LLMStack enables developers and teams to turn language model projects into production-grade applications in minutes. It offers composable workflows for chaining prompts, vector store integrations for semantic search, and connectors to external APIs for data enrichment. Built-in job scheduling, real-time logging, metrics dashboards, and automated scaling ensure reliability and observability. Users can deploy AI apps via a one-click interface or API, while enforcing access controls, monitoring performance, and managing versions—all without handling servers or DevOps.
LLMStack Core Features
Composable prompt workflows
Vector store integrations
API and data connector library
Job scheduling and automation
Real-time logging and metrics
Automated scaling and deployment
Access controls and versioning
LLMStack Pro & Cons
The Cons
The Pros
Supports all major language model providers.
Allows integration of various data sources to enhance AI applications.
Open source with community and documentation support.
Facilitates collaborative app building with role-based access control.
LLMStack Pricing
Has free plan
YES
Free trial details
Pricing model
Freemium
Is credit card required
No
Has lifetime plan
No
Billing frequency
Monthly
Details of Pricing Plan
Free
0 USD
10 Apps
1 Private App
1M Character Storage
1000 Credits (one time)
Community Support
Pro
99.99 USD
100 Apps
10 Private Apps
100M Character Storage
13,000 Credits
Basic Support
Enterprise
Unlimited Apps
Unlimited Private Apps
Usage Character Storage
Unlimited Requests
Dedicated Support
White-glove service
Discount:Save 17% when subscribing yearly ($999/year plan)
GradientJ is an AI-driven platform designed to help non-technical teams automate intricate back-office procedures. It leverages large language models to handle tasks otherwise outsourced to offshore workers. This automation facilitates significant time and cost savings, enhancing overall efficiency. Users can build and deploy robust language model applications, monitor their performance in real-time, and improve model output through continuous feedback.