Backyard AI is a private AI infrastructure platform that enables businesses to host, deploy, and customize large language models (LLMs) within their own environment. It supports multiple open-source models such as Mistral, LLaMA, and Mixtral, and can be integrated via API or self-hosted for maximum data security.
With a focus on data privacy, performance, and flexibility, Backyard AI allows users to build internal agents, apps, and tools powered by AI while maintaining full control over the architecture. From prompt tuning to model evaluation, it offers a complete stack for managing and scaling AI workloads internally. The platform is ideal for enterprises looking to avoid vendor lock-in while leveraging cutting-edge AI safely and efficiently.
Backyard AI Review Summary
Performance Score
A+
Interface
Developer-friendly, API-first
AI Technology
- LLM Hosting
- Private Cloud Deployment
- Prompt Tools
- API Integrations
Purpose of Tool
Deploy, fine-tune, and run LLMs securely in private environments
Compatibility
Web-based, self-hosted, cloud-compatible
Pricing
Custom pricing based on deployment scale and features
Who is Best for Using Backyard AI?
- Enterprise AI Teams: Build secure, private infrastructure for large language models tailored to internal workflows and data sensitivity.
- DevOps and MLOps Engineers: Deploy, monitor, and manage scalable LLM environments with robust observability and control.
- Healthcare & Finance Orgs: Use Backyard AI to ensure compliance and privacy when handling sensitive data in AI pipelines.
- Startups Building AI Tools: Rapidly prototype and deploy AI agents or apps without relying on third-party cloud APIs.
Host private LLMs (e.g. LLaMA, Mistral)
API and SDK integration
Fine-tuning and prompt tools
Secure private cloud deployment
Agent & app-building support
Role-based access control
Usage and latency metrics
Model performance evaluation
Developer-first platform
Is It Free?
Backyard AI does not offer a free version. Pricing is fully custom and tailored to the scale, deployment preference, and enterprise needs. Businesses can request a demo or contact the team to configure a plan that includes:
- LLM hosting in private infrastructure
- Support for open-source models
- On-prem or cloud-native setup
- Advanced control and security options
- Dev support and onboarding
Backyard AI Pros & Cons
Deploy LLMs privately with full control over infrastructure
Support for multiple OSS models and fine-tuning workflows
Excellent for security-sensitive and compliance-driven environments
APIs and prompt tools make dev work seamless
No free tier or self-serve setup—demo request required
Not suitable for casual users or non-technical teams
Requires dedicated infra or cloud setup for deployment
