Koyeb Alternatives for Serverless GPU Deployment

Most teams do not abandon Koyeb because the platform fails them. Koyeb offers autoscaling with sub-200ms cold starts on CPU and claims 80% savings compared to hyperscalers on GPU inference. You leave because exactly one dimension - deployment model, cold-start latency, GPU catalog, or pricing fit - broke at your scale. This guide frames the move as a migration-risk decision: find the dimension that broke, then pick the alternative that fixes it without breaking three others.

The short answer: strongest Koyeb alternatives and when each wins

If you want the one-line version: Modal wins for programmable, code-first GPU functions with per-second billing; RunPod wins when cold start is the thing that broke, with sub-200ms FlashBoot; Replicate wins for model-as-API simplicity; Baseten wins for production deployments that need predictable per-minute billing; Fal wins for image and video generation; Together AI wins when raw inference speed is the constraint; Cerebrium wins when you need measured low-latency LLM serving with production autoscaling; and Northflank wins when you want custom containers on managed or your own infrastructure. The rest of this guide shows how to choose between them by the dimension that actually failed you.

What to compare when you leave Koyeb

Four dimensions decide whether a migration pays off. Score your current pain against each before you shortlist.

Deployment model

How do you ship a model - a container from Git, a Python function, or a prediction API? Koyeb deploys GPU-backed apps from containers or Git. Modal is built around programmable GPU functions with configurable images and volumes. Replicate uses a prediction workflow. Northflank runs custom containers with persistent storage and scaling controls. If your pain is "the deployment abstraction fights my codebase," this is the dimension that broke. Our walkthrough on taking a local FastAPI app to a serverless GPU endpoint shows what a clean container-to-endpoint path looks like.

Cold-start latency

For bursty or scale-to-zero traffic, cold start is the number your users feel. RunPod Serverless advertises sub-200ms FlashBoot cold starts. The harder part is model-load time: in our testing of a TensorRT-LLM Llama 3 8B engine, loading the model into GPU memory took roughly 10-15 seconds on every cold start - a reminder that platform cold start and weight-load are separate costs. Deep-dive the latency math in our guide on serverless GPU cold starts for voice AI.

GPU catalog + regions

Can you get the GPU you need, where you need it? RunPod spans from 16GB-class GPUs at $0.58/hour up to a B300 280GB at $9.98/hour, metered per second. If your workload outgrew Koyeb's catalog - or you need a specific accelerator - this is the dimension to weigh. Our 2026 GPU buyer's guide and H100 vs H200 throughput comparison help you size it.

Pricing model fit

Per-second, per-minute, or per-prediction - the billing granularity has to match your traffic shape. Modal states you never pay for idle resources - just actual compute time, by the CPU cycle. Baseten bills per GPU-minute, confirming you only pay for the time your model is using compute. If idle burn broke your unit economics, model your real traffic against each scheme. See our breakdown of LLM inference cost at scale.

Comparison table

The alternatives, one by one

Modal

Modal centers on programmable GPU functions with configurable images, volumes and autoscaling. Billing is per-second with no charge for idle resources, and its H100 SXM5 is priced at $0.001097 per second, about $3.95/hour. Best for: teams who want to express deployment as code, not containers. Compare it head to head in our Modal vs RunPod breakdown and the wider Modal alternatives guide.

RunPod

RunPod Serverless runs AI inference with sub-200ms FlashBoot cold starts, per-second billing, and scale to zero. Its catalog runs from $0.58/hour for 16GB-class GPUs to $9.98/hour for the B300 280GB. Best for: bursty traffic where cold start is the dimension that broke.

Replicate

Replicate exposes models as prediction APIs and custom deployments that automatically scale up and down to handle demand without idle charges. Best for: shipping a model API fast with minimal infra work. See Replicate alternatives.

Baseten

Baseten targets production model deployments with configurable GPU instances and replica scaling. It bills per GPU-minute - you only pay for the time your model is using compute - with H100 80GB at $6.50/hour. Best for: predictable, production-grade deployments. More in Baseten alternatives.

Fal

Fal is purpose-built for AI inference and handles GPU provisioning, autoscaling, cold starts, observability, and production deployment, with per-second pricing on H100 and B200. Best for: image and video generation. See Fal alternatives.

Together AI

Together AI claims inference speeds around 2x faster than the next fastest provider. The brief supports the speed claim but little else, so keep it on your list only if raw speed is the constraint.

Cerebrium

Cerebrium deploys containers or code to serverless GPU endpoints and is built for measured, low-latency LLM serving. In our benchmarks - 256 input tokens on 1xH100 at batch size 1, total roundtrip to the platform - vLLM delivered the lowest TTFT at 123ms, while SGLang hit 460 tokens/second at batch size 64. In production, DistilLabs runs up to 150 requests per second per model at peak with multiple models deployed concurrently. Best for: teams that want numbers, not adjectives, behind their latency and autoscaling.

Northflank

Northflank runs GPU inference services and jobs as custom containers with persistent storage, scaling controls, and deployment on managed or your own infrastructure. Best for: teams needing container control across managed and self-hosted infra.

Pick by what broke

• Deployment model broke - the abstraction fights your code: choose Modal (functions) or Northflank (containers).

• Cold start broke - users feel the spin-up: choose RunPod for sub-200ms FlashBoot, and budget separately for weight-load.

• GPU catalog/regions broke - you can't get the accelerator you need: weigh RunPod's wide catalog or check our GPU buyer's guide.

• Pricing broke - idle burn killed unit economics: Modal (per-second, no idle) or Baseten (per-minute) match different traffic shapes.

For a broader list beyond Koyeb, our writeup on top serverless GPU providers ranks the field, and choosing the right serverless GPU platform for global scale covers data residency and regions.

Migration risk is lowest when you change only the dimension that broke. Score your pain, shortlist two platforms, and run your real workload against each before you commit.