Overview

Scalar Field uses a unified credit system. Your subscription includes a monthly credit allowance. Credits are consumed by chat queries, agent execution time, agent LLM calls, data services, and other platform activity. Your remaining credit balance is visible in the sidebar and updates in real time.

Subscription Tiers

Plan	Monthly Price	Credits / Month	Key Includes
Free	$0	10	Basic data access, limited queries
Pro	$80 –$ 175	440 – 970	200K context window, strategy agents, custom news agents
Ultra	$200 –$ 1,000	1,100 – 5,500	1M context window, higher agent limits, expanded data access
Enterprise	Custom	Custom	Unlimited queries, SSO, dedicated support, read-only brokerage connections (Interactive Brokers, Fidelity — see Supported Brokerages)

Annual billing saves approximately 17% (pay for 10 months, get 12). Within Pro and Ultra, you choose a credit tier that fits your expected usage. Higher tiers provide more credits per month at a lower effective cost per credit.

Pro Plans

Credits / Month	Monthly	Annual (per month)
440	$80	~$67
550	$100	~$83
660	$120	~$100
825	$150	~$125
970	$175	~$146

Ultra Plans

Credits / Month	Monthly	Annual (per month)
1,100	$200	~$167
2,200	$400	~$333
3,300	$600	~$500
4,400	$800	~$667
5,500	$1,000	~$833

What Consumes Credits

Activity	Credit Cost	Details
Chat queries	Varies by length	Based on conversation context size (tokens used)
Agent execution	0.01 credits / second	Charged on total run duration of each execution
Agent LLM calls (`query_llm()`)	Varies by model	`tokens × 0.0001 × model_multiplier` (see table below)
Email & alert delivery	0.05 credits / email	Per notification sent by an agent
Data services (options analytics)	0.01 credits / query	Per options analytics request

Chat Query Credits

Each chat query consumes credits based on the size of the conversation context. Longer conversations with more messages use more tokens, which translates to more credits. The cost is computed as:

credits = tokens_used / tokens_per_credit

where tokens_per_credit is set by your plan.

Agent Execution Credits

Every time a strategy agent runs, it is charged based on total execution duration:

credits = total_duration_seconds × 0.01

For example, a 60-second agent execution costs 0.60 credits. A 5-minute execution costs 3.00 credits.

Agent LLM Calls (`query_llm()`)

Python function: from scalarlib import query_llm, get_supported_models

Strategy agents can make LLM calls to reason over data or search the web. This is done via the query_llm() function in scalarlib. Credit cost depends on the number of tokens processed and the model selected:

credits = tokens × 0.0001 × model_multiplier

Supported Models

Model (short alias)	Full Provider ID	Multiplier	Relative Cost
`gemini-2.5-flash`	`google/gemini-2.5-flash`	1×	Lowest (default)
`gpt-5.4-mini`	`openai/gpt-5.4-mini`	2×
`grok-4.20`	`x-ai/grok-4.20`	3×
`gemini-2.5-pro`	`google/gemini-2.5-pro`	4×
`gpt-5.4`	`openai/gpt-5.4`	6×
`claude-sonnet-4.6`	`anthropic/claude-sonnet-4.6`	6×	Highest

You can pass either the short alias or the full provider ID as the model parameter:

from scalarlib import query_llm

# Default model (Gemini 2.5 Flash, 1x cost)
result = query_llm(
    prompt="Which of these stocks are in the tech sector?",
    response_schema={"tech_stocks": ["AAPL"]},
    context=stock_list,
)

# Specify a model by short alias
result = query_llm(
    prompt="Latest earnings surprises this quarter",
    response_schema={"surprises": [{"ticker": "AAPL", "direction": "beat"}]},
    web_search=True,
    model="gpt-5.4",  # 6x multiplier
)

Use get_supported_models() at runtime to see all available models and their provider IDs:

from scalarlib import get_supported_models
print(get_supported_models())
# {'gemini-2.5-flash': 'google/gemini-2.5-flash', 'gpt-5.4': 'openai/gpt-5.4', ...}

Credit Lifecycle

Monthly reset: Credits reset at the start of each billing period. Unused credits do not roll over.
Mid-cycle upgrades: If you upgrade your plan mid-cycle, your existing usage carries over to the new plan. You receive the higher credit limit immediately, but already-consumed credits are not refunded.
Mid-cycle downgrades: Scheduled for the next billing cycle. Your current plan remains active until renewal.
Exhaustion: When credits run out, chat and agent features are paused. You will see a banner prompting you to upgrade or wait for the next billing cycle. Trading strategies with active positions are not liquidated, but scheduled executions will not run.

Monitoring Usage

Sidebar: Your credit balance and remaining credits are shown in the left sidebar, updated in real time via live streaming.
Low balance warning: A warning banner appears in the chat input when credits are running low.
Agent activation: If you have no credits remaining, you cannot activate new agents. Existing agents will pause execution until credits are available.
Execution results: Each agent execution shows its total_duration_seconds in the execution results panel, so you can estimate the credit cost of each run.

Getting Started

Trading

Trading Venues

Market Data

Usage & Pricing

Pricing & Credits

Overview

Subscription Tiers

Pro Plans

Ultra Plans

What Consumes Credits

Chat Query Credits

Agent Execution Credits

Agent LLM Calls (`query_llm()`)

Supported Models

Credit Lifecycle

Monitoring Usage

​Overview

​Subscription Tiers

​Pro Plans

​Ultra Plans

​What Consumes Credits

​Chat Query Credits

​Agent Execution Credits

​Agent LLM Calls (query_llm())

​Supported Models

​Credit Lifecycle

​Monitoring Usage

Overview

Subscription Tiers

Pro Plans

Ultra Plans

What Consumes Credits

Chat Query Credits

Agent Execution Credits

Agent LLM Calls (`query_llm()`)

Supported Models

Credit Lifecycle

Monitoring Usage