Malted AI is an enterprise Retrieval-Augmented Generation (RAG) platform designed to help organizations build, deploy, and manage private large language model (LLM) applications securely and at scale. It offers a robust, modular RAG stack that integrates with various enterprise data sources while maintaining high standards for security, reliability, and observability.
Malted AI is purpose-built for teams that need to deploy LLMs using their proprietary data — without sending sensitive information to third-party APIs or cloud models. The platform emphasizes private deployments, full observability, and infrastructure compatibility, making it ideal for technical teams, enterprises, and AI infrastructure developers.
Features
Enterprise RAG Stack
Malted AI provides a modular RAG architecture optimized for production workloads. It supports embedding, retrieval, generation, and evaluation pipelines with custom logic.Private LLM Deployment
Run your own LLMs in secure environments (e.g., on-premise, VPC, or private cloud) with full control over model usage and data residency.Observability and Debugging Tools
Includes built-in observability tools that allow teams to inspect queries, model outputs, retrievals, and latency, improving trust and reliability.Multi-Source Connectors
Seamlessly integrates with common enterprise data sources such as Notion, Confluence, Google Drive, S3, databases, and more.Evaluation Framework
Allows technical teams to set up automatic or manual evaluations of LLM output quality and system performance across datasets and use cases.LLM Agnostic
Supports various LLMs (open-source and commercial) so teams can experiment, benchmark, and switch models with ease.Advanced Access Control
Built with enterprise-grade access management, including API keys, audit logs, and role-based permissions.Versioning and Experiments
Track experiments, deployments, and model changes using structured version control tools designed for LLM pipelines.
How It Works
Malted AI is designed for developers and ML/infra teams who want to build production-ready LLM workflows. Here’s how it works:
Step 1: Connect Data Sources
Use Malted’s prebuilt connectors to ingest internal documents, wikis, files, and databases.
Step 2: Embed and Store
Documents are chunked, embedded using state-of-the-art embedding models, and stored for fast semantic retrieval.
Step 3: Query Handling
User queries are passed through custom pipelines, which perform retrieval, grounding, and LLM invocation.
Step 4: LLM Response Generation
Based on retrieved documents, the system generates responses using your choice of model (e.g., Mistral, Claude, GPT, or open-source LLMs).
Step 5: Monitor and Evaluate
Built-in monitoring dashboards allow real-time inspection and evaluation of the system’s outputs and performance.
Step 6: Deploy and Iterate
Once validated, systems can be deployed to production environments with continuous improvement based on feedback and analytics.
Use Cases
Internal Knowledge Assistants
Deploy private AI copilots for internal teams to query documentation, wikis, and databases without exposing sensitive data.Customer Support Automation
Enable support teams to build grounded response systems for handling tickets and FAQs using internal support materials.Legal and Compliance QA Tools
Create retrieval-augmented systems for legal research or compliance checks, using proprietary legal documents and policies.Developer Documentation Search
Empower engineering teams to ask technical questions about internal APIs, libraries, or deployment tools using embedded technical docs.Enterprise AI Experimentation
Use Malted’s modular architecture to run A/B tests, benchmarking, and continuous evaluations across different LLMs and datasets.AI Infrastructure Teams
Give MLOps and DevOps teams a configurable, observable RAG stack to manage private LLM deployments reliably.
Pricing
As of June 2025, Malted AI does not publish fixed pricing plans on its website. Instead, it offers custom enterprise pricing based on usage, deployment scale, and support requirements.
To get accurate pricing, potential users must contact Malted AI directly for a demo and personalized quote.
Request access here: https://www.malted.ai/contact
Expected pricing components may include:
Per-deployment license fees
API usage or data ingestion limits
Support tier selection (basic vs. enterprise)
Hosting options (self-hosted vs. cloud-hosted)
Strengths
Designed for production RAG use cases
Full control over model and infrastructure
Supports custom data sources and pipelines
Strong observability and debugging tools
Compatible with any LLM model
Secure, private deployments by design
Built-in evaluation and version tracking
Drawbacks
No public self-serve or trial option
Requires technical expertise to implement
Not aimed at non-technical or solo users
No pricing transparency available
Limited public documentation or case studies
Comparison with Other Tools
Malted AI competes with a growing ecosystem of RAG and LLM infrastructure tools, including:
LangChain – Offers flexible pipelines for building LLM apps, but lacks enterprise observability and native deployment tooling.
LlamaIndex – Focuses on retrieval and indexing but is more developer-focused than enterprise-ready.
Haystack by deepset – Strong in modularity and open-source usage, but often requires more engineering effort for deployment.
Vespa – Excellent for large-scale search and retrieval tasks, but not LLM-focused by default.
Malted AI stands out for being enterprise-ready out of the box, with production-grade observability, evaluation tools, and secure deployment infrastructure. It bridges the gap between flexible LLM pipelines and enterprise IT requirements.
Customer Reviews and Testimonials
As of now, Malted AI does not list public customer reviews or testimonials on its website, and it is not currently featured on review platforms such as G2 or Product Hunt.
However, the platform is targeted toward advanced enterprise users and is currently in closed or early-access mode. Interested companies are encouraged to schedule a demo to evaluate performance in real-world environments.
The product appears to be trusted by forward-looking AI and infrastructure teams building secure, internal-facing LLM tools.
Conclusion
Malted AI is a powerful, modular RAG platform built for enterprises looking to deploy large language model systems securely and at scale. With strong support for observability, custom pipelines, and private infrastructure, it gives teams the tools they need to build LLM apps on proprietary data — without compromising on privacy or control.
While it may not be the right fit for non-technical users or startups looking for plug-and-play AI, it delivers tremendous value to organizations that need scalable, secure, and highly customizable RAG capabilities.
For enterprise AI infrastructure teams, Malted AI offers a best-in-class solution for building reliable, production-grade LLM applications.
