Every agent consumes credits when it works. This guide covers how to understand credit usage, reduce costs, and improve agent performance through iterative refinement.Documentation Index
Fetch the complete documentation index at: https://docs.abundly.ai/llms.txt
Use this file to discover all available pages before exploring further.
Understanding credit usage
Credit consumption depends on what your agent does, how complex the task is, and which LLM you use. Every case is unique, but here are examples to give you a sense of scale:| Task | Credits | Start tier | Pro tier |
|---|---|---|---|
| Simple news check of a couple of sources | 50 | $0.30 | $0.22 |
| Complex news scan (many sources, check memory, assess recency) | 400 | $2.30 | $1.80 |
| Review 40-page doc against 200 pages of references | 300 | $1.80 | $1.30 |
| Create weekly menu with recipes, check nearby deals, email user | 400 | $2.30 | $1.80 |
| Screen 300 acquisition targets based on industry and ownership | 5,000 | $29.00 | $22.00 |
| Analyze meeting transcript + draft follow-up email and CRM summary | 175 | $1.00 | $0.80 |
Examples from February 2026. Credit consumption varies over time depending on LLM and supplier prices, and tier pricing is subject to change.
Reducing costs
Several factors affect how many credits your agent consumes:1. Frequency of use
Running a task every few minutes versus once an hour makes a significant difference. Consider whether your agent really needs to run that often, or if a less frequent schedule would work.2. Size of context
The more information your agent reads and processes, the more credits it uses. Trim context to only what’s essential for the task. In chat conversations, you can monitor this directly with the context indicator in the chat action bar (desktop). The ring fills and shifts from gray to yellow, orange, and red as context grows. When the indicator gets high, start a new chat for a fresh context window. Keep durable reference material in agent documents so you can start fresh conversations without losing important information.3. Where you store data
Store reference information in agent documents that the agent can selectively retrieve, rather than including everything in the instructions. With the default “Summary” visibility, the agent knows documents exist but only reads them when relevant—keeping context focused.4. Complexity of instructions
Simplify logic and reduce unnecessary processing loops. Clear, concise instructions often perform better than lengthy, detailed ones.5. Model selection
By default, agents use the latest version of Claude Sonnet—a high-performance model optimized for agentic behavior. If cost is a concern and your use case allows it, considering switching to a cheaper model. See Model Selection for details.Learn more
Usage & Limits
Track credit consumption and set spending limits
Agent Design
A structured approach to designing effective agents
Pricing
Understand credit-based pricing and subscription plans