Streaming vs Batch: When to Use Each Pattern with LLM APIs

[chawkian]

Streaming vs Batch: When Does Each Pattern Shine?

When building applications on top of LLM APIs, one of the first architectural decisions you'll face is whether to use streaming (Server-Sent Events / chunked responses) or batch (wait for the full response) mode.

Streaming Wins When:

• User-facing chat UX — seeing tokens appear progressively feels 2-3x faster to users
• Long completions — if the model generates 2000+ tokens, streaming keeps the connection alive and the user engaged
• Early termination — you can stop generation mid-stream if the output goes off track

Batch Wins When:

• Structured output parsing — you need the complete JSON before validating
• Pipeline processing — the output feeds into another API call or transformation
• Cost-sensitive batch jobs — easier to manage rate limits and retries

Code Example (Python SDK)

from aiwave import AIWave

client = AIWave(api_key="your-key")

# Streaming
stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain monads"}],
    stream=True
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

# Batch
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain monads"}]
)
print(response.choices[0].message.content)

A Practical Tip

If you're building a production pipeline, consider a hybrid approach: use streaming for interactive endpoints and batch for background workers. The AIWave API docs cover this pattern in more depth, including how to handle backpressure and graceful degradation.

Question for the Community

What's your go-to pattern? Have you run into edge cases where streaming caused unexpected issues (e.g., partial JSON, connection drops)? Let's discuss! 👇