Cheapest Batch Inference for Low-Cost AI Inference: 6 Providers Compared (2026)

• The batch discount is real, and it is usually exactly 50%. DigitalOcean, Fireworks AI, Nebius, and AWS Bedrock all discount asynchronous requests by 50% against their real-time per-token rates, in exchange for a turnaround window of roughly 24 hours.
• “Up to 50% off” is not the same as “50% off your model.” Together AI markets up to 50% batch savings, but its documentation lists only two discounted models, meta-llama/Llama-3.3-70B-Instruct-Turbo and openai/whisper-large-v3, and states that “models not listed run at standard rates.” Check the model list before you assume a saving.
• A guaranteed window is worth more than a slightly better rate. DigitalOcean commits to a 24-hour results window. AWS states plainly that there are “no guaranteed SLAs for the Batch Inference API”, and Together’s window is best-effort.
• Only pay for what actually finished. DigitalOcean and Together AI both bill only successful requests, so a job that fails or expires halfway through does not bill for the remainder. This matters more than the headline rate on jobs that partially fail.
• Renting a GPU is not batch inference. Vast.ai, RunPod, Thunder Compute, and Lambda sell GPU time by the hour or second. None of them offers a per-token batch API with a batch discount, so your effective cost per token depends entirely on how well you saturate the card.
• The cheapest provider depends on your model, not on the vendor. For OpenAI and Anthropic commercial models, DigitalOcean’s flat 50% is the most predictable. For open-weight models, Fireworks AI stacks a batch discount on top of prompt caching. Do the arithmetic for your specific model.

Batch inference means submitting a large set of requests as a single job, usually a JSONL file where each line is one request, and retrieving results within a defined window rather than instantly.

The reason it costs less is scheduling. A real-time request must be served the moment it arrives, so the provider has to hold GPU capacity in reserve for spikes that may never come. A batch job carries no such promise, so the provider can slot your work into whatever capacity is idle and pass part of that efficiency back to you. DigitalOcean describes this directly: batch jobs run by using off-peak GPU capacity.

The discount gets the attention, but there is a second benefit. Batch jobs typically run against a separate rate-limit pool from your production traffic, so a 40,000-request job does not push your live application into 429 Too Many Requests errors. On several providers that is reason enough to use batch even when the money is a wash.

Batch inference fits:

• Model evaluations and prompt benchmarking
• Bulk classification, labeling, and sentiment analysis
• Content generation at scale, such as catalog copy or summaries
• Document processing and structured data extraction
• Embedding generation for a corpus you are indexing once
• Nightly or scheduled offline pipelines

It does not fit anything a person is waiting on: chat, live agents, search-as-you-type, or interactive tools. Those need real-time inference. For a fuller breakdown of when each mode makes sense, see DigitalOcean inference mode comparison.

Quick picks. Most predictable discount: DigitalOcean. Largest jobs and best prompt-cache stacking: Fireworks AI. Broadest open-weight catalog with residency options: Nebius. Highest enqueued token ceiling: Together AI. Already locked into AWS: Bedrock. No queue at all: Modal.

How we chose these five

These five were selected because each runs a distinct batch-inference story - either a dedicated batch discount program or a billing model specifically relevant to batch workloads - rather than adding to a long tail of similar real-time per-token rates. Other providers were considered and excluded because they don’t currently offer a batch discount mechanism distinct from their standard pricing. Every figure below is sourced from the provider’s own pricing or documentation page; batch pricing changes frequently, so treat this as a snapshot and confirm current rates before committing to high-volume usage.

Verdict: the simplest path to a guaranteed batch discount if you want one flat rate to plan around. DigitalOcean’s Batch Inference, available on its Inference Engine, discounts asynchronous batch requests on OpenAI and Anthropic models by up to 50% compared to real-time inference, and bills only for completed requests - if a job fails, is blocked by guardrails, or expires partway through, unprocessed requests aren’t charged

Worked cost example: 50,000 requests through Claude Opus 4.6, averaging 1,000 input and 500 output tokens each (50 million input tokens and 25 million output tokens total) - puts real-time cost at $875 versus $437.50 for the same volume through Batch Inference, a savings of $437.50, or 50%.

• Pros: simple, predictable ~50% discount; no charge for incomplete requests; fits naturally alongside existing compute if you’re already running workloads on DigitalOcean.
• Cons: ensure the model catalogue includes your desired model

Verdict: the highest per-batch token ceiling of the five, suited to genuinely large jobs. Together AI’s Batch API discounts most serverless model requests by up to 50% compared to real-time pricing, with a best-effort turnaround SLA of under 24 hours (often completing in just hours) and support for up to 30 billion enqueued tokens per model (Together AI Batch Inference).

The discount applies specifically to selected serverless models; batch jobs run on dedicated endpoints are billed at full price rather than the discounted rate (Together AI inference pricing docs).

• Pros: very high per-batch token ceiling; batch rate limits are separate from real-time usage, so batch jobs don’t compete with production traffic.
• Cons: the discount doesn’t extend to dedicated endpoint deployments.

Verdict: the clearest cost scaling as you move between model sizes. Fireworks AI bills batch inference at 50% of its serverless per-token rate, for both input and output tokens. Its base serverless rates for models outside the named-model list are tiered by parameter count - under 4B parameters at $0.10 per 1M tokens, 4B–16B at $0.20, and over 16B at $0.90 - so effective batch cost scales with the model chosen rather than sitting at one flat rate (Fireworks AI serverless pricing docs).

• Pros: transparent tiering makes it straightforward to estimate cost when moving between smaller and larger models; consistent 50% batch discount across the catalog.
• Cons: because pricing is tiered by model size, cost can shift meaningfully when switching to a larger model for better quality.

Verdict: the strongest fit when data residency requirements or very large batch payloads are the deciding factor. Nebius automatically discounts batch requests by 50% off the real-time base price of a model, rounded up to the nearest cent, and supports batch payloads of up to 10GB per request, with most jobs completing within 24 hours (Nebius managed inference, Nebius Token Factory batch inference operation reference).

• Pros: automatic discount with no separate opt-in step; large payload ceiling suits big offline jobs; EU hosting matters if a workload has data-residency requirements.
• Cons: less commonly the default choice outside contexts where EU residency specifically matters.

Verdict: the best fit when job efficiency, not a fixed discount, is what actually drives cost down. Modal doesn’t run a dedicated batch-discount program for AI inference. Instead, it bills GPU compute per second (rates range from roughly $0.000164/sec for an Nvidia T4 up to $0.001736/sec for a B200), scales to zero when idle so there are no idle charges, and its Starter plan includes $30 per month in free compute credit - so its cost-effectiveness for batch workloads comes from precise, granular billing rather than a fixed discount percentage (Modal pricing page).

• Pros: no idle charges; sub-minute billing granularity rewards efficient job design; no queue or SLA to wait out.
• Cons: no guaranteed discount percentage - total batch cost depends entirely on job efficiency and GPU tier chosen.

The five providers split into two groups, and the difference matters when choosing one: providers with a fixed discount and a defined wait (DigitalOcean, Together AI, Fireworks AI, Nebius) guarantee roughly 50% off real-time pricing but return results within a turnaround window, often up to 24 hours. Modal, with no fixed discount and no wait, charges per second of actual compute with no queue, but offers no guaranteed percentage savings - effective cost depends on how efficiently the job runs.

If a workload can tolerate a multi-hour wait and a predictable discount is the priority, the first group is the simpler choice. If results are needed faster than a batch SLA allows, or the job is short and bursty, granular per-second billing can end up cheaper without carrying a formal “batch” label at all.

No - price is a useful starting filter, but it shouldn’t be the only factor. Turnaround SLA needs to fit the workload (a 24-hour window suits a nightly job, not anything time-sensitive); model catalog matters if a specific model isn’t hosted by the cheapest provider; and rate limits or data residency requirements can rule out an otherwise-cheap option. Weigh batch pricing alongside these factors rather than choosing on cost alone.

What is the cheapest AI batch inference provider?

It depends on the workload. DigitalOcean, Together AI, Fireworks AI, and Nebius all offer roughly 50% off real-time rates for batch jobs, while Modal’s per-second billing can be cheaper still for small, efficient jobs since there’s no minimum discount tier to factor in.

How much can you save with batch inference vs. real-time inference?

Most dedicated batch APIs discount 40–50% off real-time per-token pricing, typically in exchange for a turnaround window - often up to 24 hours - instead of an immediate response.

What’s the difference between batch and real-time inference?

Real-time inference returns a response immediately for interactive use cases like chat or live agents. Batch inference processes large volumes of requests asynchronously, which lets providers schedule workloads more efficiently and pass some of the savings on.

Does every provider offer a batch inference discount?

No. DigitalOcean, Together AI, Fireworks AI, and Nebius each run an explicit batch discount program (typically ~50% off real-time). Modal bills per second of GPU time instead, with no separate discounted batch tier.

What use cases are best suited to batch inference?

Workloads that can tolerate delay: model evaluations, bulk classification, large-scale content generation, data enrichment, and nightly or scheduled offline processing jobs.

Is the cheapest batch inference provider always the right choice?

Not necessarily. Turnaround SLA, model catalog, rate limits, and data residency requirements matter alongside price - the cheapest option on paper isn’t useful if it doesn’t fit a workload’s constraints.

How is batch inference billed - per token or per GPU-hour?

Both models exist. DigitalOcean, Together AI, Fireworks AI, and Nebius discount per-token pricing for batch jobs. Modal bills per second of GPU time regardless of request volume, so its cost is tied to compute time rather than token count.

The shortest path to a running job is DigitalOcean’s Batch Inference guide: prepare a JSONL file, request a presigned upload URL, PUT the file, and create the job with a provider and a 24h completion window. For a full pipeline with chunking, retries, and reconciliation at scale, see processing a million documents overnight.

Get started with Batch Inference on the DigitalOcean Inference Engine and run your next high-volume job at up to 50% off real-time rates.