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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.

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.

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

Credit consumption examples

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

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

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.