Understanding and Maximizing Your Trellis Credits
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Trellis uses a credit system to meter its agentic AI capabilities, helping ensure predictable pricing and feature availability for all users. This guide explains how credits are generally consumed and how to get the most value out of every prompt.
How Trellis Credits Work
All users receive a monthly credit allowance of 100 credits, included in their base plan.
- Users without Trellis Plus: Your 100-credit limit resets on your monthly billing date.
- Trellis Plus for Extended Use: If your team requires more credits, you can upgrade to Trellis Plus, which grants extended credits, subject to fair use limits. Trellis Plus costs $35 per user per month for monthly subscribers, or $29 per user per month for annual subscribers. For Trellis Plus users, your credit limit resets each month based on your subscription date.
- Note on Usage: Credits are only utilized when you use Trellis Chat and Skills. Sprout AI Assist experiences do not count toward your usage limits.
What Factors Impact Credit Usage?
Credit usage is primarily driven by the complexity of your prompt and the number of MCP tools used to gather and return data. The length of your prompt does not necessarily equate to the credits used.
Credit consumption depends on many interacting factors and can vary between similar-looking requests. The patterns below reflect general tendencies we typically see, not fixed rules or guaranteed outcomes — the same type of question can cost more or less depending on your specific data, connected accounts, and conversation history.
| Tends to Use More Credits | Tends to Use Fewer Credits |
|---|---|
| Broad or Vague Questions — Queries asking for general "themes" or "interesting things" without narrowing the scope tend to consume more credits. | Narrow, Specific Questions — Questions scoped to a single network, a recent time window, or a specific metric tend to use fewer credits. |
| Large Data Sets — Queries spanning long periods (months or years), or asking Trellis to digest a large number of messages, tend to use more credits. | Specific Follow-Ups — If Trellis already fetched the data for a detailed first response, asking for more detail on that data (like displaying the messages themselves) is typically less expensive. |
| Multi-Source Questions — Questions that require pulling data from multiple sources simultaneously (e.g., an inbox and listening data, or Instagram and LinkedIn) tend to use more credits. | Clarifying Interactions — When Trellis asks you a clarifying question instead of providing an answer, this typically costs very few credits. |
| Compounding Follow-ups — Asking for a new kind of analysis within a long, existing chat thread can cause credit usage to increase significantly, since Trellis has to review the whole thread context. | Writing a Long, Specific Prompt — A long, specific prompt tends to use credits more efficiently than a short, vague one, since it clearly defines what Trellis should process. |
Tracking Credit Usage
If you are not using Trellis Plus, you can track where you are within your 100-credit limit by clicking the small usage indicator link at the bottom of your Trellis chat window.
Free Trellis users can also check credit availability and usage at any time by going to Settings > Global Features > AI Settings.
3 Tips That May Help Conserve Credits
These best practices can help you get more value from your credit allocation, though actual savings will vary by use case:
1. Be Specific in Your Prompts
Including details such as the specific topics, profiles, and date ranges you want Trellis to analyze can reduce the need for back-and-forth clarifying questions, which may help conserve credits.
2. Start a Fresh Chat for New Topics
Starting a new chat thread whenever you switch to a new conversation topic or goal can help manage credit use. The longer your chat thread, the larger the context window Trellis must review before each response — which can burn through credits more quickly.
3. Leverage Conversation Starters and Skills in Trellis Studio
Trellis Skills generally use more credits than a simple prompt, but they're professionally tested and designed to return high-value, actionable insights — which can offer a better return on investment even at a higher credit cost.
Examples
Not all prompts are created equal. To help set expectations, we've grouped example prompts into three general classes based on typical complexity and resource usage. These classes are illustrative starting points, not guarantees — the same phrased prompt can land in a different tier depending on your data volume, number of connected accounts, and conversation history. For example, a "trending themes" question against a small, recent dataset will typically behave like Class 1, but the same question against a high-volume topic with months of history can use meaningfully more credits.
Class 1 — Quick Prompts
Everyday, typically low-cost interactions. Often used for daily check-ins and ad-hoc questions, these usually return fast and use minimal credits.
- Checking trending themes in your Listening data
- Comparing engagement across tagged campaigns
- Generating a simple sentiment trend chart
- Identifying themes driving negative sentiment
- Pulling your daily inbox briefing
- Comparing top vs. lowest performing posts
Class 2 — Standard Analysis
Prompts that typically involve meaningful analysis over moderate datasets. Often suited for weekly reviews, content planning, and campaign optimization.
- Generating post ideas from listening trends
- Identifying trending themes across your listening data
- Summarizing what messages are saying about a topic
- Monthly content performance summaries compared to prior periods
- Pulling and summarizing sentiment across a set of messages (e.g., top 100 posts on a topic)
Class 3 — Deep Analysis
Typically the most resource-intensive prompts: large-scale, cross-source, or multi-year analyses. Often best reserved for strategic deep-dives like quarterly reports and competitive intelligence.
- Identifying common themes across a topic and analyzing trends over time
- Multi-year competitive analysis with per-year theme breakdowns
- Cross-source comparisons (e.g., inbox vs. listening data with sentiment analysis)
- Full content performance analysis spanning multiple months
See also: Trellis Overview · Studio Deep Dive · Using the Trellis Performance and Engagement Agent · Trellis Plus · Pro Tips for Writing Great Prompts · Trellis Fair Use Cap
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