Best Modal Alternatives in 2026

Lambda Labs provides GPU cloud compute for AI researchers and machine learning engineers: on-demand H100 and B200 instances with PyTorch pre-installed, and 1-Click Clusters with InfiniBand networking for large-scale distributed training.

BentoML is an open-source Python framework for packaging and deploying machine learning models as production APIs. It supports any ML framework, includes OpenLLM for self-hosted LLM serving, and offers BentoCloud for managed inference with autoscaling and BYOC.

Northflank is a full-stack PaaS founded in London in 2019 that lets teams deploy containers, managed databases, GPU inference workloads, and AI sandboxes on Northflank-managed cloud or their own AWS, GCP, and Azure accounts. Built for teams who want Kubernetes power without writing YAML.

Beam Cloud is a serverless GPU platform that lets Python developers deploy AI inference endpoints and background jobs using simple decorators. Pay per second on T4 through H100 hardware, scale to zero when idle, and optionally self-host via the open-source beta9 runtime.

Anyscale is a managed Ray cluster platform built by the UC Berkeley team that created Ray. It handles distributed ML training, batch inference, fine-tuning, and model serving across multi-cloud GPU clusters, with no infrastructure management required.

RunPod is a GPU cloud platform with on-demand and spot instances from $0.17/hr, serverless inference endpoints with sub-200ms cold starts, and a template library covering Stable Diffusion, Llama, ComfyUI, and more. Built for AI developers who need flexible GPU access without hyperscaler pricing.

LiteLLM is an open-source AI gateway by BerriAI (YC W23) that translates 100+ LLM provider APIs into a single OpenAI-compatible endpoint. Self-host the proxy server to route across OpenAI, Anthropic, Bedrock, and more with spend tracking, rate limiting, and fallback routing built in.

Axolotl is a free, open-source LLM fine-tuning framework that lets ML teams run reproducible LoRA, QLoRA, and GRPO training runs on 100+ model architectures using a single YAML config file across multi-GPU setups.

Unsloth is an open-source fine-tuning library by Daniel and Michael Han that delivers 2x faster LLM training and up to 70% less VRAM through custom Triton CUDA kernels. Supports 500+ models including Llama, Qwen, and DeepSeek. Free to use, with a paid Pro tier for multi-GPU training.

Vast.ai is a peer-to-peer GPU rental marketplace where independent hosts list compute at market rates. H100s from ~$0.90/hr, RTX 4090s from $0.34/hr. Far cheaper than managed clouds, with tradeoffs on reliability and setup complexity.