Koyeb

Koyeb GPU pricing overview

Koyeb is a specialist GPU cloud provider headquartered in Paris, France. Koyeb is a serverless GPU platform for deploying AI inference endpoints without managing infrastructure, with automatic scaling to zero and pay-per-use billing across EU and US regions. Git-based deployment and a simple dashboard make it easy to ship LLM inference APIs and AI model serving endpoints in minutes. A strong choice for teams that want zero-ops GPU inference with automatic scaling and no idle compute costs. Billing is Pay-per-use (serverless) with a minimum commitment of None. Available regions include EU, US. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Koyeb suitable for both short-duration experiments and sustained production workloads.

Koyeb vs other GPU providers

Koyeb competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Serverless — no infrastructure management; Automatic scaling to zero; EU and US regions. Use the side-by-side comparison tool above to see Koyeb pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 2 Koyeb listings alongside 94+ providers in a single sortable view.

Best use cases for Koyeb

Koyeb is best suited for: Inference API deployments, Serverless AI apps, Teams wanting zero-ops GPU. Support tiers range from Community → Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 2 active GPU listings on Koyeb, covering L4, A100 80GB. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

Koyeb billing model and cost structure

Koyeb uses Pay-per-use (serverless) pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Koyeb

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Koyeb pricing data is collected

Prices shown are sourced from Koyeb's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are retained for 90 days and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.