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.Want to know exactly what each agent run costs by type? See Pricing for per-run rates broken down by agent complexity.