Vercel vs Netlify vs Railway: Best Platform for AI App Deployment

TL;DR

• Vercel: Best for Next.js + AI, edge functions, fastest deploys ($20/month Pro, $0/Hobby)
• Netlify: Best for JAMstack + AI, strong build plugins, generous free tier ($19/month Pro, $0/Starter)
• Railway: Best for long-running AI workloads, background jobs, flexible infra ($5/month + usage)

Feature comparison

Vercel

Best for: Next.js AI applications, edge-deployed chatbots, streaming responses

Strengths:

• Tightest Next.js integration (same company)
• Edge Functions for <50ms latency worldwide
• Streaming responses (perfect for LLM output)
• Excellent developer experience (preview deployments)
• Built-in analytics and speed insights

Weaknesses:

• Serverless timeout (60s Pro, 300s Enterprise)
• Expensive for high bandwidth ($40/100GB overage)
• Vendor lock-in to Next.js ecosystem
• No persistent processes (serverless only)

AI-specific limitations:

• Large model inference (>60s) requires external service
• Vector database must be external (no persistent storage)
• Background jobs need separate queue system

Use cases:

• AI chatbots with streaming responses
• Next.js + OpenAI API applications
• Edge-deployed RAG systems
• Customer-facing AI interfaces

Verdict: 4.5/5 - Best for Next.js + AI, but serverless limits constrain complex workflows.

Netlify

Best for: Static AI frontends, build-time AI generation, JAMstack + AI

Strengths:

• Most generous free tier (100GB bandwidth, 300 build minutes)
• Excellent build plugin ecosystem
• Background Functions (up to 960s timeout)
• Split testing and edge functions
• Strong Git integration

Weaknesses:

• Slower cold starts than Vercel
• Less optimized for Next.js
• Background Functions only on Pro+ ($19/month)
• Smaller community than Vercel

AI-specific capabilities:

• Background Functions good for batch embeddings
• Build plugins for AI-generated content
• Edge Functions for lightweight inference

Use cases:

• AI-generated static content (blogs, docs)
• Marketing sites with AI features
• Prototype AI applications (free tier)
• JAMstack + AI hybrid

Verdict: 4.2/5 - Excellent free tier, good for AI-enhanced static sites, less ideal for heavy AI workloads.

Railway

Best for: Long-running AI agents, background processing, stateful applications

Strengths:

• No serverless timeouts (run processes indefinitely)
• Persistent storage (volumes)
• WebSocket support (real-time AI)
• Docker support (any stack)
• Simple pricing (pay for resources used)

Weaknesses:

• No edge deployment (single region)
• Cold starts for unused services
• Requires more DevOps knowledge
• Smaller ecosystem than Vercel/Netlify

AI-specific strengths:

• Perfect for agent workflows (hours/days runtime)
• Can run local LLMs (Ollama, llama.cpp)
• Background job queues (BullMQ, Celery)
• Persistent vector databases (pgvector, Qdrant)

Use cases:

• Multi-agent systems with long workflows
• Fine-tuning pipelines
• Self-hosted LLMs
• Background embedding generation

Verdict: 4.6/5 - Best for complex AI workloads requiring persistent processes.

Pricing comparison

Scenario: AI chatbot with 50K requests/month, 10GB bandwidth, 5 hours compute

Vercel:

• Hobby: Free (if within limits)
• Pro: $20/month (100GB bandwidth included)
• Estimated: $20/month (Pro for team features)

Netlify:

• Starter: Free (if within limits)
• Pro: $19/month (1TB bandwidth)
• Estimated: $19/month (Pro for Background Functions)

Railway:

• $5 included credit + usage
• Compute: 2 vCPU × $0.000463/min × 300min = $0.28/month
• Memory: 2GB × $0.000231/GB/min × 300min = $0.14/month
• Estimated: $5.42/month (within free credit)

Scenario 2: AI agent with 24/7 processing, 100GB storage, 4GB RAM

Vercel: Not possible (serverless timeouts)

Netlify: Not ideal (Background Functions expensive at scale)

Railway:

• Compute: $0.000463/min × 43,200min = $20/month
• Memory: 4GB × $0.000231/GB/min × 43,200min = $40/month
• Storage: 100GB × $0.25/GB = $25/month
• Estimated: $85/month

Winner: Railway for complex workloads, Vercel/Netlify for simple chatbots.

Performance: Cold starts

Tested with Next.js app calling OpenAI API (1536 token response):

Winner: Vercel Edge for latency-sensitive AI applications.

Deployment speed

Time from git push to live deployment:

Winner: Vercel for fastest deploys.

AI-specific features

Vercel

• Edge Functions: Deploy lightweight AI (embeddings, classification) globally
• Streaming: Native support for LLM streaming responses
• Image Optimization: Great for AI-generated images
• Analytics: Built-in monitoring (track AI response times)

Netlify

• Background Functions: Long-running embeddings, batch processing
• Build Plugins: AI content generation at build time
• Split Testing: A/B test AI prompt variations
• Edge Functions: Lightweight AI at edge

Railway

• Persistent Storage: Store fine-tuned models, vector databases
• WebSockets: Real-time agent communication
• Cron Jobs: Scheduled AI tasks (daily embeddings refresh)
• Multi-service: Run LLM + vector DB + API in one project

Use case recommendations

Choose Vercel if:

• Building Next.js + AI application
• Need streaming LLM responses
• Want fastest edge deployment
• Latency critical (<100ms)

Choose Netlify if:

• AI-enhanced static site
• Need generous free tier for prototyping
• Want build-time AI generation
• Background processing occasional (not 24/7)

Choose Railway if:

• Long-running AI agents (hours/days)
• Self-hosted LLMs or vector databases
• WebSocket-based AI interfaces
• Need persistent storage for models

Real-world example

At Athenic, we use multi-platform approach:

• Vercel: Customer-facing chatbot (Next.js, streaming responses)
• Railway: Multi-agent orchestration (24/7 processing, pgvector database)
• Netlify: Marketing site with AI-generated blog content

Lesson: Match platform to workload characteristics, not "one platform for everything."

Expert quote (Lee Robinson, VP of Product at Vercel): "Edge Functions excel for quick AI tasks -think embeddings, classification, routing. Long-running agents need traditional servers or serverless with extended timeouts."

Migration complexity

Vercel ↔ Netlify: Easy (1-2 days)

• Both support Next.js
• Update environment variables
• Change build commands

Vercel/Netlify → Railway: Moderate (3-5 days)

• Refactor serverless → persistent processes
• Setup Docker configuration
• Migrate environment secrets

Railway → Vercel/Netlify: Hard (1-2 weeks)

• Break long processes into serverless chunks
• External queue for background jobs
• External database required

FAQs

Can I run local LLMs on these platforms?

Railway: Yes (with sufficient resources). Vercel/Netlify: No (serverless timeouts too short).

Which has best Next.js support?

Vercel (same company), but Netlify also excellent. Railway requires manual Next.js setup.

What about GPU support for AI?

None offer native GPU. Use external services (Modal, Replicate, RunPod) for GPU inference.

Can I host vector databases?

Railway: Yes (persistent storage). Vercel/Netlify: No (external DB like Pinecone/Supabase required).

Which is most cost-effective?

Railway for 24/7 workloads. Vercel/Netlify for bursty traffic. All have generous free tiers for starting.

Summary

Vercel best for Next.js AI applications with streaming responses and edge deployment. Railway best for long-running AI agents, background processing, and self-hosted infrastructure. Netlify best for AI-enhanced static sites and generous free tier. Most production AI apps benefit from multi-platform approach: Vercel/Netlify for frontend, Railway for backend agents.

Winner: Vercel for customer-facing AI, Railway for complex AI workloads.

Internal links:

• /blog/multi-agent-orchestration-implementation-guide
• /blog/production-rag-systems-complete-guide

External references:

• Vercel Documentation
• Netlify Documentation
• Railway Documentation