📘 Understanding Credits in Lyzr Agent Studio
Lyzr Agent Studio uses a credit-based compute system to manage infrastructure usage, ensure system fairness, and optimize for both low-latency experimentation and high-throughput production. Every action that invokes computation — whether model inference, semantic indexing, or multi-agent orchestration — is metered in terms of credits. This allows users to monitor and scale usage predictably across plans.🔹 Credit Allocation and Plans
Lyzr credits are provisioned based on your account’s subscription tier. Credits reset or replenish on a fixed schedule and can be supplemented as needed.Free Tier
- 500 credits/month automatically provisioned to each new account.
- These credits refresh monthly based on your signup date.
- Ideal for trying out agents, creating simple RAG flows, and basic API calls.
Paid Plans
- Credit allocation scales with plan level (Starter, Pro, Enterprise).
- Plans are available on monthly or annual billing cycles.
- Top-ups can be purchased at any time.
One-Time Top-Ups
- Used to prevent service interruptions when monthly quota is exhausted.
- Credits from top-ups do not expire.
- Stackable with recurring plan credits.
🔹 What Consumes Credits?
Lyzr’s platform operations are broken down into credit-metered units. The following subsystems contribute to credit consumption:1. Language Model Inference
- Token-based billing per LLM (similar to OpenAI’s pricing model).
- Models have differing price-per-token multipliers:
| Model | Relative Credit Usage | Typical Use Case |
|---|---|---|
| GPT-4o Mini | 1× | Lightweight chatbots, simple instructions |
| GPT-4o | 16× | Strategic reasoning, agents with memory |
| Claude Haiku | 2× | Midweight contextual interactions |
| Claude Opus | 15–20× | Deep reasoning and multi-hop workflows |
💡 Heavier models yield higher accuracy, but also consume more tokens. Choose based on your use case.
2. Token Length: Input + Output
- Lyzr computes usage on the combined token length of:
- Prompt (agent logic, user input, KB context)
- Response (model output)
- This includes:
- RAG-pulled knowledge
- Agent history (if memory is enabled)
- Instruction chains and multi-agent dependencies
Example: A long SQL-generating prompt with schema context and 500-word response can consume up to 3–5× more credits than a standard chat exchange.
3. Platform Features & Services
| Feature | Credit Usage | Notes |
|---|---|---|
| Memory | Free | Stores interaction context for multi-turn dialogue. No credit cost. |
| Tools Execution | Fixed Cost | Each invocation of a 3P integration (e.g., Slack, Gmail) uses credits. |
| Knowledge Base (Classic) | Indirect | No feature cost, but large documents increase prompt size. |
| Semantic Model | Indirect + Inference | Embeds tabular metadata, uses credits when LLM is invoked. |
| Knowledge Graph | Indirect + Processing | Graph traversal + node summarization incurs token load. |
| Context Relevance Filtering | Moderate | Adds LLM-based ranking layer over retrieved chunks. |
| Groundedness Checks | Fixed per use | Post-response factuality validation using internal validators. |
| Responsible AI Modules | Variable | Cost depends on number of submodules invoked (e.g., hallucination, profanity). |
🔹 Credit Consumption Examples
| Scenario | Approximate Credit Cost |
|---|---|
| Simple agent query using GPT-4o Mini | 1–2 credits |
| Tool-based agent querying Gmail + Slack | 10–25 credits |
| RAG agent using 5 PDF files with GPT-4 | 20–60 credits |
| Text-to-SQL with Semantic Model (with schema + sample rows) | 15–50 credits |
| Agent using Claude Opus with Context Relevance + RAI | 80–120 credits |
These values are estimates and depend on prompt structure and content size.
🔹 Credit Management Best Practices
To avoid excessive credit burn, especially on larger workloads:- ✅ Use light models (GPT-4o Mini, Haiku) during development and testing.
- ✅ Limit file size and chunk count in KBs when setting up RAG.
- ✅ Disable unnecessary modules like RAI or Groundedness for internal use-cases.
- ✅ Use preview tools in Studio to inspect prompt-token size before triggering agent run.
🔹 Monitoring Credit Usage (Coming Soon)
Lyzr will soon support in-dashboard analytics to monitor:- Credit burn by agent
- Model-specific usage
- Peak usage windows
- Alerts for low-credit thresholds
🔹 Credit Expiry and Overflow
| Plan Type | Expiry Rules | Overflow Handling |
|---|---|---|
| Free Plan | Resets every 30 days | No rollover |
| Monthly Plan | Resets monthly | No rollover unless top-up applied |
| Annual Plan | Renewed annually | Quota spreads evenly or loaded in full |
| One-Time Top-Up | Never expires | Used after subscription credits are exhausted |
Summary
| Component | Role |
|---|---|
| Credits | Unit of Lyzr compute usage |
| Tracked Dimensions | Model, token count, tools, features |
| Replenishment | Monthly/annual plans or one-time top-ups |
| Monitoring | In progress (dashboard reporting soon) |
| Optimization | Choose light models, minimize token overhead, selective tools |
With this understanding, developers and builders on Lyzr can architect intelligent agents efficiently and affordably, optimizing both performance and compute cost.