Added images to pricing examples

src/pages/docs/platform/pricing/examples/data-broadcast.mdx

@@ -31,6 +31,8 @@ One odds update sent to 200 viewers = 201 messages (one sent to Ably + 200 deliv
 
 [Message conflation](/docs/messages#conflation) removes redundant and outdated messages by only sending the latest message to subscribers, within a specific time frame. Published messages are aggregated for a set period of time, such as every 500ms, evaluated against a conflation key with all but the most recent being discarded and sent to subscribers. For live odds, this is ideal, as users need the current price, not a history of changes.
 
+![Diagram showing Ably conflated subscriptions: live data feed, 500ms conflation window keeping only the latest update, and end user receiving one message per interval](../../../../../images/content/diagrams/conflated-subscriptions-example-pricing-docs.png)
+
 In this example, conflation reduces outbound delivery from 10 updates/sec to 2 updates/sec (500ms interval), **saving approximately 80%** at this scale.
 
 | Message type              | Calculation                                            | Messages    |

src/pages/docs/platform/pricing/examples/livestream.mdx

@@ -45,6 +45,8 @@ One message sent to a room with 5,000 subscribers = 5,001 messages (the sender o
 
 With Ably's [server-side batching](/docs/messages/batch), messages published within a short window (for example 500ms) are grouped and delivered together. With a 500ms window, that's 7,200 batches per hour, with each batch being charged as one billable message.
 
+![Diagram showing messages sent in chat being batched within a 500ms window and published as one billable message](../../../../../images/content/diagrams/batching-example-pricing-docs.png)
+
 This reduces the number of published messages which is the main cost driver in large rooms. In this scenario, batching reduces the total billed message volume by 79%.
 
 #### With batching messages