ChatGPT Plugin

AtlasBurn

Track and control AI spend

Brand: AtlasBurn

Category: Developer Tools
Primary Subcategory: Cloud Cost Management (FinOps)

Integration details

Description

Connect AtlasBurn to see where your AI spend is going across models, providers, workflows, and agents. Ask ChatGPT to break down usage and costs, identify expensive requests or unusual spending patterns, investigate retries and costly agent behavior, and review forecasts and budget guardrails. AtlasBurn helps teams understand what is driving their AI costs and spot problems before they grow into larger bills. The connection is read-only and content-blind: it reads cost and usage telemetry from your AtlasBurn workspace, never changes your settings, and never sees your prompts or model outputs.

Integration type: Plugin
Verification status: Not applicable
Platform: ChatGPT
Category: Developer Tools
Primary Subcategory: Cloud Cost Management (FinOps)
Secondary Subcategories: None listed
Brand: AtlasBurn
Access: Account required
First tracked: 2026-10-28
Tool count: 7
Market: US

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Primary Subcategory

Competing in ChatGPT Cloud Cost Management (FinOps)

View Category

Listed in alphabetical order

Primary Subcategory

AtlasBurnComing soonGet notified

MavvrikComing soonGet notified

VantageComing soonGet notified

Tools

7 tools agents can invoke

AI spend attribution for this organization, broken down by model and by product feature, over a window (default 30 days). Shows where the cost is actually going.
get_cost_breakdown
Total AI spend and token usage for this AtlasBurn organization over a window (default 30 days): cost, prompt/completion tokens, request count, and requests/second.
get_cost_summary
After the user has approved an integration request, returns a one-time setup code (single use, valid 10 minutes) plus the exact commands to run in the root of the user's project. The CLI exchanges the code for the ingest key and writes it to the project's env file itself; the key never appears here. Use the code only in those commands; don't repeat it to the user or save it anywhere else. status "pending" means the user hasn't approved yet. For method "proxy", after the command runs, update the app's AI client to use the printed proxy base URL and send the AtlasBurn-API-Key header from process.env.ATLASBURN_KEY (or the language's equivalent).
get_setup_code
Current safety-guardrail state for this organization: whether guardrails are enabled, the mode, the authoritative enforcement status (ACTIVE / THROTTLED / SUSPENDED), any individual features paused after a runaway loop, configured daily/hourly budgets, and the most recent breach events.
get_guardrail_status
Start connecting the user's project to AtlasBurn. Creates a one-time request for a NEW ingest key and returns an approval link. Nothing is created until the user opens the link, signs in again and approves; existing keys are never revoked. Give the user the approvalUrl, wait until they say they've approved, then call get_setup_code with the requestId. method: "sdk" for Node.js/TypeScript apps (the CLI installs and wires the SDK) or "proxy" for any language (AI calls are routed through proxy.atlasburn.com).
request_integration_key
Behavioral runtime signals for this organization over a window (default 30 days): request rate, retry rate, loop probability, context-expansion rate, token volume, and model mix. Use this to tell whether anything looks like a runaway/looping agent.
get_runtime_signals
Capital runway and burn economics for this organization: projected monthly bill, current daily burn, capital reserves, budget runway (days), MRR, burn volatility, and systemic-risk probabilities. Fields may be null until enough economic inputs/history exist.
get_runway_forecast

FAQs

AtlasBurn ChatGPT Plugin FAQ

How the directory, categories and Discoverability Score work.

Read the methodology

How do I improve AtlasBurn's ChatGPT Plugin discoverability?

The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.

What are AtlasBurn alternatives on ChatGPT?

As of 2026-10-28, AtlasBurn competes with Mavvrik, Vantage in ChatGPT Cloud Cost Management (FinOps), ranked by public Discoverability Score.

Where is this profile measured?

This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.