Batch processing, concurrency, rate limits, streaming, and fallback
Words made simple
A few useful meanings before you read the explanation.
- Streaming
- Sending an answer in small pieces as it is produced, rather than waiting for the complete response.
- Rate limit
- A cap on how many requests or tokens a service allows during a period of time.
- Batching
- Processing several pieces of work together. It can improve efficiency, but waiting to fill a batch can add delay.
More words from this lesson
- Token
- A small piece of text processed by a model. It may be a whole word, part of a word, or punctuation.
- Embedding
- A list of numbers that represents the meaning of some content. Similar meanings can produce nearby lists of numbers.
- Agent
- An application that uses a model to choose its next action within defined limits. It can use tools and continue through several steps.
- Tenant
- A customer or organization whose data must stay separate from other customers in a shared application.
- Latency
- How long a request takes to return a result. Lower latency usually means a shorter wait for the user.
Overview
- Batch processing groups noninteractive tasks; bounded concurrency controls simultaneous requests; rate limits constrain traffic over time.
- Streaming exposes progress earlier.
- Fallback uses an approved alternative after eligible failures.
Why this matters
Provider quotas, long queues, and variable demand can overwhelm naive request-per-item loops.
An everyday example
Process overnight paperwork in batches while reserving staff capacity for live calls.
A technical example
Queue document embeddings with a concurrency cap and tenant quotas; return 202 plus job ID for long ingestion.
How it works
- Separate interactive/background traffic
- Enforce quota
- Queue work
- Run bounded workers
- Respect provider throttling
- Retry with jitter
- Apply safe degraded path.
Architecture and flow
Request → quota/admission control → live route or queue → bounded execution → result.
The insurance assistant example
Bulk policy re-indexing should not consume all provider capacity needed by customer chat.
When to use it
High volume, asynchronous tasks, and reliability requirements.
When to avoid it
Do not batch latency-sensitive interactions or retry against the same exhausted quota without backoff.
Interview answer
“I isolate traffic classes, use bounded queues and concurrency, respect quotas, and fall back only within the approved quality and privacy contract.”
Benefits, trade-offs, and production considerations
- Cap queue age, reject overload clearly, and watch retries causing a traffic storm.
- Streaming affects perceived latency; caching/routing/batching may reduce total work.
- Their benefits are different.
Illustrative cost calculation: at fictional rates of $1 per million input tokens and $4 per million output tokens, 2,000 input and 300 output tokens cost $0.002 + $0.0012 = $0.0032. At 100,000 requests, that is $320 for those calls only. Add routing calls, retries, embeddings, reranking, external tools, storage, telemetry, and review. These rates are arithmetic examples, not current provider prices.
- Use
total operating cost / successful tasksas a practical efficiency measure. - Also report the worst-cost runs, because an unbounded agent can dominate the bill even when the average looks small.
Can you explain it without the notes?
Batch processing, concurrency, rate limits, streaming, and fallback
Use your own words. Explain an example and one limitation before checking the explanation.
Check your explanation
Batch processing groups noninteractive tasks; bounded concurrency controls simultaneous requests; rate limits constrain traffic over time. Streaming exposes progress earlier. Fallback uses an approved alternative after eligible failures.
Turn this concept into a clear answer.
Practice this conceptKeep connecting the ideas.
Next lesson: Gateways, routing, fallback, quotas, and observabilityAdapted from 13_AI.MD · “Batch processing, concurrency, rate limits, streaming, and fallback”. Examples are study fragments; framework behavior can depend on your version.