Best Serverless GPU Platforms for LLM Inference (2026)

Fireworks AI is the inference platform I reach for first when serving stock open-source LLMs in production. Built by ex-Meta PyTorch engineers, it pairs an OpenAI-compatible API with a proprietary CUDA inference engine called FireAttention that delivers up to 4x faster throughput than vanilla vLLM on the same hardware — and that speed advantage shows up directly in user-facing latency for chat applications.

For LLM inference specifically, Fireworks shines because it gets the boring-but-critical things right: zero cold starts on its 200+ pre-hosted models (Llama 3.3, DeepSeek V3, Mixtral, Qwen), native function calling and JSON-mode for agentic workflows, and HIPAA-eligible deployments for regulated industries. LoRA fine-tunes share base-model GPUs at no extra cost, so you can ship dozens of customer-specific variants without paying for dozens of dedicated instances.

The trade-off is that the inference engine is closed-source — if you ever need to migrate, your prompts will travel but your performance optimizations won't. For most teams shipping LLM features, that's an acceptable bet for the speed and DX gains.

Modal is the best serverless GPU platform if you need to deploy custom LLM inference logic — fine-tuned models, novel architectures, multi-step pipelines that combine an LLM with embeddings and reranking, or agentic systems that spawn sandboxed subprocesses. It's Python-native to a degree no other platform matches: you decorate a function with @app.function(gpu="H100"), push, and you have a scaling HTTPS endpoint backed by serverless H100s within seconds.

For LLM inference workloads, Modal's killer feature is its custom container runtime, which delivers sub-second cold starts even for multi-GB model weights via aggressive snapshotting. That makes it viable to run smaller models (7B-13B) with scale-to-zero economics — something most competitors can't match because their cold starts are measured in tens of seconds. Network volumes and distributed dictionaries let you cache embeddings, KV-cache state, or model weights across invocations without external Redis.

The Python-only constraint is real, but if your stack is already Python-first (and most ML stacks are), it's a non-issue. Pricing is per-second of actual compute used, so idle costs are zero — you genuinely don't pay between requests.

Baseten is a production-first inference platform used by companies like Descript, Patreon, and Writer for mission-critical LLM endpoints. Where Modal optimizes for developer experience, Baseten optimizes for SLA-grade reliability — dedicated deployments with reserved GPU capacity, predictable autoscaling, and zero-downtime model rollouts that prevent the request drops you'd get on pure scale-to-zero platforms.

For LLM inference, Baseten's performance engineering is its differentiator. The platform applies TensorRT-LLM optimizations, FP8 quantization, speculative decoding, and custom CUDA kernels on your behalf, often delivering 2-3x throughput improvements over a naive vLLM deployment with no code changes. Its open-source Truss packaging format means your deployment artifact is portable — you can reproduce it locally or migrate elsewhere — which is rare among serverless GPU vendors.

The \u002430/month minimum and dedicated-deployment focus make it a poor fit for hobbyists or one-off experiments, but for teams running LLM inference as a core product feature, Baseten's reliability story is the strongest in this list.

Together AI is Fireworks's closest competitor in the token-based serverless inference space. It hosts 200+ open-source models with an OpenAI-compatible API, supports LoRA fine-tuning with auto-deployment, and runs on its own optimized inference stack that delivers competitive throughput to FireAttention. For many workloads the performance gap is small enough that pricing and model availability become the deciding factors.

For LLM inference specifically, Together's strengths are model breadth (it often has new open releases live within hours) and its dedicated endpoints for guaranteed throughput at high volume. The Together Code Interpreter and Together Embeddings round out a stack that can serve full RAG and agentic workloads from a single vendor. Pricing is generally a hair lower than Fireworks for the same models, which adds up at scale.

The weakness is that cold starts on less-popular models can be longer than Fireworks (which keeps a wider warm pool), and the API surface for advanced features like structured output isn't quite as polished. For most stock-model use cases, you can A/B test Together against Fireworks on a free tier and pick whichever wins on your specific prompts.

RunPod is the most cost-efficient choice when you need raw serverless GPU capacity and don't mind a less polished developer experience. Its Serverless product offers pay-per-request billing with FlashBoot cold starts measured in milliseconds (on warm pools), and its pricing is consistently 30-50% lower than equivalent capacity on Modal or Baseten. The catch: more of the inference stack is your responsibility.

For LLM inference, RunPod works best when you bring a vLLM or TGI worker template (the marketplace has dozens of pre-built ones for popular models) and tune it yourself. Pre-configured templates for Llama, Mistral, and Stable Diffusion let you go from zero to a serverless inference endpoint in under 10 minutes. Per-second billing and zero ingress/egress fees mean batch inference jobs are dramatically cheaper than equivalent runs on AWS or GCP.

The trade-offs are a steeper learning curve (you'll touch Docker, env vars, and worker handler code), occasional capacity constraints in popular regions, and a less-mature observability story than Baseten or Modal. For cost-sensitive teams or batch workloads, those trade-offs are usually worth it.

Replicate takes a different angle than the rest of this list: it's optimized for running any model behind a simple API with minimal setup, not for high-throughput production LLM serving. Its Cog framework lets you containerize any Python model with a couple of YAML lines, and the platform handles versioning, autoscaling, and the API surface for you. For prototyping and low-to-medium volume LLM workloads, that simplicity is a real advantage.

For LLM inference, Replicate's strengths are model variety (it has the largest catalog of community-deployed models, especially for image and audio generation alongside LLMs) and its predictable per-second billing. Cold starts on less-popular models can be slow (15-60 seconds) because Replicate doesn't aggressively pre-warm long-tail models, but pre-warmed popular models like Llama 3 respond quickly.

It's the right pick when you want to ship a prototype LLM feature in an afternoon, or when you need access to obscure community-fine-tuned models that no other platform hosts. For high-volume, latency-sensitive production traffic, the four platforms above will serve you better.