> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praxis-ai.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Plans and credits
> Understanding plans, credit usage and credit rollovers in Praxis AI
## Choose Your Model
Each new user receives **50 free credits** to explore the platform when they get started with their Digital Twin. Once you are ready for more credits, you can set up a subscription plan or purchase a discounted credit bundle.
You can browse credit bundles by scrolling to the bottom of your [Digital Twin gallery](/mdx/user-guide/interface/digital-expert-gallery) and clicking on **Add Credits**. These bundles can be purchased either individually or through your institution, ensuring uninterrupted access to AI-powered learning and productivity tools.
When considering which credit bundle is right for you, keep in mind that credits NEVER expire!
## Per Credit Usage
The **Per Credit Usage** model allows you to buy bundles of credits and apply them to your personal account or to the Digital Twins you manage for your school or organization.
Each interaction consumes credits based on the tokens it processes. Approximately **1 credit is used per 10,000 tokens** of combined input and output, with a 1-credit minimum per AI call. Failed AI calls are never billed.
**Bring Your Own Keys (BYOK):** Customers who provide their own Large Language Model API credentials (OpenAI, Anthropic, Gemini, Mistral, etc.) receive a substantial 40% discount on standard pricing, allowing you to maintain direct control over AI usage costs while leveraging your existing provider relationships and enterprise agreements. The discount applies to base service fees while standard setup and support charges remain unchanged. For details on connecting your own models, see [Bring Your Own Model](/mdx/introduction/byot).
## Credit Bundles
All credit packages are one-time purchases and **credits never expire**. Pricing starts at 11 cents per credit for the base tier, with increasing discounts as you purchase larger bundles. Savings percentages are calculated relative to the base rate of 11¢/credit.
**Standard Packages for Personal Use**
| **Package** | **Price** | **Credits** | **Per Credit** | **Savings** |
| ----------- | --------- | ----------- | -------------- | ----------- |
| Silver | \$10 | 90 | 11.00¢ | — |
| Premium | \$25 | 238 | 10.50¢ | Save 5% |
| Pro | \$50 | 500 | 10.00¢ | Save 9% |
| Gold | \$120 | 1,263 | 9.50¢ | Save 14% |
| Diamond | \$300 | 3,333 | 9.00¢ | Save 18% |
**Select in the Gallery**
**Personal Use**
Each user receives a default personal account called Pria, which serves as your dedicated digital assistant for handling personal tasks and inquiries. This account is designed specifically for your individual needs and can be managed independently by purchasing and adding credits directly to maintain its functionality and access to various services.
By default your personal account is awarded 50 credits to start.
Requests made in Public or shared Digital Twins that don't poll credits will draw from your personal credit pool, so purchase credits for your personal account to continue using them.
**Volume Packages for your Digital Twin**
| **Package** | **Price** | **Credits** | **Per Credit** | **Savings** |
| ----------- | --------- | ----------- | -------------- | ----------- |
| Level 1 | \$1,000 | 11,428 | 8.75¢ | Save 20% |
| Level 2 | \$2,750 | 32,352 | 8.50¢ | Save 23% |
| Level 3 | \$5,000 | 60,606 | 8.25¢ | Save 25% |
| Level 4 | \$9,500 | 117,283 | 8.10¢ | Save 26% |
| Level 5 | \$41,500 | 518,750 | 8.00¢ | Save 27% |
Larger volumes (Level 6 and beyond) are available by arrangement — contact [humans@praxis-ai.com](mailto:humans@praxis-ai.com).
**Select in the Gallery**
**Digital Twin Use**
Credits added to your digital twin can be pooled and shared among all members of the digital twin community. When the **Pool Credits** setting is enabled in the [Configuration](/mdx/admin-guide/configuration), users will automatically draw from the pooled credit balance instead of consuming their individual personal credits, creating a collaborative resource system that benefits the entire group.
**Account-Level Credits**
For institutions managing multiple Digital Twins, credits can also be managed at the **Account** level. An Account groups multiple Digital Twin instances together, allowing centralized credit distribution across all instances. Contact your administrator for account-level credit management.
Digital twins can be configured to either pool credits on behalf of their members or operate without credit pooling, requiring members to rely on their own personal credit balances. When pooling is disabled, each user must maintain and consume their individual credits for accessing services and features within the digital twin environment.
## Caching Discounts
"Caching" is essentially the AI "remembering" parts of your conversation to work faster and more efficiently the next time.
**How it saves you money:** When the AI uses this "memory" instead of processing everything from scratch, it costs you less — the cached portion is usually around 50% cheaper. When you use models that support caching (for example, OpenAI GPT‑5, GPT‑Realtime, or Claude Sonnet v4, which typically provide stronger caching behavior), you will benefit from these savings. These savings also apply in Conversation Mode.
**How to see savings:** You don't have to guess if you're saving money. You can view your actual savings in the **Dialog Report Card** or the **Admin → History** section of your account, so administrators can review and validate the realized credit reductions.
### Token and Credit Calculation
When an interaction runs, several token metrics are tracked and used to determine the final number of credits billed.
### Definitions
**Tokens**\
Total number of tokens processed by the model, including both input and output.
**Input Tokens**\
Number of tokens sent *to* the model for the interaction (prompt, system instructions, tools, etc.).
**Input Cached Discount**\
Portion of the input tokens that are recognized as cached and therefore discounted from the billable input. These tokens still count toward usage, but not fully toward cost.
**Discount Ratio**\
Percentage discount applied to the cached portion of the input tokens. A higher ratio means a larger cost reduction from caching.
**Baseline Tokens**\
Final effective token count used to compute credits after applying the caching discount. This is the value that is converted into credits.
### Example
In the example below, without any caching benefit, the interaction would have cost **5 credits**.
With caching enabled:
* Some of the input is recognized as cached
* The **Input Cached Discount** is applied
* The **Discount Ratio** determines how much of those cached tokens are discounted
After applying the discount, only **4 credits** are billed, which corresponds to a **24% discount** compared to the original 5-credit cost.
### Completion
Content generated by the LLM counts toward **Completion tokens** (output). For most models that support caching, these completion tokens are generally **around 10× more expensive** than input tokens. It is important to note that caching is applied **only to input tokens**—completion tokens are **never cached** by the underlying models.
To your direct benefit, Praxis AI does **not** introduce any surcharge or special markup for completion tokens:
* Completion tokens are billed using the **same pricing curve** as input tokens.
* **Caching discounts apply only to input tokens** when the underlying model supports input caching.
* As a result, you get transparent, predictable pricing for all generated output, without hidden multipliers or extra completion-specific fees.
### Credit Optimization
See [Credits Optimization](/mdx/user-guide/credits-optimization) for ways to optimize your credit usage.
## Named User Subscription
The **Named User Subscription** model is an annual contract that allows Client to purchase a per named user subscription that can be used anytime, anywhere, with an aggregated number of maximum credits based on the size of the population; calculated as one thousand (1,000) credits x number of named users. For example, a population of 500 named users would have a combined 500,000 credits to be used among all users each year. This model is perfect for schools, corporations, and other entities that want to pool credits on behalf of their users.
Below is the volume breakdown:
| **Tier** | **Users** | **Description** |
| -------- | ---------------- | ---------------------------------- |
| 1 | Up to 1,000 | Annual subscription per named user |
| 2 | 1,001 to 5,000 | Annual subscription per named user |
| 3 | 5,001 to 10,000 | Annual subscription per named user |
| 4 | 10,001 to 15,000 | Annual subscription per named user |
| 5 | 15,001 to 50,000 | Annual subscription per named user |
| 6 | 50,001 to 75,000 | Annual subscription per named user |
| 7 | 75,000+ | Annual subscription per named user |
Contact our sales representative to jump on this plan structure.