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Description
An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.
Description
Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts.
Key features of Langtail allow teams to:
• Perform in-depth testing of LLM models to identify and resolve issues before production deployment.
• Easily deploy prompts as API endpoints for smooth integration into workflows.
• Track model performance in real-time to maintain consistent results in production environments.
• Implement advanced AI firewall functionality to control and protect AI interactions.
Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.
API Access
Has API
API Access
Has API
Integrations
Apify
Azure OpenAI Service
Claude Haiku 3
Claude Opus 3
Claude Sonnet 3.5
Claude Sonnet 3.7
Gemini
Gemini 1.5 Pro
Gemini 2.0
Gemini Advanced
Integrations
Apify
Azure OpenAI Service
Claude Haiku 3
Claude Opus 3
Claude Sonnet 3.5
Claude Sonnet 3.7
Gemini
Gemini 1.5 Pro
Gemini 2.0
Gemini Advanced
Pricing Details
$500 per month
Free Trial
Free Version
Pricing Details
$99/month/unlimited users
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Evidently AI
Founded
2020
Country
United States
Website
www.evidentlyai.com
Vendor Details
Company Name
Langtail
Founded
2023
Country
Czech Republic
Website
langtail.com
Product Features
Data Quality
Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization