Best Platforms for Deploying LLM Apps

Bottom Line Up Front

Stop reading comparison articles that end with “it depends.” Here’s what actually wins:

• Modal — best for GPU workloads, fine-tuning pipelines, and anything that needs cold-start-aware serverless. The pricing model is the most honest in the industry.
• Fly.io — best general-purpose platform for LLM apps that need persistent connections, WebSockets, or always-on inference. Fast deploys, no cold start drama.
• Railway — best for solo developers and small teams who want near-zero ops overhead with a reasonable price ceiling.
• AWS (ECS + ALB) — best for teams that already live in AWS and need to integrate LLM inference with existing production infrastructure.
• Cloud Run (GCP) — best when you need serverless that can actually handle bursty traffic without paying for idle compute.
• Vercel — best if your LLM app is primarily edge-adjacent AI features (summarization, classification, streaming chat) bolted onto a Next.js frontend. Not a general-purpose LLM host.

Everything else is situational. The detailed breakdown follows.

What Makes LLM Deployment Different

Deploying an LLM app is not like deploying a CRUD API. The constraints that matter are:

• Cold start latency — LLMs are heavy. A 7B model in half-precision is 14GB. Even with a preloaded container, spinning up inference from zero takes 10–30 seconds. That’s not acceptable for interactive products.
• GPU availability and cost — NVIDIA A10G, A100, H100. These are scarce and expensive. Most platforms either don’t offer them, ration them, or charge 3x market rate.
• Streaming — Virtually every LLM product streams tokens. Serverless platforms that kill connections at 30 seconds (Lambda’s default) or buffer responses before sending them will break your UX.
• Memory and concurrency — LLM inference is memory-bound. A single model instance might handle 8–32 concurrent requests depending on batch size and model size. Your platform needs to let you right-size this.
• Model loading time — Downloading a model from S3 or HuggingFace at container start adds minutes. Platforms with persistent volumes or model caching matter a lot here.

If you’re using a hosted API (OpenAI, Anthropic, Gemini), most of these constraints shift — you’re now mostly deploying a thin orchestration layer. But if you’re running your own inference, the platform choice is make-or-break.

Platform-by-Platform Breakdown

Modal

Best for: GPU inference, fine-tuning jobs, batch pipelines

Modal is the most purpose-built platform for ML workloads. The key insight is that Modal inverts the traditional deployment model: instead of defining infrastructure and deploying code into it, you annotate Python functions with hardware requirements and Modal handles everything else.

@app.function(gpu="A100", image=image, timeout=300)
def run_inference(prompt: str) -> str:
    ...

What sets it apart:

• Cold starts are fast by ML standards (~2–5s for containers with cached layers, not 30s+)
• GPU types: T4, A10G, A100, H100 — all available on-demand
• Built-in model caching via volumes — load a 70B model once, keep it warm
• Scales to zero between jobs, so batch workloads cost nothing when idle
• Web endpoints support streaming out of the box

Pricing: Pay per second of compute. A100 80GB is ~$2.50/hr, H100 is ~$3.95/hr. Substantially cheaper than equivalent Lambda Labs or CoreWeave if you’re not running 24/7.

Limitations: Not ideal for latency-sensitive always-on APIs because cold starts, while faster than most, still exist. For production inference serving, you’ll want to keep at least one container warm, which means paying for idle time.

Fly.io

Best for: Always-on inference APIs, WebSocket-heavy apps, global edge deployment

Fly.io runs actual VMs (Firecracker microVMs) close to your users, in ~35 regions. Unlike serverless platforms, your containers stay running — no cold starts, persistent connections, WebSocket support, and full control over your networking.

What sets it apart:

• Machines start in ~300ms (VM wake from suspend) vs 10s+ for cold serverless
• Persistent volumes for model weights — load once, serve forever
• fly proxy and private networking make multi-service LLM architectures clean
• Autoscaling that actually works: scale to minimum 1 machine, not 0
• GPU support (A10, L40S, A100) in select regions — being deprecated after July 2026

Pricing: VMs billed per second. A 4-core/8GB machine is ~$0.14/hr. GPUs add ~$1.50/hr for L40S. A small LLM API at modest traffic runs $50–150/month.

Limitations: GPU support is being deprecated after July 31, 2026, making Fly a CPU-only platform going forward. Fly is excellent for CPU-bound LLM apps (OpenAI/Anthropic API wrappers, RAG pipelines, prompt chains) but can’t match Modal for GPU-heavy workloads.

Railway

Best for: Fast iteration, small teams, side projects with real traffic

Railway is the closest thing to “Heroku if it were built in 2024.” Connect a GitHub repo, define your service, deploy. It handles Docker builds, environment variables, persistent volumes, and private networking with minimal configuration.

What sets it apart:

• The fastest path from code to running service in the industry
• Template marketplace with working LLM app starters
• Reasonable $5/month Hobby plan or $20/month Pro plan for solo developers
• Built-in Postgres, Redis, and other services in the same private network

Pricing: Usage-based from $0 (Free tier with $1 credit) or $5/month (Hobby plan). Most small LLM apps (wrapping OpenAI, lightweight RAG) run $10–40/month. The $20/month Pro plan covers most use cases.

Limitations: No GPU support. Not designed for inference at scale. If you’re hitting 1000+ requests/minute, you’ll want something with more control over autoscaling behavior. Railway is a starting point, not an endgame.

Render

Best for: Teams stepping up from Railway but not ready for AWS

Render occupies the same space as Railway but with more enterprise-adjacent features: DDoS protection, private services, SSH access to running instances, and a cleaner approach to background workers.

What sets it apart:

• Auto-deploy from Git with zero-downtime deploys
• Persistent disks with reasonable pricing
• Private networking between services
• Good support for long-running background jobs (model downloads, batch inference)

Pricing: Starter instances at $7/month, standard at $25/month. Competitive for compute, but costs add up fast when you add databases and background workers.

Limitations: Still no GPU support. Cold starts on free tier are brutal (up to 60 seconds). Paid plans stay warm but you’re paying for that idle compute.

AWS (Lambda + ECS + SageMaker)

Best for: Teams already in AWS, production-grade reliability requirements

AWS gives you the most complete infrastructure toolkit, at the cost of the most complete operational overhead. The right AWS service depends heavily on your workload:

Lambda — Good for lightweight LLM endpoints (API wrappers, prompt classification) with the 15-minute timeout and up to 10GB RAM. Terrible for anything requiring GPU or persistent model loading. Cold starts with large Python dependencies hit 5–15 seconds even with provisioned concurrency. Streaming support via Lambda Function URLs works but adds complexity.

ECS (Fargate or EC2) — The right choice for containerized LLM inference at scale. Run your vLLM or TGI inference server in a container, behind an ALB with target group scaling. EC2-backed ECS gives you GPU instances (g4dn, p3, p4d). Fargate gives you simpler ops but no GPU support yet.

SageMaker — Designed specifically for ML inference. Supports real-time endpoints, batch transform, and async inference. Managed model hosting with built-in autoscaling. The DLC (Deep Learning Container) images pre-install everything you need. The downside: SageMaker is expensive for small-scale deployments and has significant learning curve.

Pricing: Lambda is cheap for bursty workloads. ECS with EC2 GPU instances (g4dn.xlarge at ~$0.526/hr) is cost-competitive. SageMaker adds a 30–40% markup over raw EC2 for managed endpoints.

Google Cloud Run

Best for: Serverless that needs to handle real traffic spikes

Cloud Run is the most capable serverless container platform for LLM workloads that don’t require GPUs. It addresses the core serverless failure mode for LLM apps — cold starts — with minimum instance configuration and startup probes.

What sets it apart:

• GPU support landed in 2025 (L4), with RTX PRO 6000 Blackwell added in 2026 — A100 is not available on Cloud Run
• Startup CPU boost: allocates extra CPU during container startup to reduce cold start
• Always-on minimum instances keep your inference API warm at low cost
• Generous request timeout (60 minutes) handles long-running inference jobs
• Built-in streaming response support

Pricing: Per 100ms compute billing. A 4-core/8GB minimum instance runs ~$70/month always-on. GPU instances are competitive with AWS equivalents.

Limitations: GPU availability is improving but still spotty. RTX PRO 6000 Blackwell GPUs are available for larger workloads, but options remain limited compared to dedicated GPU platforms. For CPU inference and lightweight GPU workloads, it’s a strong serverless option.

Vercel

Best for: AI features embedded in Next.js apps

Vercel’s AI SDK is genuinely excellent for building streaming chat UIs, running edge-compatible AI features, and integrating LLM calls into existing Next.js applications. The ai package handles streaming, tool calling, and provider switching with a clean abstraction.

What sets it apart:

• AI SDK is the best DX for building LLM-powered React apps
• Edge functions run globally with sub-50ms routing latency
• Fluid compute (2024) reduces cold start impact for serverless functions
• Native integration with OpenAI, Anthropic, and other hosted providers

Pricing: Pro plan at $20/month for most apps. Compute adds up quickly at scale — serverless function invocations are billed per execution.

Limitations: Vercel is not an inference platform. You’re always calling a hosted LLM API — Vercel handles the orchestration and UI layer, not the model. Function timeout is 300 seconds on Pro (5 minutes), which is enough for most use cases but constraining for agentic workloads. No GPU support whatsoever.

Comparison Table

Recommendation Matrix

You’re building a chatbot UI wrapping OpenAI/Anthropic: Use Vercel (for Next.js) or Railway (for any stack). You don’t need GPU. Pick what gets you deployed fastest.

You’re running open-source inference (Llama, Mistral, Qwen): Start with Modal for the simplest path to GPU. Graduate to ECS on EC2 when you need predictable latency and are running > $1K/month in Modal compute.

You need always-on, low-latency inference API: Fly.io or ECS. Both keep containers running. Fly.io is simpler; ECS scales higher and gives you more instance types.

You’re running batch jobs (fine-tuning, embedding pipelines, evaluation runs): Modal wins here, it’s not close. Scale-to-zero serverless GPU with job queuing built in.

You have existing AWS infrastructure: Lean into ECS + ECR. Don’t introduce a new platform for the LLM layer unless you have a specific reason to. SageMaker is worth the learning curve if you need autoscaling managed endpoints.

You’re a solo developer, validating a product: Railway. Ship fast, pay almost nothing until you have users.

You need global edge deployment with AI features: Vercel AI SDK + Cloud Run for inference backend. Edge handles routing and UI; Cloud Run handles the actual model calls.

Things Nobody Talks About

Model caching is the hidden performance lever. If your inference container downloads a 7B model from HuggingFace on every cold start, you’re adding 3–10 minutes to your cold start time. Use Modal volumes, Fly.io persistent storage, or ECS with an EFS mount to pre-cache model weights. This single change eliminates most cold start complaints.

Streaming requires full-duplex HTTP support. Not all reverse proxies and load balancers pass through chunked transfer encoding correctly. AWS ALB handles it fine. Some CDN configurations will buffer your entire response before sending — test this explicitly.

vLLM and TGI are not the same. If you’re self-hosting inference, vLLM is faster for most workloads due to PagedAttention. TGI has better multi-model serving support. Both are production-grade. Don’t roll your own inference server unless you have a specific reason.

Egress costs will surprise you. On AWS and GCP, data transfer out is ~$0.08–0.09/GB. An LLM app generating 1M tokens/day at ~4 chars/token is sending ~4GB of text. That’s $300/month in egress before you’ve paid for compute. Fly.io and Railway have more generous egress policies.

The Short Version

For most LLM apps in 2026, the deployment stack looks like this:

• Inference: Modal (GPU) or Fly.io (always-on CPU)
• Orchestration layer: Railway or Cloud Run
• Frontend + streaming: Vercel (Next.js) or any static host
• At scale: AWS ECS + EC2 GPU instances, with SageMaker for managed endpoints

Don’t over-engineer early. Start with Railway or Modal, get real traffic data, then make infrastructure decisions based on actual bottlenecks — not anticipated ones.