Alternatives to Kalavai

Rent per-second GPU pods, autoscaling serverless inference endpoints and multi-node clusters on Runpod's AI developer cloud.

Rent GPUs on Vast.ai's marketplace — 20,000+ cards, per-second billing, and API-native provisioning agents can drive.

Parallax is a distributed model serving framework for building your own self-hosted AI cluster across scattered GPUs, Macs, and

Self-hosted control plane that turns your own GPUs into OpenAI- and Anthropic-compatible model endpoints

Distributed compute clusters across hyperscalers and neoclouds for massively parallel AI workloads

MatrixHub is a self-hosted AI model registry that gives vLLM and SGLang clusters a private Hugging Face replacement.

CoreWeave is the AI-native GPU cloud for training, reinforcement learning post-training, and low-latency inference on NVIDIA cluster-scale

Lilac turns idle GPU clusters into a white-labeled neocloud with spot H100 leases and serverless inference.

Salad Cloud rents idle consumer GPUs from $0.015 per GPU-hour, billed per second, for retryable inference and batch work.

Run 20+ pre-installed open-source AI apps — ComfyUI, Stable Diffusion, RVC, LoRA training — in your browser on rented cloud GPUs

Mesh LLM splits large open-weight models into layer ranges across your own GPUs and serves them through one OpenAI-compatible endpoint at localhost:9337.

European GPU cloud with self-service NVIDIA GB300, B300 and H200 instances plus serverless GPU inference

Nscale sells sovereign GPU cloud capacity — reserved compute, inference endpoints, and AI data centers it owns and powers.

KubeAI is a Kubernetes operator for self-hosted LLM, embedding, reranking, and speech-to-text inference with an OpenAI-compatible API.

Per-second-billed cloud GPUs (H100, A100, A6000, V100) with 1-click Jupyter notebooks, model deployment, and distributed training in beta.

Run fast, self-hosted inference for LLMs, vision-language and diffusion models with SGLang serving.

Self-hosted embedding inference in your AWS VPC with sub-20ms latency and no rate limits.

DistriFusion splits SDXL image generation across multiple GPUs for up to 6.1x lower latency at high resolution — no retraining required.

Serverless GPU cloud for production AI — declare Python functions and hardware in one file and pay per second.

Managed Ray platform for distributed training, multimodal data curation, batch embedding, and post-training on your own GPUs

Run fast, production-grade GPU inference on dedicated GPUs or token-priced Model APIs — with per-minute billing.

Cerebras inference cloud: frontier LLMs at up to 750 tokens/sec for real-time agents and voice AI.

Run open-weight model inference on Together AI — serverless APIs, batch jobs, and GPU clusters.

MAX Engine serves open models through one OpenAI-compatible endpoint across NVIDIA, AMD, Apple silicon, CPUs, and ASICs.

Open-source LLM fine-tuning framework for MoE, LoRA, and GRPO runs on your own GPUs

Serverless APIs for 200+ open-weight LLM, image, audio, video and vision models, plus GPU cloud and an agent sandbox.

Serverless GPU compute with forkable microVMs and millisecond-billed sandboxes for AI agents.

TensorRT-LLM is NVIDIA's open-source inference library for high-throughput LLM and visual generation serving on NVIDIA GPUs.

vLLM turns open-weight LLMs into high-throughput, OpenAI-compatible inference endpoints on your own GPUs.

Deploy your own ML models on serverless Nvidia GPUs, billed per second with scale-to-zero.