LoginLlama focuses on real-time risk scoring at the login step, with the core goal of detecting anomalous logins before the session is handed off, rather than discovering problems only afterward through logs or 2FA alerts.
Main capabilities:
- Real-time scoring: each login returns a risk score from 0–10, along with the specific risk code that triggered that score.
- Risk signals: including impossible travel (for example, Austin 9:00, Lagos 9:12), Tor and VPN exit nodes, new devices and new browser fingerprints, a city or country where the user appears for the first time, bots and automated traffic (headless browsers, scripted credential-stuffing traffic), and more.
- Low latency and fail-open: response time under 500 milliseconds, suitable for placement in the login path; when the service is unavailable, real users will not be locked out.
- Decision interface: applications can choose to allow, escalate to MFA, or block directly based on their own thresholds.
How to use it: After authentication checks pass, send the user, IP, and User-Agent to a single endpoint to obtain a risk score and trigger codes. The official offering provides three integration paths: one is to use a prompt to let a coding agent automatically detect the tech stack, install the SDK, and connect the risk check into the login flow; another is to manually integrate the SDK; it is also possible to connect to LoginLlama's MCP server and query login data in natural language within an agent, for example "What is the average login risk score for Chrome?" or "Show the highest-risk logins this week." The query is read-only and limited to the scope of the team it belongs to.
Applicable scenarios: Any application or framework that needs to protect the login step, especially suitable for development teams that want to quickly gain account takeover, credential-stuffing, and bot traffic detection capabilities without building their own machine learning team. The official claim is that it is suitable for any authentication system or framework, the API handles core detection, and developers retain final decision-making authority.
Limitations and notes: The official materials do not provide specific pricing details beyond the free quota, nor do they explain the prices, feature differences, or call volume limits of each paid tier. Information on detection accuracy, false positive rates, the list of supported languages or frameworks, data compliance, and regional support is not yet provided in the materials. The official estimate is that building similar capabilities in-house would require 6–12 months of engineering investment and continuous maintenance of threat lists, geographic data, and device fingerprints; LoginLlama leaves this maintenance work to the platform. The free quota is 1,000 checks per month, no credit card required; getting started does not require talking to a salesperson, the SDK is open source, and the API uses a fail-open design.
