---
title: "Pricing"
description: "Understand Pipeline pricing and resource plan costs"
---

# Pricing

## How Pipeline Billing Works

TIR Pipelines are billed based on the **resource plan** selected for each run. Key billing principles:

- **Per-hour billing** — You are charged for the duration of active run execution, billed hourly.
- **No idle charges** — There is no cost when pipelines have no active runs. You only pay during execution.
- **Resource-plan based** — Pricing varies by the CPU or GPU plan selected when creating a run.
- **Scheduled runs** — Each triggered execution is billed independently based on its duration and resource plan.

## Resource Plans

### CPU Plans

CPU plans are suitable for data preprocessing, lightweight inference, and orchestration tasks.

| Plan Type | Use Case |
| :--- | :--- |
| **CPU Basic** | Simple data processing, file transfers, orchestration |
| **CPU Standard** | Medium workloads, data transformation, batch processing |
| **CPU High** | CPU-intensive tasks, large-scale data preprocessing |

### GPU Plans

GPU plans are designed for training, fine-tuning, and GPU-accelerated workloads.

| Plan Type | Use Case |
| :--- | :--- |
| **GPU Basic** | Small model training, quick experiments |
| **GPU Standard** | Full model training, fine-tuning |
| **GPU High** | Large-scale training, multi-step GPU workflows |

:::info
For specific plan pricing and configurations, use the **E2E Calculator**. [Click here](https://calculator.e2enetworks.com/estimate-pricing) to estimate your costs.
:::

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## Pricing Examples

### Example 1: Short Training Job

**Scenario:** A single-step training pipeline running for 2 hours on a GPU plan.

- **Resource:** GPU Standard plan
- **Duration:** 2 hours
- **Billing:** 2 hours x GPU Standard hourly rate
- **Result storage:** Pipeline artifacts stored in EOS (billed separately under Datasets pricing)

### Example 2: Scheduled Batch Processing

**Scenario:** A daily scheduled pipeline for data preprocessing, running 30 minutes each time on a CPU plan.

- **Resource:** CPU Standard plan
- **Frequency:** Once daily (30 runs/month)
- **Duration per run:** 30 minutes (billed as 1 hour minimum or pro-rated)
- **Monthly billing:** 30 x CPU Standard hourly rate

### Example 3: Multi-step Argo Workflow

**Scenario:** A 3-step pipeline — data preprocessing (CPU, 1 hour), training (GPU, 4 hours), evaluation (CPU, 30 minutes).

- **Step 1:** CPU plan x 1 hour
- **Step 2:** GPU plan x 4 hours
- **Step 3:** CPU plan x 1 hour (rounded)
- **Total:** Sum of each step's resource-plan cost

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## Cost Optimization Tips

### 1. Right-size Resources
- Use **CPU plans** for data preprocessing, file transfers, and evaluation steps.
- Reserve **GPU plans** only for steps that require GPU acceleration (training, inference).
- Avoid selecting GPU plans for orchestration-only steps.

### 2. Use Scheduling Wisely
- Set appropriate `max_concurrency` on scheduled runs to prevent overlapping executions that consume duplicate resources.
- Remove or disable scheduled runs that are no longer needed.

### 3. Optimize Pipeline Steps
- Leverage the **retry mechanism** to resume failed jobs without re-running from scratch — this avoids paying for already-completed work.
- Store intermediate results in EOS buckets to avoid recomputation if a later step fails.
- Break large pipelines into smaller steps so that only failed steps need to be retried.

### 4. Clean Up Unused Resources
- Delete pipelines and versions that are no longer needed.
- Delete completed or failed runs to keep your workspace clean.



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