> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Understanding Credits

> How Lyzr measures and bills for compute usage.

Lyzr Agent Studio uses a credit-based compute system to meter infrastructure usage and let you predict costs at a glance. Every action that invokes computation (model inference, semantic indexing, multi-agent orchestration) is measured in credits. One credit equals \$1.

## Credit allocation and plans

Lyzr credits are provisioned based on your subscription tier and reset or replenish on a fixed schedule. Top-ups can be purchased at any time and are stackable with plan credits.

| Plan                     | Credits                | Notes                                                                 |
| ------------------------ | ---------------------- | --------------------------------------------------------------------- |
| Free                     | \$20 credits/month     | Resets monthly on your signup date. No rollover.                      |
| Starter, Pro, Enterprise | Scales with plan level | Monthly or annual billing.                                            |
| One-time top-up          | Any amount             | Credits do not expire. Used after subscription credits are exhausted. |

## What consumes credits

### Language model inference

Credits are deducted based on token count multiplied by a per-model rate. Heavier models consume more credits per token.

| Model        | Relative credit usage | Typical use case                          |
| ------------ | --------------------- | ----------------------------------------- |
| GPT-4o Mini  | 1x                    | Lightweight chatbots, simple instructions |
| GPT-4o       | 16x                   | Strategic reasoning, agents with memory   |
| Claude Haiku | 2x                    | Mid-weight contextual interactions        |
| Claude Opus  | 15-20x                | Deep reasoning and multi-hop workflows    |

### Token length

Lyzr computes usage on the combined token length of the prompt (agent logic, user input, Knowledge Base context) and the response (model output). This includes RAG-retrieved knowledge, agent history when memory is enabled, and instruction chains in multi-agent workflows. A long SQL-generating prompt with schema context and a 500-word response can consume three to five times more credits than a standard chat exchange.

### Platform features

| Feature             | Credit usage          | Notes                                                                           |
| ------------------- | --------------------- | ------------------------------------------------------------------------------- |
| Memory              | Free                  | Stores interaction context for multi-turn dialogue.                             |
| Tools execution     | Fixed per invocation  | Each call to a third-party integration (Slack, Gmail, and others) uses credits. |
| Knowledge Base      | Indirect              | No direct feature cost, but large documents increase prompt size.               |
| Semantic Model      | Indirect + inference  | Embeds tabular metadata; credits are used when the LLM is invoked.              |
| Knowledge Graph     | Indirect + processing | Graph traversal and node summarization add token load.                          |
| Context Relevance   | Moderate              | Adds an LLM-based ranking layer over retrieved chunks.                          |
| Groundedness checks | Fixed per use         | Post-response factuality validation using internal validators.                  |
| Responsible AI      | Variable              | Cost depends on the number of submodules invoked.                               |

## Credit consumption examples

These values assume 1 credit = \$1. Typical usage results in fractional deductions.

| Scenario                                                        | Approximate cost    |
| --------------------------------------------------------------- | ------------------- |
| Simple agent query using GPT-4o Mini                            | 0.01 - 0.02 credits |
| Tool-based agent querying Gmail + Slack                         | 0.10 - 0.25 credits |
| RAG agent using 5 PDF files with GPT-4                          | 0.20 - 0.60 credits |
| Text-to-SQL with Semantic Model (with schema + sample rows)     | 0.15 - 0.50 credits |
| Agent using Claude Opus with Context Relevance + Responsible AI | 0.80 - 1.20 credits |

## Credit management best practices

To avoid excessive credit burn on larger workloads:

* Use light models (GPT-4o Mini, Claude Haiku) during development and testing.
* Limit file size and chunk count in Knowledge Bases when setting up RAG.
* Disable Responsible AI and Groundedness modules for internal use cases that don't require them.
* Use the prompt preview tools in Agent Studio to inspect token size before triggering an agent run.

## Credit expiry and overflow

| Plan type       | Expiry               | Overflow handling                                |
| --------------- | -------------------- | ------------------------------------------------ |
| Free            | Resets every 30 days | No rollover                                      |
| Monthly plan    | Resets monthly       | No rollover unless a top-up is applied           |
| Annual plan     | Renewed annually     | Quota spreads evenly or loads in full            |
| One-time top-up | Never expires        | Applied after subscription credits are exhausted |

## Monitoring credit usage

In-dashboard analytics for credit burn by agent, model-specific usage, peak usage windows, and low-credit alerts are coming soon.

<Note>
  Want to know exactly what each agent run costs by type? See [Pricing](/enterprise/get-started/pricing) for per-run rates broken down by agent complexity.
</Note>