Images make environments reproducible without requiring every team to maintain Dockerfiles.
2. Beam Cloud: Serverless GPU for AI/ML Model Deployment and Fine-Tuning
Beam Cloud combines a Python-native serverless experience with an open-source runtime, beta9, and bring-your-own-cloud deployment options. It is suited to teams that want to run GPU training code on managed infrastructure or retain more control by deploying into an existing cloud account.
How Beam Cloud Simplifies Fine-Tuning
Beam lets developers package Python functions and containers as remote GPU workloads, so a fine-tuning script can run without manually provisioning an instance. Per-second billing and scale-to-zero can fit intermittent experiments, while persistent storage is important for keeping datasets, adapters, and checkpoints outside an ephemeral worker. Teams should validate the GPU types and distributed-training topology available for their specific model before committing to large runs.
Beam's Architecture for AI Workloads
Beam's open-source beta9 runtime and BYOC support across AWS, GCP, Azure, and Hetzner distinguish it from fully closed managed platforms. The architecture gives teams a path to place workloads closer to governed data or existing cloud services while retaining serverless abstractions. Its managed cloud option offers per-second billing and scale-to-zero, and warm-pool controls are more relevant when the resulting model moves from training into latency-sensitive serving.
3. Cerebrium: High-Performance Serverless GPUs for Advanced AI Applications
Cerebrium is a production-oriented AI infrastructure platform with a Python SDK, multiple GPU types, per-second active-compute billing, scale-to-zero, and built-in observability. It fits teams that want one environment for custom model workloads and production deployment controls.
Cerebrium for Fine-Tuning and Serving Custom Models
Developers can package fine-tuning code and dependencies into reproducible deployments, attach storage for model artifacts, and expose a trained model as an endpoint after evaluation. Cerebrium's production focus, including autoscaling and monitoring, is useful when a fine-tuned model must move beyond an experiment into a maintained service. GPU choice, storage throughput, and checkpoint recovery should be tested with the intended model rather than inferred from endpoint benchmarks.
Supported Frameworks and Integrations
Containerized Python workloads can use common ML frameworks such as PyTorch, TensorFlow, JAX, Hugging Face Transformers, and custom CUDA dependencies when compatible with the selected runtime and drivers. Cerebrium announced completion of its SOC 2 Type II audit in July 2026, a relevant assurance for enterprise reviews, though buyers should still assess tenancy, networking, data location, and access-control requirements.
4. Koyeb: Global Serverless Platform for Modern Application and GPU Hosting
Koyeb combines a PaaS deployment model with GPU compute, Git and Docker-based deployments, application versioning, rollback, and a global edge network. It is primarily attractive to teams that value straightforward application hosting and want to place a trained model behind a globally reachable service.
Koyeb's Serverless Architecture for AI
Koyeb runs applications in core regions while edge locations route users to the nearest core region. GPU work therefore executes in supported core regions rather than at every edge point. GPU scale-to-zero is in public preview, so teams should confirm current availability and operational limits before designing a production training pipeline around it.
Deploying Fine-Tuned Models with Koyeb
A team can package a fine-tuned model and inference server in Docker, push it to a registry, and deploy that artifact through Koyeb's PaaS workflow. Zero-downtime deployment and rollback help when promoting new adapter or checkpoint versions. On February 17, 2026, Koyeb announced a definitive agreement to join Mistral AI and said the platform would continue operating while gradually becoming a core part of Mistral Compute, an important roadmap consideration for buyers.
5. Nebius: Scalable Serverless Infrastructure for Data-Intensive GPU Workloads
Nebius introduced container-based Serverless AI Jobs and Endpoints in public preview on March 26, 2026. Jobs address finite batch-style workloads, while Endpoints serve models, giving teams separate primitives for training-related tasks and deployment within the broader Nebius AI Cloud.
Nebius's Approach to Serverless GPUs
Nebius runs user-supplied public or custom container images and charges pay-as-you-go compute while workloads run. This supports portable training environments and avoids a proprietary function format. At launch, Jobs were described as single-node workloads under the service terms, while Endpoint autoscaling, startup optimization, and multi-region scheduling appeared on a Q4 2026 and later roadmap; large distributed fine-tunes therefore require checking the current product state or using broader Nebius AI Cloud infrastructure.
Use Cases Beyond Traditional ML
The same job abstraction can execute data preprocessing, synthetic-data generation, evaluation, media processing, and other GPU-accelerated batch tasks. That breadth matters because fine-tuning pipelines often spend substantial time preparing and validating data before the optimizer runs. Nebius operates AI Cloud infrastructure in Europe and the US, but teams should verify the regions and residency guarantees attached to the exact serverless service they select.
6. Google Cloud Run: Serverless Containers for Scalable GPU-Powered ML
Google Cloud Run extends the familiar serverless-container model to GPU-backed services and jobs. It can be a practical option for teams already using Google Cloud storage, networking, identity, observability, and artifact tooling, especially when they want to run containerized ML code without administering Kubernetes.
Integrating GPUs with Cloud Run for Fine-Tuning
Teams can build a training image with PyTorch, TensorFlow, Hugging Face, or another framework, store datasets and checkpoints in Google Cloud services, and run the container as a Cloud Run job where supported GPU resources and execution limits fit the workload. Cloud Run is better matched to bounded, containerized fine-tunes than to tightly coupled multi-node training; model size, job duration, GPU availability, quota, and regional support should be validated before choosing it for an LLM pipeline.
Benefits of Google Cloud's Serverless Offering
Cloud Run's main advantage is integration with the wider Google Cloud environment: IAM, Artifact Registry, Cloud Logging, networking, and managed data services can reduce the number of new systems an existing GCP team must adopt. The tradeoff is that general-purpose serverless container controls may expose fewer AI-specific training primitives than a platform built expressly around model development.
7. RunPod Serverless: On-Demand GPU for Fine-Tuning and High-Performance ML
RunPod provides broad on-demand GPU access through Pods, Serverless, and cluster-oriented products. Its hardware selection and global footprint make it useful for teams that want flexibility across training, batch processing, and model deployment, but the correct product depends on whether a run needs a persistent machine, a request-driven worker, or distributed infrastructure.
RunPod's Serverless Approach to GPU Access
RunPod Serverless executes custom Docker workers and bills GPU compute on demand, with scale-to-zero and controls for active or warm workers. FlashBoot targets faster startup on snapshot-hit paths. For fine-tuning, teams must also account for persistent network storage, which RunPod bills separately from GPU compute, and ensure checkpoints survive worker termination.
Deploying and Managing Models on RunPod
A custom image can include the training framework, dependencies, and worker handler, while external or persistent storage holds data and model artifacts. After training, teams can deploy the resulting model through Serverless or use another RunPod product for a continuously available endpoint. RunPod's REST API v2 entered public beta on August 13, 2026, covering Pods, Serverless, storage, templates, registries, hardware catalog, and billing, while legacy GraphQL remained available.
Why Modal Stands Out for Serverless GPU Fine-Tuning
Modal combines AI-specific infrastructure, code-defined execution, broad GPU access, durable data primitives, and a path from fine-tuning to inference. That combination is especially useful for teams that want fast experimentation without creating a separate operations stack for every stage of the model lifecycle.
GPU Memory Snapshots Accelerate Iteration
Modal's GPU Memory Snapshots, currently an Alpha feature, capture initialized CPU and GPU state for compatible Functions. They target repeated initialization work such as imports, framework setup, JIT compilation, and warm-up rather than replacing durable training checkpoints. Modal has reported up to 10x improvements in selected startup examples, which can shorten feedback loops around evaluation or serving stages adjacent to fine-tuning.
Code-First Development Experience
Modal supports code-defined infrastructure through SDKs in Python, TypeScript, and Go, while containers can run the language or runtime a workload requires. In Python, @app.function places GPU and resource requirements beside application code, and common environments can be built in Python. Integrated secrets, Volumes, logs, and scheduled jobs keep training projects cohesive.
Free Credits and Startup Programs
New users receive $30 in free compute to test a workflow before committing budget. Eligible startups can also apply for Modal startup credits. These programs lower the cost of validating dataset loading, framework compatibility, checkpointing, and deployment on a small run before scaling to larger GPUs or distributed training.
Enterprise-Ready Security
Modal has completed a SOC 2 Type II audit with no deviations found and supports HIPAA-compliant workloads on Enterprise plans via a BAA. Its security architecture includes gVisor-based compute isolation, TLS 1.3 for public APIs, encryption in transit and at rest, and role-based access control at Workspace and Environment levels on Team and Enterprise plans. AWS and Google Cloud Marketplace availability can also simplify procurement using committed cloud spend.
