Technology Comparison
Diffusion can be applied to a range of use cases and messaging contexts, and therefore draws comparison with a number of other platforms, libraries, and technologies.
What follows is a capability comparison that may help you decide whether Diffusion is a good fit for your needs.
Definition of terms
Capability definitions
- Real-time data
- Ability to push updates immediately to subscribers as soon as they occur, without polling.
- Enables low-latency experiences like trading dashboards, betting odds, live collaboration, fraud detection.
- Designed for the Web
- Native support for web standards - WebSocket, HTTP APls, and developer SDKs.
- Ensures easy integration into browsers, mobile apps, and SaaS platforms without custom gateways.
- Suitable for Mobile & loT
- Optimized for constrained environments (low bandwidth, battery, intermittent connectivity).
- Critical for loT sensors, mobile apps, wearables where efficiency and power usage are key.
- Reduces bandwidth usage
- Mechanisms like delta streaming (send only changes), conflation (combine bursts), message compression, flow control, and flexible distribution patterns that bring data closer to clients.
- Saves cost, improves user experience, avoids network overload in high-frequency data scenarios.
- Handles high data throughput
- Ability to ingest and distribute very large volumes of data (millions of msgs/sec).
- Necessary for market data, telemetry, and backend event pipelines where volume is extreme.
- Handles high numbers of connections
- Efficiently supports thousands or millions of concurrent client sessions.
- Enables global scale apps (trading platforms, gaming, chat, betting) without bottlenecks.
- Delta Streaming & Conflation
- Send only differences between messages (deltas) and coalesce bursts of updates
- Reduces network load, improves performance for fast-moving feeds (prices, odds, sensor streams).
- Per-client Data Shaping
- Server-side filtering, transformation, or routing of data specific to each subscriber
- Delivers personalized feeds without duplicating publishers; reduces compute and client-side logic.
- Intelligent Back-pressure
- System automatically adapts delivery rate when consumers lag (throttling, conflation).
- Prevents slow clients from causing system failures or cascades under heavy load.
- Fine-grained Authorization
- Ability to restrict access at topic/channel or even field/message level.
- Ensures data security and compliance by enforcing "least privilege" access at scale.
- Where it shines
- The vendor's natural sweet spot or use case where it provides the most value.
Comparing enterprise technologies
| Capability | Diffusion | REST APIs | Kafka / AWS MSK | MQTT protocol solutions | RabbitMQ | Google Pub/Sub |
|---|---|---|---|---|---|---|
| Real-time data | ✅ Yes | ❌ No | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| Designed for the Web | ✅ Yes (native WebSocket & SDKs. Secure HTTP long polling protocol as WebSocket fallback) | ✅ Yes (req/resp) | ❌ No (needs proxy) | ❌ No (IoT first) | ❌ No (protocol add-ons) | ✅ Yes (HTTP/gRPC) |
| Suitable for Mobile & loT | ✅ Yes (delta, conflation, low power) | ⚠ Partial | ⚠ Partial | ✅ Yes (lightweight protocol) | ⚠ Yes (with plugins) | ⚠ Partial |
| Reduces bandwidth usage | ✅ Yes (delta streaming, conflation) | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Handles high data throughput | ✅ Yes | ❌ No | ✅ Yes | ⚠ Broker dependent | ⚠ Moderate-High | ✅ High |
| Handles high numbers of connections | ✅ Yes (100s of thousands) | ✅ Yes (stateless) | ⚠ Limited (not edge scale) | ✅ Yes (designed for devices) | ⚠ Moderate-High | ✅ Yes (managed scale) |
| Delta Streaming & Conflation | ✅ Native | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Per-client Data Shaping | ✅ Topic views, filters | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Intelligent Back-pressure | ✅ Yes | ❌ No | ⚠ Limited (consumer groups) | ❌ No | ⚠ Limited | ❌ No |
| Fine-grained Authorisation | ✅ Per-topic tree | ⚠ Basic (OAuth, endpoint-level) | ⚠ Broker-level ACLs | ⚠ Basic/Broker dependent (e.g. topic ACLs) | ⚠ Basic | ⚠ Basic |
| Where it shines | Web/mobile fan-out at scale with efficient bandwidth | Simple request/response APIs | High-throughput event streaming pipelines | IoT Telemtery at scale | General-purpose queues, AMQP use cases | Global event ingestion + analytics pipelnes |
Comparing event broker platforms
| Capability | Diffusion | Ably | Solace | PubNub | iPushPull | NATS.io | Pusher |
|---|---|---|---|---|---|---|---|
| Real-time data | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
| Designed for the Web | ✅ Yes (native WebSocket & SDKs. Long polling protocol) | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ⚠ Partial | ✅ Yes |
| Suitable for Mobile & loT | ✅ Yes (delta, conflation, low pwer) | ✅ Yes | ✅ Yes | ✅ Yes | ⚠ Partial | ✅ Yes | ✅ Yes |
| Reduces bandwidth usage | ✅ Yes (delta streaming, conflation) | ⚠ Partial | ⚠ Partial | ⚠ Partial | ⚠ Partial | ❌ No | ❌ No |
| Handles high data throughput | ✅ Yes | ✅ High | ✅ Very High | ✅ Moderate-High | ⚠ Moderate | ✅ Very High | ⚠ Moderate |
| Handles high numbers of connections | ✅ Yes (100s of thousands) | ✅ Yes | ✅ Yes | ✅ Yes | ⚠ Moderate | ✅ Yes | ✅ Yes |
| Delta Streaming & Conflation | ✅ Native | ⚠ Partial | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
| Per-client Data Shaping | ✅ Topic views, filters | ⚠ Partial | ✅ Yes | ⚠ Partial | ✅ Yes | ❌ No | ❌ No |
| Intelligent Back-pressure | ✅ Yes | ⚠ Partial | ✅ Yes | ⚠ Limited | ⚠ Partial | ⚠ Limited | ❌ No |
| Fine-grained Authorisation | ✅ Per-topic tree | ✅ Yes | ✅ Enterprise-grade | ✅ Channel-based | ✅ Yes | ⚠ Basic | ⚠ Basic |
| Where it shines | Web/mobile fan-out at scale with efficient bandwidth | Realtime APIs for apps and presence | Enterprise event mesh, multi-protocol hub | Mobile/web chat, gaming, app messaging | Realtime data sharing (Excel, web, APIs) for finance/capital markets | High performance, low-latency core messaging bus for microservices/IoT | Realtime app features (chat, notifications, presence, collaboration) |