Struggling to choose between Agones and Nakama? Both products offer unique advantages, making it a tough decision.
Agones is a Gaming Software solution with tags like kubernetes, game-servers, scaling, open-source.
It boasts features such as Kubernetes native - uses Kubernetes primitives for self-healing, auto-scaling, rolling updates etc, Automatic scaling of game server fleets, Health checking and lifecycle management of game servers, Allocate game servers using Kubernetes native APIs, SDKs for integration with game engines like Unity and Unreal, Support for Windows and Linux game servers, Metrics and monitoring via Prometheus, Multiple cloud provider support including Google Cloud, AWS, Azure etc and pros including Leverages Kubernetes for efficient resource management and scaling, Reduces operational overhead for game developers, Integrates with major game engines, Open source and extensible.
On the other hand, Nakama is a Gaming Software product tagged with opensource, social, realtime, messaging, matchmaking.
Its standout features include User account management, Data storage, Social network integration, Realtime messaging, Game matchmaking, and it shines with pros like Open source, Scalable, Handles common social and realtime features, Saves development time.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
Agones is an open source platform for hosting, scaling and managing multi-player game servers in Kubernetes. It simplifies game server operations and frees game developers to focus on building great games.
Nakama is an open source server designed for social and real-time games and apps. It handles user accounts, data storage, social network integration, realtime messaging, game matchmaking, and more. Nakama helps developers build multi-player games and social apps quickly and at scale.