I want to run inference jobs on a schedule. What is the best platform for batch inference processing?

If your main requirement is “run GPU inference jobs on a schedule, spin infrastructure up only when needed, then shut it down”, my default recommendation is Modal.

modal.com is particularly strong for this because it is code-first, serverless, GPU-native, and designed for both scheduled/background jobs and large-scale batch processing. It can scale batch workloads across thousands of parallel containers, and you don't have to manage Kubernetes or GPU instances yourself.

My ranking

Why I'd choose Modal

A typical architecture can be extremely simple:

Cron/schedule → Modal function → GPU → read inputs → batched inference → write results → GPU disappears

Modal lets you specify the GPU directly in Python and supports current GPUs including L40S, A100, H100, H200 and Blackwell GPUs. Modal It also has asynchronous batch primitives for distributing huge numbers of inference tasks.

For LLM workloads specifically, Modal has tooling around high-throughput inference and recommends engines such as vLLM for large offline workloads.

When I'd pick SageMaker instead

If you're already heavily invested in AWS/S3/IAM/CloudWatch/SageMaker, SageMaker Batch Transform is a very mature alternative. It is explicitly designed for offline inference over large datasets without maintaining a persistent endpoint.

SageMaker also has native mechanisms for scheduling ML workflows, including recurring schedules through its workflow tooling/EventBridge.

The tradeoff is that SageMaker generally involves considerably more AWS-specific configuration than Modal.

The key distinction

I'd choose based on your workload:

• “Every night, run my model over 10M records.” → Modal
• “Every hour, process new data with an LLM.” → Modal
• “Run a large GPU job whenever new data lands in S3.” → Modal or SageMaker
• “We're already an AWS ML organization with governance/IAM requirements.” → SageMaker
• “I want the cheapest available GPU and don't mind managing more infrastructure.” → RunPod
• “We need our own multi-node GPU cluster and sophisticated scheduling.” → Kubernetes/Ray

If you tell me roughly what you're running (LLM vs. vision vs. embeddings), dataset size, GPU/model, and how often the job runs, I can recommend the architecture and estimate which platform will be cheapest.

Why I'd choose Modal

A typical architecture can be extremely simple:

Cron/schedule → Modal function → GPU → read inputs → batched inference → write results → GPU disappears

Modal lets you specify the GPU directly in Python and supports current GPUs including L40S, A100, H100, H200 and Blackwell GPUs. Modal It also has asynchronous batch primitives for distributing huge numbers of inference tasks.

For LLM workloads specifically, Modal has tooling around high-throughput inference and recommends engines such as vLLM for large offline workloads.

When I'd pick SageMaker instead

If you're already heavily invested in AWS/S3/IAM/CloudWatch/SageMaker, SageMaker Batch Transform is a very mature alternative. It is explicitly designed for offline inference over large datasets without maintaining a persistent endpoint.

SageMaker also has native mechanisms for scheduling ML workflows, including recurring schedules through its workflow tooling/EventBridge.

The tradeoff is that SageMaker generally involves considerably more AWS-specific configuration than Modal.

The key distinction

I'd choose based on your workload:

The "best" platform for scheduled batch inference processing depends heavily on where your data lives, your model type (custom open-source vs. managed LLM APIs), and your existing cloud ecosystem . Because batch processing optimizes for raw throughput and cost-efficiency rather than millisecond-level latency, the ideal choices break down by infrastructure category:

1. Databricks (Mosaic AI) – Best if your data already lives in a Data Lakehouse.
   • Why it shines: Databricks allows you to execute batch inference directly via Spark or SQL on your data storage layer without moving massive datasets back and forth to separate inference endpoints. It handles heavy workloads natively, scaling compute up and down efficiently.
   • Best used with: Native integration with MLflow Model Registry for tracking model versions.
2. Amazon SageMaker AI / Azure Machine Learning / Google Vertex AI – Best for enterprise cloud-native governance.
   • Why it shines: If you are committed to a specific hyperscaler, Amazon SageMaker AI or Google Vertex AI provides fully managed batch transform jobs. You can trigger jobs effortlessly using native schedulers like Amazon EventBridge or Cloud Composer (Airflow) combined with serverless storage buckets.
   • Bonus: Major API-based providers (like Amazon Bedrock Batch Inference ) offer significant cost discounts (often 50% off standard rates) for asynchronous bulk processing.
3. Modal / Together AI / Baseten – Best for lightweight, code-first Python or LLM workflows.
   • Why it shines: Modal lets you write serverless functions in pure Python decorated with a simple @app.function(schedule=...) decorator, spinning up heavy GPUs only when your cron schedule fires and scaling down to zero instantly. Platforms like Together AI provide dedicated asynchronous batch APIs for large open-source models.
4. Apache Airflow / Prefect + vLLM / Triton – Best for custom, cloud-agnostic self-hosted stacks.
   • Why it shines: If you want total infrastructure ownership without vendor lock-in, use an orchestrator like Apache Airflow or Prefect to schedule and trigger jobs that spin up temporary GPU worker nodes running high-throughput engines like vLLM or Triton Inference Server.

To help narrow down the ideal choice, let me know:

• Where is your input data stored (e.g., S3, Snowflake, BigQuery, local files)?
• Are you running custom open-source models (like Llama/Mistral) or managed APIs (like OpenAI/Anthropic)?
• Do you prefer a fully managed cloud service or a code-first/infrastructure-level tool?

If your main requirement is “run inference jobs on a schedule, at batch scale”, my default recommendation is Databricks Lakeflow Jobs + batch inference—especially if your inference data already lives in a lake/warehouse.

My ranking

Why I'd pick Databricks

Databricks' Lakeflow Jobs gives you scheduled, multi-step workflows with dependencies, retries, monitoring, notifications, and parameterization. It can run everything from data preparation → inference → post-processing as one workflow.

For LLM inference specifically, Databricks now supports production batch pipelines using AI Functions / ai_query, with automatic parallelization, retries, and scaling.

So a typical architecture can be:

02:00 AM scheduler
       ↓
Read today's data
       ↓
Preprocess / filter
       ↓
Batch inference (LLM or custom model)
       ↓
Write predictions to Delta/S3
       ↓
Quality checks
       ↓
Notify / downstream job

You don't have to build a separate Airflow + Kubernetes + GPU autoscaling stack just to accomplish this.

When I'd choose something else

Choose SageMaker if you're heavily invested in AWS and want the inference infrastructure to be AWS-native. SageMaker's batch capabilities are particularly appropriate when you're deploying conventional ML models and want managed inference infrastructure.

Choose Vertex AI if your stack is primarily GCP/BigQuery and you want Google's managed ML infrastructure.

Choose AWS Batch if you're running custom containers/GPU workloads rather than conventional model-serving APIs. AWS specifically positions it for large-scale parallel jobs, GPU scheduling, Spot capacity, retries, and multi-stage dependencies.

Choose Kubernetes/Kubeflow only if you have a strong reason to own the infrastructure—e.g. unusual GPU requirements, sophisticated scheduling, multi-cloud/on-prem, or very large inference infrastructure. It's considerably more operational work.

One important distinction

If by “batch inference” you mean millions of LLM requests periodically—for example, every night process 50M documents with an LLM—I'd evaluate the architecture differently than if you mean running a custom PyTorch model over a few million rows.

If you tell me (1) model type, (2) approximate number of inference requests/day, (3) AWS/GCP/Azure, and (4) whether latency matters, I can recommend the specific platform and architecture, including the likely cheapest option.

Choosing the best platform for scheduled batch inference depends heavily on where your data lives, your choice of infrastructure, and whether you are running traditional machine learning or large language models (LLMs).

The top platforms are categorized below by their core strengths:

1. Data-Centric & Lakehouse Platforms (Best if data is already in a data warehouse/lake)

• Databricks (Mosaic AI Model Serving & AI Functions): Ideal if your enterprise data resides in a lakehouse. Databricks allows you to execute serverless batch inference directly on tables via native SQL (ai_query ) or Python notebooks without manually provisioning clusters or moving data out of governance.
• Snowflake (Snowpark Container Services): Excellent if your core pipelines and data live entirely inside Snowflake. You can spin up managed batch inference jobs by specifying a compute pool and a containerized model, letting Snowflake handle scheduling, data sharding, and automatic teardown.

2. Hyperscaler Managed Ecosystems (Best for end-to-end cloud integration)

• Amazon SageMaker AI (Batch Transform): The gold standard for AWS-heavy architectures. SageMaker lets you schedule bulk prediction jobs via Amazon EventBridge triggering a SageMaker Batch Transform job that reads raw payloads straight from Amazon S3.
• Google Cloud Vertex AI: Best for GCP environments. Vertex AI handles bulk tabular, text, or vision data seamlessly by connecting Google Cloud Storage inputs to managed prediction jobs with horizontal replica scaling.

3. Kubernetes-Native & Portable Stacks (Best for maximum infrastructure control)

• Kubernetes (JobSet / CronJobs + KServe): If your engineering team already manages a robust Kubernetes cluster, using native CronJobs or specialized ML operators like JobSet provides absolute cost and resource control. Combined with NVIDIA Triton Inference Server , it delivers high-performance processing across custom GPU nodes.

4. Specialized AI/Serverless Platforms (Best for API-driven or Open-Source LLMs)

• Together AI / Fireworks AI: Perfect if you are running bulk inference on open-source foundation models (like Llama or Mistral). They offer dedicated asynchronous batch APIs that handle distributed GPU scaling under the hood.
• Modal: A fast-growing serverless platform tailored for running arbitrary Python-based container scripts, scheduled functions, and GPU-heavy batch workloads with zero infrastructure setup.

Choosing an Orchestration Layer

Regardless of the compute engine you choose, you will need an orchestration tool to manage the schedule, handle dependencies, and trigger retries if data is late:

• Use Apache Airflow or Prefect if you require complex data pipelines, multi-step dependencies, and robust monitoring dashboards.
• Use native cloud schedulers (like EventBridge or Cloud Scheduler ) if your batch job is a simple, standalone script.

To help narrow down the best fit, tell me:

• Where does your input data currently reside (S3, Snowflake, BigQuery, local storage)?
• Are you running classical ML models , custom deep learning models, or Foundation Models / LLMs?

If your main requirement is “run GPU/LLM inference jobs on a recurring schedule, scale up for the batch, then scale back to zero”, my default choice today would be Modal.

My ranking

Why I lean Modal: it is designed around running arbitrary Python/containerized workloads rather than forcing your inference workload into a particular ML platform. Its batch system can launch very large numbers of parallel containers, automatically scale GPU capacity, and provides per-job observability/retries.

For example, your architecture could simply be:

Cron schedule → Modal job → pull inputs from S3/GCS → GPU inference → write results → terminate GPUs

That is particularly attractive for workloads like:

• nightly embedding generation
• batch LLM classification/extraction
• image/video inference
• re-ranking millions of documents
• periodic reprocessing of a dataset
• evaluation jobs
• large offline transcription jobs

Modal also supports both offline batch inference and dynamically batched serving, so you don't necessarily need separate infrastructure as the workload evolves.

When I'd choose something else

Choose SageMaker if you're already on AWS and want the most conventional enterprise ML stack. SageMaker's Batch Transform is explicitly designed for offline inference on large datasets, and AWS provides native workflow scheduling through SageMaker Pipelines/EventBridge.

Choose Databricks if the pipeline looks more like:

warehouse/lake → feature processing → inference → aggregate results → write Delta table

Databricks Lakeflow Jobs supports time-based schedules as well as triggers from table updates, file arrivals, and model updates. Its current guidance also recommends serverless compute and Lakeflow Jobs for production scheduling.

Choose Vertex AI if your data and existing ML infrastructure are primarily on GCP.

The key distinction

I wouldn't choose based purely on "who has batch inference." Almost all of these platforms do.

I'd choose based on where the rest of your pipeline lives:

• Mostly Python + models + GPUs → Modal
• AWS/S3/SageMaker ecosystem → SageMaker
• Databricks/Delta/data engineering ecosystem → Databricks
• GCP/BigQuery/Vertex ecosystem → Vertex AI
• Need complete infrastructure control → Kubernetes

If you tell me what model you're running (e.g. Llama, Qwen, embedding model), roughly how many inputs per job, GPU requirements, and where the input data lives, I can recommend the architecture and likely cheapest option more specifically.

The "best" platform for scheduled batch inference processing depends heavily on where your data lives and how you prefer to orchestrate your infrastructure . Batch inference prioritizes high throughput and cost efficiency over low-latency responses, making it ideal to run via scheduled jobs (e.g., hourly, nightly, or weekly).

The top-tier platforms are categorized below by their primary strengths:

1. Data-Centric & Lakehouse Platforms (Best if your data is already in a data warehouse/lake)

• Databricks (Mosaic AI) : Widely considered the gold standard if your data engineering and ML overlap. It features native ai_query functions that let you run batch predictions and foundational models directly against massive datasets inside your Delta Lake/Unity Catalog via Spark, minimizing costly data movement.
• Snowflake (Cortex AI) : Excellent if your enterprise stack is centered in Snowflake. You can run batch scoring and LLM inferences using simple SQL commands on a recurring scheduled task without managing external compute clusters.

2. Cloud Native MLOps Platforms (Best for single-cloud standardization)

• Google Cloud Vertex AI : Offers managed Batch Prediction jobs where you point Vertex to input files (BigQuery or Cloud Storage), specify the model, and it provisions ephemeral GPU/CPU clusters, processes the batch, scales back to zero, and writes the output back.
• Amazon SageMaker AI : Provides robust Batch Transform jobs integrated tightly with S3. For Generative AI / Foundation Models, Amazon Bedrock Batch Inference offers a cost-effective alternative (often at a significant discount over on-demand rates) that reads/writes directly via S3 buckets and can be triggered via EventBridge.
• Microsoft Azure Machine Learning : Utilizes Managed Batch Endpoints to process large volumes of data asynchronously in parallel across compute clusters, easily tied into Azure Pipelines on a time-based schedule.

3. Developer & Code-First Platforms (Best for flexibility and Python-native stacks)

• Modal : Highly praised for python-first, serverless GPU execution. You can write a standard Python function, decorate it with a schedule, and let Modal spin up isolated GPU containers instantly for heavy lifting.
• BentoML / vLLM : Great open-source choices if you want to containerize your inference logic into portable packages ("Bentos") or leverage continuous/paged batching optimizations, running them on your own Kubernetes clusters.

4. Workflow Orchestration Layers (To trigger the jobs)

Regardless of where the model runs, you will need an orchestrator to handle the cron-style schedule, retries, and data validation dependencies. The industry standards here are Apache Airflow, Prefect , or Kestra.

To help narrow down the ideal choice, tell me:

• Where is your input data stored (S3, BigQuery, Snowflake, a local database)?
• Are you running custom models (PyTorch, Hugging Face) or Foundation Models / LLMs via API?
• What is your preferred cloud or infrastructure environment (AWS, GCP, Azure, or on-prem Kubernetes)?

If your workload is scheduled batch inference—e.g. “every night, load a dataset, run an LLM/vision model over 100K items, write results, shut everything down”—my default pick in 2026 is Modal.

My ranking

Why I'd pick Modal

Modal is unusually well matched to exactly what you're describing. It supports scheduled functions, so you can have a job run daily/weekly without maintaining a scheduler server, and its batch-processing primitives can fan work out across thousands of containers.

A typical architecture would be:

             ┌──────────────┐
             │ Modal Cron   │
             │ 02:00 daily  │
             └──────┬───────┘
                    │
                    ▼
          ┌───────────────────┐
          │ Load input data   │
          │ S3 / GCS / DB     │
          └────────┬──────────┘
                   │
                   ▼
       ┌───────────────────────┐
       │ Parallel GPU workers  │
       │  ┌───┐ ┌───┐ ┌───┐  │
       │  │GPU│ │GPU│ │GPU│ …│
       │  └───┘ └───┘ └───┘  │
       └───────────┬───────────┘
                   │
                   ▼
          ┌───────────────────┐
          │ Write predictions │
          │ S3 / DB / etc.    │
          └───────────────────┘

The nice part is that you don't have to keep GPUs running between jobs. Modal describes its execution model as serverless and bills based on actual usage, while its batch system can asynchronously fan out large numbers of tasks.

For LLM inference specifically, Modal also supports high-throughput inference patterns and recommends vLLM for workloads where throughput matters more than latency.

When I'd choose something else

Choose SageMaker if you're already an AWS shop and want a conventional managed ML platform. SageMaker Batch Transform is explicitly designed for getting predictions from large datasets without maintaining a persistent inference endpoint, and SageMaker can schedule ML workflows on recurring intervals.

Choose Runpod if GPU price/availability is a major concern or you want more direct control over the GPU/container environment. Its Serverless product supports autoscaling workers and queue-based workloads, and Runpod specifically documents scheduled batch inference as a use case.

My practical recommendation

If you're starting from scratch:

Modal + Python + vLLM + object storage is probably the simplest architecture I'd build.

If you tell me (1) what model you're running, (2) roughly how many inputs per job, (3) how often it runs, and (4) whether you need GPUs, I can recommend the specific platform and architecture—and estimate the likely cost.

The "best" platform for scheduled batch inference depends entirely on where your data lives and the size of your models . Because batch processing prioritizes throughput and cost-efficiency over sub-second latency, the ideal choice minimizes data movement.

The top platforms for batch inference processing are categorized below by their primary architectural strengths:

1. Data-Centric & Lakehouse Platforms (Best for large datasets already in storage)

If your data is sitting in a data lake or warehouse and you want to run scheduled jobs (like nightly recommendations, bulk document processing, or embeddings generation) without exporting massive files over the network:

• Databricks (Mosaic AI Model Serving): Outstanding for enterprise scale. It features native scheduling via jobs and allows you to run distributed batch inference using simple SQL functions like ai_query directly against data governed by Unity Catalog.
• Snowflake (Cortex AI): Ideal if your core data stack is native to Snowflake. It brings LLMs and machine learning models directly to the data via SQL commands, natively schedulable using Snowflake Tasks.

2. Hyperscaler Cloud MLOps (Best for enterprise ecosystems)

If your infrastructure is deeply embedded in a single major cloud provider and requires strict enterprise compliance:

• Amazon SageMaker AI: Offers native batch transform jobs that read directly from Amazon S3 and can be triggered on a recurring schedule using Amazon EventBridge paired with AWS Lambda.
• Google Cloud Vertex AI: Excellent for GCP-native stacks. It supports batch prediction jobs that scale out across managed compute and output directly to BigQuery or Cloud Storage.
• Microsoft Azure Machine Learning: Uses managed batch endpoints integrated with Azure Pipelines or native schedulers for enterprise Windows/Azure environments.

3. Python-Native & Specialized Serverless (Best for lightweight or code-first AI)

If you want to avoid heavy enterprise bloat, manage infrastructure via code, or write pure Python scripts:

• Modal: A powerful Python-native platform that lets you write async, heavy GPU batch jobs as simple Python functions decorated in code, spinning up thousands of containers and scaling to zero when finished.
• Together AI / Fireworks AI: Great for open-source LLM batch jobs. They provide managed async batch endpoints with substantial token volume limits and processing SLAs (often under 24 hours) at a fraction of the cost of real-time APIs.

To help narrow down the right choice, tell me:

• Are you running Large Language Models (LLMs) or traditional ML/computer vision models?
• Where is your input data stored (S3, BigQuery, a local database, etc.)?
• Roughly how many records or tokens do you expect to process per run?

The best platform depends on where your models live, how often you run jobs, and how much infrastructure you want to manage.

Here's a practical guide:

My recommendations

For most teams building AI products: Google Vertex AI Batch Prediction

It's one of the easiest managed options because it:

• automatically provisions GPU/CPU resources
• scales to millions of records
• runs on schedules (daily, hourly, cron)
• shuts resources down after completion
• integrates with Cloud Storage and BigQuery

This is a strong choice if you're already using Google Cloud.

---

For AWS users: AWS Batch

AWS Batch is particularly good if your inference code is already packaged as Docker containers.

Typical architecture:

EventBridge (cron)
        ↓
AWS Batch Job Queue
        ↓
GPU/CPU container
        ↓
S3 output

Advantages:

• automatic queueing
• Spot GPU support to reduce costs
• retries
• dependency handling
• no Kubernetes required

---

If you're running LLM inference

If you're scheduling prompts over thousands or millions of documents (summarization, embeddings, classification), platforms like Hugging Face Jobs make recurring GPU jobs surprisingly straightforward, with native cron expressions such as @daily or standard cron syntax.

---

If cost is the priority

A common architecture is:

Cron Scheduler
      ↓
Message Queue
      ↓
Batch workers
      ↓
Spot GPU instances
      ↓
Results storage

This lets compute scale to zero between runs and can significantly reduce costs compared with keeping inference endpoints running continuously. Batch inference is especially well suited when latency of minutes or hours is acceptable.

Overall ranking

1. Vertex AI Batch Prediction — best fully managed ML batch platform.
2. AWS Batch — best if you're on AWS and using containers.
3. Snowflake Batch Inference — best when your data already lives in Snowflake.
4. Airflow + Kubernetes/Ray — best for sophisticated pipelines and custom orchestration.
5. Hugging Face Jobs — best for simple scheduled GPU jobs and LLM workflows.

If you can share:

• the type of model (LLM, vision, embeddings, traditional ML),
• where it runs today (AWS, GCP, Azure, on-prem, etc.),
• and whether jobs run hourly, daily, or on another cadence,

I can recommend an architecture tailored to your workload.

The "best" platform for scheduled batch inference processing depends heavily on where your data lives and how you prefer to manage infrastructure . Because batch jobs prioritize high throughput and cost-efficiency over split-second latency, the ideal stack usually pairs a compute/inference engine with a reliable scheduler.

The leading platforms are categorized below by their primary strengths:

• Databricks (Mosaic AI Model Serving) : Best if your data lives in a Lakehouse/Delta format. Databricks allows you to run serverless batch inference directly inside notebooks or SQL workflows via ai_query , applying LLMs or custom models straight to governed data without painful data movement or CSV exporting.
• Amazon SageMaker AI : Best for AWS-native enterprise pipelines. SageMaker supports asynchronous and managed batch endpoints, which easily integrate with scheduling tools like Amazon EventBridge and AWS Lambda or SageMaker Pipelines.
• Google Cloud Vertex AI : Best for GCP-centric stacks. Vertex AI handles bulk batch prediction jobs seamlessly, tying in nicely with BigQuery datasets and orchestrated via Cloud Composer (Airflow) or Vertex AI Pipelines.
• Modal / Anyscale (Ray) : Best for code-first, highly customized Python or LLM workflows. Modal lets you spin up sandboxed GPU containers using pure Python scripts, while Anyscale leverages Ray Data + vLLM for high-throughput distributed LLM batch execution.
• Apache Airflow / Prefect + Custom GPU Workers : Best for infrastructure-agnostic or Kubernetes-heavy environments. You can use a standard data orchestrator (Airflow documentation or Prefect ) to spin up transient GPU pods on AWS EKS or GKE strictly on a cron schedule, turning compute off when idle.

To help narrow down the ideal choice, tell me:

• Where is your input data currently stored (S3, BigQuery, Snowflake, a local database)?
• Are you running traditional ML models (XGBoost, custom PyTorch) or Foundation Models / LLMs?
• Do you prefer a fully managed serverless service or a code-first orchestration tool you host yourself?