> ## 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

## 📘 Understanding Credits in Lyzr Agent Studio

Lyzr Agent Studio uses a **credit-based compute system** to manage infrastructure usage, ensure system fairness, and optimize for both low-latency experimentation and high-throughput production.

Every action that invokes computation — whether model inference, semantic indexing, or multi-agent orchestration — is **metered** in terms of **credits**. To simplify usage tracking, **1 credit now directly equals \$1**. This allows users to monitor and scale usage predictably across plans at a glance.

***

## 🔹 Credit Allocation and Plans

Lyzr credits are provisioned based on your account's subscription tier. Credits reset or replenish on a fixed schedule and can be supplemented as needed.

### Free Tier

* **5 credits/month** (\$5 value) automatically provisioned to each new account.
* These credits refresh **monthly** based on your signup date.
* Ideal for trying out agents, creating simple RAG flows, and basic API calls.

### Paid Plans

* Credit allocation scales with plan level (Starter, Pro, Enterprise).
* Plans are available on **monthly** or **annual** billing cycles.
* Top-ups can be purchased at any time.

### One-Time Top-Ups

* Used to prevent service interruptions when monthly quota is exhausted.
* Credits from top-ups **do not expire**.
* Stackable with recurring plan credits.

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## 🔹 What Consumes Credits?

Lyzr’s platform operations are broken down into credit-metered units. Because the system is now 1:1 with the dollar, usage now deducts **fractional credits**. The following subsystems contribute to credit consumption:

### 1. **Language Model Inference**

* **Token-based billing** per LLM (similar to OpenAI’s pricing model).
* Models have differing **price-per-token** multipliers:

| Model        | Relative Credit Usage | Typical Use Case                          |
| ------------ | --------------------- | ----------------------------------------- |
| GPT-4o Mini  | 1×                    | Lightweight chatbots, simple instructions |
| GPT-4o       | 16×                   | Strategic reasoning, agents with memory   |
| Claude Haiku | 2×                    | Midweight contextual interactions         |
| Claude Opus  | 15–20×                | Deep reasoning and multi-hop workflows    |

> 💡 *Heavier models yield higher accuracy, but also consume more tokens. Choose based on your use case.*

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### 2. **Token Length: Input + Output**

* Lyzr computes usage on the **combined token length** of:
  * **Prompt** (agent logic, user input, KB context)
  * **Response** (model output)
* This includes:
  * RAG-pulled knowledge
  * Agent history (if memory is enabled)
  * Instruction chains and multi-agent dependencies

> Example: A long SQL-generating prompt with schema context and 500-word response can consume up to 3–5× more credits than a standard chat exchange.

***

### 3. **Platform Features & Services**

| Feature                 | Credit Usage          | Notes                                                                  |
| ----------------------- | --------------------- | ---------------------------------------------------------------------- |
| **Memory**              | Free                  | Stores interaction context for multi-turn dialogue. No credit cost.    |
| **Tools Execution**     | Fixed Cost            | Each invocation of a 3P integration (e.g., Slack, Gmail) uses credits. |
| **Knowledge Base**      | Indirect              | No feature cost, but large documents increase prompt size.             |
| **Semantic Model**      | Indirect + Inference  | Embeds tabular metadata, uses credits when LLM is invoked.             |
| **Knowledge Graph**     | Indirect + Processing | Graph traversal + node summarization incurs token load.                |
| **Context Relevance**   | Moderate              | Adds 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 number of submodules invoked (e.g., profanity).        |

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## 🔹 Credit Consumption Examples

*Note: These values reflect the updated display where 1 credit = \$1. Typical usage results in fractional deductions.*

| Scenario                                                    | Approximate Credit 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 + RAI        | 0.80 – 1.20 credits     |

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## 🔹 Credit Management Best Practices

To avoid excessive credit burn, especially on larger workloads:

* ✅ **Use light models (GPT-4o Mini, Haiku)** during development and testing.
* ✅ **Limit file size and chunk count** in KBs when setting up RAG.
* ✅ **Disable unnecessary modules** like RAI or Groundedness for internal use-cases.
* ✅ **Use preview tools** in Studio to inspect prompt-token size before triggering agent run.

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## 🔹 Monitoring Credit Usage (Coming Soon)

Lyzr will soon support **in-dashboard analytics** to monitor:

* Credit burn by agent
* Model-specific usage
* Peak usage windows
* Alerts for low-credit thresholds

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## 🔹 Credit Expiry and Overflow

| Plan Type       | Expiry Rules         | Overflow Handling                             |
| --------------- | -------------------- | --------------------------------------------- |
| Free Plan       | Resets every 30 days | No rollover                                   |
| Monthly Plan    | Resets monthly       | No rollover unless top-up applied             |
| Annual Plan     | Renewed annually     | Quota spreads evenly or loaded in full        |
| One-Time Top-Up | **Never expires**    | Used after subscription credits are exhausted |

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## Summary

| Component          | Role                                                          |
| ------------------ | ------------------------------------------------------------- |
| Credits            | Unit of Lyzr compute usage (**1 credit = \$1**)               |
| Tracked Dimensions | Model, token count, tools, features                           |
| Replenishment      | Monthly/annual plans or one-time top-ups                      |
| Monitoring         | In progress (dashboard reporting soon)                        |
| Optimization       | Choose light models, minimize token overhead, selective tools |

***

With this understanding, developers and builders on Lyzr can architect intelligent agents **efficiently and affordably**, optimizing both performance and compute cost.