Best Self Hosted Alternatives to Murf AI

LocalAI is a self-hostable AI inference server that provides a drop-in, OpenAI-compatible REST API for running models locally or on-premises. It supports multiple model families and backends, enabling text, image, and audio workloads on consumer hardware, with optional GPU acceleration.

Key Features

• OpenAI-compatible REST API for integrating with existing apps and SDKs
• Multi-backend local inference, including GGUF via llama.cpp and Transformers-based models
• Image generation support (Diffusers/Stable Diffusion-class workflows)
• Audio capabilities such as speech generation (TTS) and voice-related features
• Web UI for basic testing and model management
• Model management via gallery and configuration files, with automatic backend selection
• Optional distributed and peer-to-peer inference capabilities

Use Cases

• Replace cloud LLM APIs for private chat and internal tooling
• Run local multimodal prototypes (text, image, audio) behind a unified API
• Provide an on-prem inference endpoint for products needing OpenAI API compatibility

Limitations and Considerations

• Capabilities and quality depend heavily on the selected model and backend
• Some advanced features may require GPU-specific images or platform-specific setup

LocalAI is a practical foundation for building a local-first AI stack, especially when OpenAI API compatibility is a requirement. It offers flexible deployment options and broad model support to cover common generative AI workloads.

Willow is an open-source, privacy-focused voice assistant platform designed for low-cost ESP32-S3 hardware. It provides fast on-device wake-word and command recognition and can optionally integrate with a self-hosted inference server for high-quality speech-to-text, TTS, and LLM tasks. (heywillow.io)

Key Features

• On-device wake-word engine and voice-activity detection with configurable wake words and up to hundreds of on-device commands. (heywillow.io)
• Integration with Home Assistant, openHAB and generic REST endpoints for home automation and custom workflows. (heywillow.io)
• Willow Inference Server (WIS) option: a performance-optimized server that supports ASR/STT (Whisper models), TTS, and optional LLM inference with REST, WebRTC and WebSocket transports. WIS targets CUDA GPUs for low-latency workloads and includes deployment scripts and Docker compose support. (github.com)
• Device management and OTA flashing via the Willow Application Server (WAS) with a provided Docker image to simplify onboarding. (heywillow.io)

Use Cases

• Privacy-first smart-home voice control: local wake-word and command recognition that triggers Home Assistant automations without cloud transcription.
• On-premises speech processing: self-hosted WIS for low-latency ASR/STT and TTS for accessibility, transcription, or edge assistant applications.
• Developer integrations: embed Willow devices into custom REST/WebRTC workflows or use WIS to add LLM-powered assistants to local networks. (github.com)

Limitations and Considerations

• Advanced WIS features (LLM, high-quality TTS) expect CUDA-capable GPUs and NVIDIA drivers; CPU-only setups are supported but significantly slower and may disable some features. (github.com)
• Primary device target is the ESP32-S3-BOX family; other hardware may require additional porting or tuning. (heywillow.io)

Willow combines a small-footprint device runtime with an optional, high-performance inference server to enable private, low-latency voice assistants and on-premises speech workflows. It is actively developed with documentation, Docker deployment options, and community discussion channels for support. (heywillow.io)

Speaches is an OpenAI API-compatible server for speech-to-text, translation, and text-to-speech, designed to be a local “model server” for voice workflows. It supports streaming and realtime interactions so applications can transcribe or generate audio with minimal integration changes.

Key Features

• OpenAI API compatibility for integrating with existing OpenAI SDKs and tools
• Streaming transcription via Server-Sent Events (SSE) for incremental results
• Speech-to-text powered by faster-whisper, with support for transcription and translation
• Text-to-speech using Piper and Kokoro models
• Realtime API support for low-latency voice interactions
• Dynamic model loading and offloading based on request parameters and inactivity
• CPU and GPU execution support
• Deployable with Docker and Docker Compose and designed to be highly configurable

Use Cases

• Replace hosted speech APIs with a self-managed, OpenAI-compatible voice backend
• Build realtime voice assistants that need streaming STT and fast TTS responses
• Batch transcription/translation pipelines for recordings with optional sentiment analysis

Speaches is a practical choice when you want OpenAI-style endpoints for voice features while retaining control over models and infrastructure. It fits well into existing OpenAI-oriented application stacks while focusing specifically on TTS/STT workloads.