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.

Keep users informed and engaged with Ably's expanded push notification support. We're thrilled to announce that Ably Pub/Sub now supports web push notifications, in addition to our existing support for iOS and Android devices. With just a few lines of code, developers can easily integrate web push into their applications, providing a seamless and unified experience across all major platforms.

We are excited to announce that the Ably Chat SDK for JavaScript now includes support for custom contexts and hooks for React and React Native applications allowing developers working with these platforms to integrate chat features in an efficient and idiomatic way. Ably Chat is designed to meet a wide range of chat use cases, such as livestreams, in-game communication, customer support, or social interactions in SaaS products.

Today’s fans are global - often based across the world from their favorite team, sports star, or artist; and they are also virtual - for example, 99% of NBA fans across the world will probably never set foot in an arena. These trends are driving a shift in not only fan behaviors, but also expectations. Fans engaging in live events virtually rely on applications to deliver rich realtime experiences with such low latency and reliability that it can almost replace the in-person experience.

Ably CEO, Matthew O'Riordan, recently joined sports and media veteran, Carlo De Marchis, on his podcast ("A Guy with a Scarf") to discuss the dynamic world of realtime fan engagement in sports and entertainment. In the interview, Matt and Carlo cover the evolution of realtime applications and how the changing expectations of fans, particularly younger generations, are shaping the future of fan engagement. Watch the full interview, including Matt’s predictions for the future of this space.

At Ably, we provide a realtime messaging service that aims to deliver messages between globally-distributed participants with the lowest latency possible. When powering apps that enable realtime collaboration, or require time-critical updates, low and consistent latency is essential.

Kafka is often chosen as a solution for realtime data streaming because it is highly scalable, fault-tolerant, and can operate with low latency even under high load. This has made it popular for companies in the fan engagement space, and where transactional data is used (e.g. betting) as low latency ensures that actions and responses happen quickly, maintaining the fluidity and immediacy of the experience.