Real-Time vs Batch Inference for LLMs: Use Cases, Costs, Workflow

Choosing between real-time and batch inference is one of the highest-leverage architecture decisions in LLM applications. It impacts latency, throughput limits, reliability patterns, and most of all cost.

Quick definitions

• Real-time inference: synchronous request and response, typically seconds, optimized for low latency.
  
  
• Batch inference: asynchronous job submission, results later (minutes to hours), optimized for throughput and cost.

TL;DR: Decision Matrix

⚡ Real-Time API

• Fast responses (1–10 s)
• Standard cost
• Simple request/response
• Good for chatbots and live tools
• Handles rate limited traffic

📦 Batch API

• Slower turnaround (hours)
• Deep discounts
• Job submit + async results
• Good for heavy background jobs
• Handles large throughput

Understanding Real-Time LLM Inference

Real-time inference APIs process requests immediately. When you send a prompt, the model generates a response within seconds and returns it synchronously.

When to Use Real-Time

Real-time inference is essential for interactive applications where users expect immediate responses:

• Chatbots and conversational AI that need instant replies
• Customer support assistants during live interactions
• Content generation tools where users wait for output
• Interactive search and question-answering systems
• Real-time code review tools providing instant feedback

Any application where user experience degrades noticeably with delays requires real-time endpoints.

Understanding Batch LLM Inference

Batch inference APIs process multiple requests together asynchronously. Submit a batch job with hundreds, thousands, or millions of requests, and the service processes them when compute resources are most efficiently utilized - typically within the vendor stated SLA (1-24 hours).

When to Use Batch

Batch inference is ideal when you don't need immediate responses and can benefit from significant cost savings:

• Data processing pipelines that enrich datasets with LLM insights
• Document processing that summarizes, extracts, or translates content
• Classification and moderation that labels, categorizes, or filters content
• Synthetic data generation for model training or testing
• Model evaluation running prompts across test suites
• Embedding generation for semantic search or recommendations
• Async AI agents that research, analyze, or generate reports

Why Batch APIs are so much cheaper

The pricing difference comes from infrastructure economics.

Real-time systems must keep GPU capacity warm for unpredictable spikes and prioritize low latency scheduling. That tends to waste capacity and prevents aggressive packing of work.

Batch systems can:

• Queue and pack requests densely on GPUs
  
  
• Schedule work when compute is cheaper or more available
  
  
• Optimize for throughput rather than single-request latency
  
  

That usually translates into lower cost per token for the same class of work.

For a workload involving 100 million input tokens and 10 million output tokens each month, utilizing Anthropic's real-time APIs would cost $27,000 (based on Claude Opus 4). Switching to batched inference with the same model reduces this cost by 50%, resulting in savings of around $13,000.

However, moving to a specialist batched API, such as Doubleword, using a model of comparable quality (like Qwen3-235B), drastically cuts the cost to just $312 for this workload. This represents a 99% cost saving, amounting to tens of thousands of dollars saved annually on a single workload. Specialized providers like Doubleword that focus exclusively on high-volume async workloads can optimize their entire infrastructure for cost efficiency, delivering even lower prices while maintaining reliable SLAs. (Pricing as of Jan 2026).

Key Comparisons

Cost vs Latency: Real-time delivers responses in seconds at full price. Batch introduces hour-scale latency but cost much less.

Throughput: Real-time endpoints limit concurrent requests and have strict rate limits. Batch APIs handle massive volumes naturally—submit 100,000 requests in one job without rate limits.

Developer Experience: Real-time feels familiar—request, response, done. Batch requires async workflows—submit jobs, poll for completion, retrieve results. Modern batch APIs provide webhooks and SDKs that simplify this.

Hybrid Strategies That Work Well

Smart applications use both API types strategically:

Pre-compute with batch, serve in real-time: Generate content in batch overnight, store results, and serve them instantly when requested. This combines batch economics with real-time user experience.

Real-time for users, batch for internal workflows: Use real-time for customer-facing features while running analytics and reporting in batch.

‍Tiered processing: Route urgent requests to real-time, defer lower-priority tasks to batch queues based on business value.

Common Mistakes

Defaulting to real-time: Many developers choose real-time APIs because they're simpler, even when batch works fine. Always ask: does this actually need to be real-time?

‍Underestimating batch reliability: Modern batch APIs offer clear SLAs. 

Ignoring compounding costs: 80% might not sound game changing, but $500/month saved per feature equals $6,000/year. Across multiple features, savings compound significantly.

Conclusion

The choice between real-time and batch inference directly impacts user experience and costs. Real-time APIs are essential for interactive features. Batch APIs deliver the same intelligence at 50% lower cost for async workloads.

Audit your current LLM usage. Which calls are truly user-facing and time-sensitive? Which could happen asynchronously? Route data processing, document analysis, classification, synthetic data generation, embeddings, and evaluation to batch APIs.

Build hybrid architectures that use real-time where necessary and batch everywhere else. Your users won't notice the difference, but your budget will.

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Ready to Cut Your LLM Inference Costs in Half?

Doubleword Batch specializes in high-volume, async LLM workloads. We deliver sustainably low costs with trusted SLAs (1-hour or 24-hour) and a developer experience built specifically for batch workflows.

Perfect for data processing pipelines, document processing, classification tasks, model evaluation, async agents, embeddings, and synthetic data generation.

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