{"post":{"slug":"batch-vs-streaming-inference-2026","title":"Batch vs streaming inference in 2026: which serving mode to use","excerpt":"Batch and streaming inference trade throughput against latency when you run a model. Here is what each mode is in 2026, why continuous batching blurred the line, and how to route your workload.","category":"ai","date":"June 27, 2026","author":"ByteLedger Team","content":"<span class=\"dropcap\">B</span>atch and streaming inference are two ways to run a model against your data, and the difference is mostly about time. Batch inference processes a large pile of inputs together, offline, optimizing for total throughput and cost per item. Streaming (online) inference handles requests one at a time as they arrive, optimizing for low latency so a user is not left waiting. Most of the confusion around the topic comes from mixing up two different senses of the word streaming.\n\n## What changed in 2026\n\n- **Continuous batching blurred the line.** Modern servers batch incoming online requests dynamically, so you get batch-like accelerator efficiency with streaming-like latency, without choosing one or the other by hand.\n- **Batch APIs got cheaper.** Providers offer large discounts for asynchronous batch jobs with relaxed completion deadlines, making offline work far more economical.\n- **Workloads split cleanly.** Teams now deliberately route latency-sensitive traffic to online serving and everything else to batch.\n\n## Two meanings of streaming\n\nThis trips up nearly everyone, so it is worth separating:\n\n- **Streaming as a serving mode** means handling requests online, in real time, as they arrive.\n- **Token streaming** means sending the response back incrementally, token by token, so the user sees text appear as it is generated.\n\nThey are independent. You can stream tokens from an online request, and a batch job can stream nothing at all. When someone says \"streaming inference,\" ask which one they mean.\n\n## How each mode works\n\nBatch inference collects many inputs, submits them as a job, and lets the scheduler pack work to keep the accelerator busy. Nobody is watching the clock on any single item, so the system maximizes utilization and drives down cost per request. Online inference serves each request as soon as it arrives against a latency target, which means the hardware is sized for peak responsiveness rather than peak efficiency. Getting the interactive experience right also depends on techniques covered in [speculative decoding explained in 2026](/blog/speculative-decoding-explained-2026).\n\n## Batch vs streaming\n\n| Dimension | Batch inference | Streaming (online) inference |\n| --- | --- | --- |\n| Optimizes for | Throughput and cost | Latency |\n| User waiting | No, offline | Yes, real time |\n| Cost per request | Lowest | Higher |\n| Accelerator use | High, well packed | Variable, sized for peaks |\n| Typical use | Embeddings, evals, bulk jobs | Chat, copilots, agents |\n| Deadline | Minutes to hours | Milliseconds to seconds |\n\n## When to use which\n\nUse **batch** for work that is not user-facing and can tolerate delay: backfilling embeddings, bulk classification or tagging, running evaluation suites, summarizing archives, and generating synthetic data. Use **online** for anything a person is waiting on: chatbots, coding assistants, live agents, and interactive search. When both a user experience and a large offline pipeline share the same model, run the two paths separately so bulk work never competes with interactive latency.\n\n## Pitfalls to watch\n\n- **Wrong path, wrong cost.** Pushing a million offline items through the interactive endpoint pays a latency premium for nothing. Route them to batch.\n- **Bad UX from batching interactive traffic.** Making a user wait in a batch queue for a live answer is the opposite mistake.\n- **Ignoring continuous batching.** If your server supports it, you often get most of the throughput benefit without abandoning low latency.\n- **No deadline management.** Batch jobs still need queue limits and priorities, or a big job can starve everything behind it.\n\n## FAQ\n\n**Is token streaming the same as streaming inference?**\nNo. Token streaming is how the response is delivered. Streaming inference as a serving mode means handling requests online in real time. They are separate choices.\n\n**Which is cheaper?**\nBatch, usually by a wide margin, because it packs the hardware and often qualifies for discounted asynchronous pricing. Verify current rates for your provider.\n\n**Can one deployment do both?**\nYes. Many teams serve interactive traffic online and schedule bulk work as batch jobs against the same model, sometimes on separate capacity.\n\n**What is continuous batching?**\nA serving technique that groups live incoming requests into shared model passes on the fly, giving batch-like efficiency while keeping per-request latency low.\n\n## Where to go next\n\n- [Speculative decoding explained in 2026](/blog/speculative-decoding-explained-2026)\n- [LLM caching strategies in 2026](/blog/llm-caching-strategies-2026)\n- [LLM evaluation metrics in 2026](/blog/llm-evaluation-metrics-2026)","hue":290,"metaTitle":"Batch vs Streaming Inference 2026","metaDescription":"Batch vs streaming inference for 2026 — throughput versus latency, cost tradeoffs, and how to choose the right LLM serving mode for your workload.","keywords":["batch vs streaming inference 2026","batch inference","streaming inference","llm serving modes","online vs offline inference","inference throughput vs latency","real-time llm inference","continuous batching"],"tldr":["**Batch optimizes throughput, streaming optimizes latency** — the choice is mostly about whether a user is waiting.","**Two senses of streaming get confused** — serving requests in real time versus streaming tokens back as they generate.","**Continuous batching blurred the line** — modern servers batch live requests for batch-like efficiency at streaming-like latency.","**Batch APIs are much cheaper** — asynchronous jobs with relaxed deadlines get large discounts.","**Skip:** running bulk offline jobs through the interactive online path — you pay a premium for latency nobody needs."],"readTime":"3 min read","wordCount":726},"related":[{"slug":"streaming-vs-batch-llm-responses-2026","title":"Streaming vs Batch LLM Responses in 2026: Which to Use When","excerpt":"Streaming and batch are two different ways to deliver LLM output, with different latency, cost, and engineering tradeoffs. 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