Koyeb

Overview

Koyeb is a serverless platform purpose-built for deploying AI applications, inference endpoints, and containerized workloads at global scale. It abstracts infrastructure complexity by offering Git-based deploys, managed Kubernetes orchestration, and native support for GPUs and specialized AI accelerators. The platform eliminates the need to manage servers, load balancers, or networking—developers push code or Docker images and Koyeb handles scaling, monitoring, and global edge distribution automatically.

The platform bridges the gap between simple PaaS solutions and complex Kubernetes setups, making it ideal for teams deploying LLM inference services, multi-agent systems, vector database endpoints, and real-time ML applications. Built-in pgvector support means you can run vector embeddings and semantic search without external dependencies, while preview environments enable testing before production deployment.

Key Strengths

Koyeb's inference-first architecture excels at deploying model endpoints with minimal latency. The platform supports CPU, GPU (including NVIDIA H100), and custom accelerators, with automatic scaling based on request volume. You can deploy vLLM, Ollama, or custom inference servers without configuring autoscaling policies—Koyeb infers optimal settings from your application metrics.

Git-based deployments are frictionless: connect a GitHub or GitLab repo, and every push triggers automatic testing and deployment. The managed pgvector PostgreSQL integration eliminates the operational burden of running a separate vector database, allowing you to embed semantic search directly into your application stack. Built-in monitoring dashboards, error tracking, and performance metrics provide production visibility without third-party tools.

• Global edge deployment with automatic failover and geographic load balancing
• Native Docker support—deploy any containerized workload without vendor lock-in
• Generous free tier covering most hobby and small production use cases
• Serverless functions alongside long-running services on the same platform

Who It's For

Koyeb is best suited for AI/ML teams deploying inference services, semantic search applications, and agentic systems that need reliable global infrastructure without DevOps overhead. Startups building on open-source models (Llama, Mistral, Stable Diffusion) benefit from frictionless GPU deployment and cost-effective scaling. Teams migrating from Heroku or Railway will appreciate the familiar developer experience paired with modern AI capabilities.

It's less ideal for teams requiring extensive customization of networking, storage policies, or those already committed to AWS/Azure ecosystems. Applications demanding sub-millisecond latencies or specialized hardware (TPUs, Cerebras chips) should consider cloud providers with deeper hardware partnerships.

Bottom Line

Koyeb solves a critical gap in the AI infrastructure landscape: it makes deploying and scaling inference workloads as simple as pushing code to GitHub, while remaining powerful enough for production multi-agent systems and semantic search platforms. The combination of GPU support, managed vector storage, and global edge deployment removes substantial operational complexity.

For developers prioritizing developer experience and fast time-to-market over maximum customization, Koyeb offers exceptional value. The freemium model is genuinely generous, allowing teams to validate AI product ideas without upfront costs. Its inference-first design signals that Koyeb understands the current AI development workflow better than general-purpose PaaS platforms.