The recent $100 million funding round for Railway—a platform that grew to two million developers without traditional marketing—signals something important: the infrastructure layer is shifting beneath AI applications. Legacy cloud providers weren’t built for the deployment patterns that modern AI demands. If your team is running inference endpoints, managing GPU resources, or deploying AI-powered features, this guide will help you evaluate and execute a migration to infrastructure designed for these workloads.
Understanding Why Legacy Cloud Falls Short for AI
Traditional cloud infrastructure was architected for a different era. AWS, Azure, and GCP built their foundations around web servers, databases, and batch processing. AI workloads introduce fundamentally different requirements: bursty GPU utilization, rapid model iteration, and deployment pipelines that need to move from notebook to production in hours, not weeks.
The friction shows up in predictable ways. Your team spends days configuring CUDA drivers instead of improving models. Cold starts on serverless functions make inference latency unpredictable. Costs spiral because GPU instances run idle between inference requests. These aren’t minor inconveniences—they directly impact how quickly you can ship AI features and how much they cost to operate.
Modern AI-native platforms like Railway, Modal, and Replicate approach these problems differently. They assume GPU workloads are the norm, not an afterthought. They provide deployment primitives that match how AI engineers actually work. Understanding this distinction is the first step toward a successful migration.
Step 1: Audit Your Current AI Infrastructure
Before migrating anything, document exactly what you’re running and why. Create an inventory that answers these questions for each workload:
Resource utilization patterns: When do your GPU instances actually run at capacity? Most teams discover significant idle time, especially for inference endpoints that serve variable traffic. Pull metrics from your current provider and calculate actual utilization versus billed time.
Deployment frequency: How often does your team push model updates? If you’re deploying weekly but your CI/CD pipeline takes four hours, that’s a quarter of an engineer’s week lost to infrastructure friction.
Integration dependencies: Map which services connect to your AI workloads. A model endpoint that feeds real-time recommendations needs different migration planning than a batch processing job that runs overnight.
For example, a fintech company we observed was running fraud detection models on EC2 GPU instances. Their audit revealed 73% idle time—they were paying for GPU capacity that sat unused between transaction bursts. This single finding justified their migration investment before addressing any other friction points.
Step 2: Select the Right Migration Candidates
Not every workload should move at once. Prioritize based on three factors: pain level, risk tolerance, and independence.
Start with workloads that cause the most friction but carry lower risk. Development and staging environments are ideal first candidates. They let your team learn the new platform’s patterns without production pressure. Inference endpoints for internal tools come next—they provide real-world testing without customer-facing exposure.
Leave your most critical production systems for last. By then, your team will have developed expertise with the new infrastructure, and you’ll have documented patterns specific to your use cases.
Create a migration backlog with clear acceptance criteria for each workload. What does success look like? Define it in terms of deployment time, latency percentiles, cost per inference, and operational overhead.
Step 3: Execute a Parallel Deployment Strategy
The safest migration path runs old and new infrastructure simultaneously. This isn’t about hedging—it’s about validating performance with real traffic before cutting over completely.
Set up your first workload on the new platform while keeping the legacy version running. Route a small percentage of traffic—start with 5%—to the new deployment. Monitor everything: latency distributions, error rates, and cost accumulation.
Increase traffic gradually as confidence builds. Most teams reach 50/50 splits within two weeks if initial results look good. At this point, you have enough data to make an informed decision about full migration.
Document everything your team learns during this phase. Which deployment patterns worked well? What configuration quirks did you encounter? This institutional knowledge accelerates subsequent migrations.
Step 4: Optimize for the New Paradigm
Once migrated, resist the urge to simply replicate your old architecture on new infrastructure. Modern platforms enable patterns that weren’t practical before.
Explore scale-to-zero capabilities for inference endpoints with variable traffic. Implement preview deployments that spin up complete environments for every pull request. Use native secrets management instead of maintaining separate vaults. These aren’t just conveniences—they compound into meaningful improvements in development velocity and operational costs.
Schedule a retrospective 30 days post-migration. Compare actual metrics against your pre-migration baseline. Calculate realized savings and time recovered. This data informs future infrastructure decisions and helps justify the investment to stakeholders who weren’t involved in the technical details.
Moving Forward with Confidence
Infrastructure migrations feel risky because they are—but the risk of staying on platforms that don’t match your workload patterns accumulates daily in slower deployments, higher costs, and engineer frustration. The companies that move decisively will build compounding advantages as AI capabilities become central to their products.
Start your audit this week. Identify one low-risk workload to migrate first. Set a timeline that creates urgency without recklessness—30 days to first parallel deployment is achievable for most teams. The infrastructure layer is evolving; your architecture should evolve with it.
