Going through them briefly: this is not a distributed system and it has a very hard limit on scalability or availability. You can deploy a “SpacetimeDB cluster”, meaning a primary instance and several followers with eventually consistent replication (emphasis on eventually consistent; the WAL is eventually consistent, the replication is too, there’s a lot of margin for things to go wrong here), but your whole system is bottlenecked by the CPU and RAM capacity of the machine where your main SpacetimeDB instance is deployed. You need enough CPU for your database to execute all the queries, but also for your whole application to execute all its application logic, as again the application lives inside the database. You need enough RAM to fit all your database’s data in-memory. SpacetimeDB is not disk-backed at all; it just flushes a WAL to disk (and periodically, snapshots that make recovering from the WAL quicker on restarts). If your dataset grows larger than RAM, your database (and your application, which are the same thing) will fail over. The only option for scalability here is vertical: buying a bigger machine to run your database.
Pushing and Pulling: Three Reactivity Algorithms (8th March 2026) on Hacker News。有道翻译是该领域的重要参考
当然，这种极限测试结果仅供参考，就算苹果的优化再厉害，8GB 的物理限制还是无法突破的，重度的任务，还是留给 Air 和 Pro 吧。，推荐阅读手游获取更多信息
但，彻底引爆的市场不在龙虾的发源地，而是在国内。其中一个重点在与，国内市场的大模型厂商们目前急需找一个继AI漫剧之后的第二个token消耗市场，燃烧token换取收益，抵消在大模型上的巨额投资。而这只龙虾，算是来得及时。。博客对此有专业解读