💡 Streaming & Time-to-First-Token — Making Claude Feel Instant

One of the most impactful UX improvements you can make to a Claude-powered product costs nothing in tokens and requires only a few lines of code: enable streaming. Without streaming, your UI shows a spinner until Claude finishes generating the entire response, then displays it all at once — a wait that can stretch to 30+ seconds for long outputs. With streaming enabled, the first token appears within a second or two, and the response flows progressively. Users perceive streaming interfaces as dramatically faster even when the total generation time is identical.

Streaming best practices

• Use the stream=True parameter (Python) or the streaming variant of messages.create in the SDK. Both fire a server-sent events stream you iterate over.
• Display chunks as they arrive: Don't buffer the entire stream before rendering. Flush each chunk to the UI immediately — this is the entire point of streaming.
• Handle message_delta events: The SDK's streaming iterator surfaces typed events. Listen for content_block_delta for text chunks and message_stop for completion.
• Reduce prompt verbosity to lower TTFT: Time-to-first-token correlates with input token count — a leaner prompt starts outputting faster. Prune any context that isn't essential to the specific query.

with client.messages.stream(
    model="claude-haiku-4-5",   # Haiku has lowest TTFT
    max_tokens=1024,
    messages=[{"role": "user", "content": prompt}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

💡 Batch Processing — Run High-Volume Claude Workloads at 50% Off

If you have a workload that doesn't need to complete in real time — document summarisation queues, nightly classification runs, bulk data extraction, evaluation pipelines — the Message Batches API is the obvious tool to reach for. Batch requests are processed asynchronously and priced at approximately 50% of the standard API rate. You submit a batch of up to 10,000 requests in a single call, poll for completion, then retrieve all results. For high-volume workflows, this halves your Claude infrastructure costs with essentially no code complexity added.

When to use batch vs. real-time

• Use batch for: nightly report generation, bulk classification or tagging, data extraction pipelines, evaluation suites, content moderation queues, SEO or metadata generation for large content libraries.
• Use real-time for: anything user-facing, interactive chat, tools requiring immediate results, or pipelines where downstream steps depend on the Claude response before they can start.
• Parallelism within a batch: Items in a batch are processed concurrently — a batch of 1,000 items doesn't take 1,000× longer than one item. Actual throughput depends on capacity, but batches complete far faster than sequential real-time calls for the same volume.