We're happy to announce the release of message editing and deletion in our Javascript and React SDKs and within our NEW iOS and Android SDKs for Ably Chat. These additions will enable the development of richer chat experiences and simplify the development of cross-platform chat applications.

In light of the recent deprecation of MongoDB Atlas Device Sync (ADS), developers are seeking alternative solutions to synchronize on-device data with cloud databases. Ably LiveSync offers a potential alternative and can replace some of ADS’s functionality, enabling realtime synchronization of database changes to devices at scale.

There is increasing demand for realtime data delivery as users expect faster experiences and instantaneous transactions. This means not only is lower latency required per message, but providers must also handle far greater capacity in a more globally-distributed way.

Following the deprecation of Realm, we have partnered with MongoDB to ensure that MongoDB customers can continue to deliver the device sync capabilities their customers depend on. The solution: a MongoDB Connector for the Ably LiveSync product. Ably LiveSync enables you to seamlessly fan-out your MongoDB Atlas state to billions of clients in realtime - ensuring reliable, low-latency delivery of your document data.

When you publish a message to Ably Pub/Sub, you can be confident that the message will be delivered to subscribing clients, wherever they are in the world. Ably is fast: we have a 99th percentile transmit latency of <50ms from any of our 635 global PoPs, that receive at least 1% of our global traffic. But being fast isn’t enough; Ably is also dependable and scalable. Ably doesn’t sacrifice data integrity for speed or scale; it’s fast and safe.

This is one of a series of posts that explain Ably’s four pillars of dependability. The four pillars project at Ably is about making concrete, objectively verifiable, statements about the technical characteristics of the service. We aim to ensure that our claims about service performance are expressed clearly in terms of explicit metrics, and we explain in technical terms how those performance levels are met.

When was the last time you used a chat service and noticed messages arriving out of order? It’s likely that you can’t think of one. Getting messages to arrive in the right order is more or less job number one of a chat service. Sounds simple. But scale this to millions of users across a global network and it becomes a complex distributed computing problem. Even at smaller scales, message ordering isn’t guaranteed once you go beyond a simple, direct connection between two users.

Consumption-based pricing has become a popular model for SaaS and PaaS businesses, allowing customers to pay only for what they use. Pioneers like Slack and AWS have successfully adopted this approach, offering flexibility and reducing waste. However, not all consumption-based models are created equal. The Monthly Active Users (MAU) model, while appealing in its simplicity, often leads to inefficiencies and unexpected costs.

Kafka is a highly popular realtime data streaming platform, renowned for handling massive volumes of data with minimal latency. Typical use cases include handling user activity tracking, log aggregation and IoT telemetry. Kafka’s architecture, based on distributed partitions, allows it to scale horizontally across multiple brokers. But although Kafka excels at high data throughput, scaling it to manage thousands of client connections can be costly and complex.

Our new per-minute, consumption-based pricing model offers granular control over your costs, eliminating the need to pay for peak users or connections. Whether you're building a proof of concept or handling massive user loads, our pricing model provides what you need to succeed - volume discounts, cost optimizations and business critical support. Consumption-based pricing has become widespread in SaaS and PaaS businesses, and it's easy to understand why: you should only ever pay for what you consume.