AWS SageMaker vs GCP Cloud Run vs RunPod vs Railway: Real Costs for ML Inference in 2026

The MLOps landscape has shifted. Teams are moving away from managed AI platforms and toward self-managed infrastructure on hyperscale clouds. But which provider gives you the best price-performance for production inference? We deployed the same FastAPI + PyTorch sentiment analysis model across four platforms and measured real costs.

The Test Setup

• Model: DistilBERT-base fine-tuned for sentiment analysis
• Framework: FastAPI 0.104 + PyTorch 2.1
• Hardware target: NVIDIA T4 (or nearest equivalent)
• Traffic pattern: 1,000 requests/hour @ ~200ms latency budget
• Region: us-east-1 (or nearest for each provider)

AWS SageMaker

Base cost: $0.35/hr for ml.g4dn.xlarge (T4, 16GB VRAM)

SageMaker has the most mature ecosystem. You get model registry, endpoints with auto-scaling, monitoring, and tight IAM integration. But the sticker price is misleading — you pay for the instance even when it's idle.

Monthly estimate (single endpoint): ~$255/month for inference-only, plus ~$20/month in ECR storage and data transfer. If you need a staging endpoint + production, double it.

Verdict: Best for enterprise teams already on AWS with existing VPC setups. Overkill (and overpriced) for smaller teams.

GCP Cloud Run

Base cost: $0.28/hr for GPU-attached instance (T4, 16GB VRAM)

Cloud Run with GPU support is newer but surprisingly competitive. The serverless billing model means you only pay for request-processing time, not idle time. Cold starts are painful though — expect 15-30 seconds for a GPU container to spin up.

Monthly estimate: ~$180/month for the same workload (assuming consistent traffic, no cold starts counted). With bursty traffic, savings are even higher.

Verdict: Best price-performance of the big three. The cold start problem is real for low-traffic endpoints.

RunPod

Base cost: $0.29/hr for RTX 4000 Ada (similar to T4, 20GB VRAM)

RunPod's GPU cloud is purpose-built for ML inference. The community templates make setup trivial. Their serverless offering (vLLM-based) removes cold starts entirely, but it's optimized for LLMs — not great for custom PyTorch endpoints.

Monthly estimate: ~$210/month for reserved GPU. Serverless pricing is consumption-based and harder to predict for custom models.

Verdict: Best for GPU-only workloads. Limited ecosystem beyond GPU compute. If you need a database, logging, monitoring — you're stitching it together yourself.

Railway

Base cost: $0.000463/GB-min RAM, GPU coming soon

Railway doesn't offer GPU instances yet (T4 is on the roadmap for Q3 2026). We ran on a high-RAM CPU instance for comparison. Performance was 4x slower than the GPU providers, but the deployment experience was the best of all four — push to GitHub, it deploys.

Monthly estimate: ~$40/month for CPU-only (not comparable for GPU workloads). When GPUs arrive, projected pricing is competitive.

Verdict: Best DX (developer experience) of all platforms. Once GPU support lands, it'll be the best option for teams that want zero infrastructure management.

The BYOC Advantage

Here's what we learned: none of these platforms are bad. The problem is lock-in. Once you're on SageMaker, migrating to RunPod means rewriting your deployment pipeline. Once you're on RunPod, adding GCP redundancy requires an entirely different workflow.

This is why we built Roptal. It's an orchestration layer that works across all four platforms. Connect your repo once, deploy anywhere. Shift traffic between clouds based on cost, availability, or GPU type. No vendor lock-in.

Cost Comparison Table

Platform	GPU Instance	Monthly Cost	Cold Start	Lock-in Risk
AWS SageMaker	g4dn.xlarge	~$275	45-90s	High
GCP Cloud Run	T4-attached	~$180	15-30s	Medium
RunPod	RTX 4000 Ada	~$210	None (reserved)	Medium
Railway	CPU only	~$40	None	Low
Roptal (multi-cloud)	Any	Dynamic	Optimized	None

Bottom Line

For production inference in 2026:

• Budget-constrained teams: GCP Cloud Run or RunPod (wait for Railway GPU)
• Enterprise AWS shops: SageMaker (but negotiate reserved pricing)
• Teams who want freedom: Multi-cloud via Roptal — pick the best provider per workload

The real savings come from being able to switch providers without re-platforming. That's the bet we're making.