Koyeb

Name: Koyeb
Brand: Koyeb

High-performance serverless infrastructure for AI. Run inference, sandboxes, and intensive workloads on CPUs and GPUs

Est 2019|Valuation $22.4M|Raised $13.7M|16 Ref Customers

LinkedIn 6K|Web traffic 369K|FTEs 27|HQ Boulogne-billancourt, France

MarketsApplication Hosting Platforms

01Competitive Landscape

Narrative analysis

#### Koyeb vs Fly.io - Fly.io’s pricing is highly granular (per-second). It provides many infrastructure knobs and can be suitable for globally distributed apps, but users may take on additional operational complexity. - Koyeb focuses on a PaaS-like workflow (GitHub deploy, managed primitives) and emphasizes scale-to-zero and reduced management overhead. #### Koyeb vs Railway - Railway also bills by the second and positions itself as pay-for-what-you-use, with published unit prices for CPU/RAM and plan tiers. - Railway advertises enterprise controls like SSO, audit logs and compliance add-ons on enterprise plans; Koyeb similarly gates SSO and audit behind Enterprise. #### Koyeb vs Render - Render is a frequent alternative in this category. Koyeb publishes a vendor-authored comparison page describing differences in CDN reach, pre-built container workflows, and cross-region private networking. - Render has its own free tier for web services and Postgres; vendor comparisons may be outdated and should be verified against competitor documentation. **Buyer takeaway:** Koyeb’s competitor comparisons are useful for feature framing but should be verified against competitor docs because platform capabilities change over time.

	Koyeb	Fly.io	Railway
Starting Price	Free tier + usage; paid plans start in the low tens per month + usage	Usage-based (Machines billed by second; additional support plans)	Trial then low-cost starter + usage (plus tiers; usage billed by second)
Typical Use Cases	Multi-region containerized services, APIs, workers, AI inference on GPUs	Running apps close to users; VM-like control; edge-friendly deployments	Developer-focused workflows from prototype to production with usage-based resources
Key Strength	Simple multi-region deploy, scale-to-zero, and GPU options; strong developer experience in reviews	Control and flexibility with Machines; good for distributed apps	Fast onboarding and straightforward unit pricing for CPU/RAM
Key Weakness	Some feature gaps (e.g., preview environments), account verification friction, enterprise security gated behind Enterprise	More operational responsibility than pure PaaS; stateful systems can be complex	Costs can become unpredictable; platform constraints vary by plan

01Competitive Landscape

Narrative analysis

Koyeb vs Fly.io

Fly.io’s pricing is highly granular (per-second). It provides many infrastructure knobs and can be suitable for globally distributed apps, but users may take on additional operational complexity.
Koyeb focuses on a PaaS-like workflow (GitHub deploy, managed primitives) and emphasizes scale-to-zero and reduced management overhead.

Koyeb vs Railway

Railway also bills by the second and positions itself as pay-for-what-you-use, with published unit prices for CPU/RAM and plan tiers.
Railway advertises enterprise controls like SSO, audit logs and compliance add-ons on enterprise plans; Koyeb similarly gates SSO and audit behind Enterprise.

Koyeb vs Render

Render is a frequent alternative in this category. Koyeb publishes a vendor-authored comparison page describing differences in CDN reach, pre-built container workflows, and cross-region private networking.
Render has its own free tier for web services and Postgres; vendor comparisons may be outdated and should be verified against competitor documentation.

Buyer takeaway: Koyeb’s competitor comparisons are useful for feature framing but should be verified against competitor docs because platform capabilities change over time.

	Koyeb	Fly.io	Railway
Starting Price	Free tier + usage; paid plans start in the low tens per month + usage	Usage-based (Machines billed by second; additional support plans)	Trial then low-cost starter + usage (plus tiers; usage billed by second)
Typical Use Cases	Multi-region containerized services, APIs, workers, AI inference on GPUs	Running apps close to users; VM-like control; edge-friendly deployments	Developer-focused workflows from prototype to production with usage-based resources
Key Strength	Simple multi-region deploy, scale-to-zero, and GPU options; strong developer experience in reviews	Control and flexibility with Machines; good for distributed apps	Fast onboarding and straightforward unit pricing for CPU/RAM
Key Weakness	Some feature gaps (e.g., preview environments), account verification friction, enterprise security gated behind Enterprise	More operational responsibility than pure PaaS; stateful systems can be complex	Costs can become unpredictable; platform constraints vary by plan