OpenAI-compatible API
Use the same request and response shapes as the OpenAI API, but point your client at https://inference.alvoff.ai/v1 and authenticate with your Alvoff key.
Alvoff Inference
Alvoff Inference is an OpenAI-compatible API for speech-to-text, text-to-speech, embeddings, and chat/code generation. It is built for developers who want to swap in a different base URL, use familiar SDKs, and pay per request.
What Alvoff Inference is
Alvoff Inference is an API service for speech-to-text, text-to-speech, embeddings, and chat/code generation. It presents those models through OpenAI-compatible endpoints under `/v1`, so teams can keep familiar SDKs and change only the base URL and API key.
The platform currently serves a small catalogue of live models, including Whisper large v3 turbo for transcription, Kokoro 82M for speech synthesis, qwen3-embedding-0.6b for embeddings, and qwen2.5-coder:1.5b for code-focused chat completions. The service is positioned as pay-as-you-go infrastructure with metered billing and free credits for new accounts.
Core capabilities
One API for multiple model types
Serve audio transcription, speech synthesis, embeddings, and chat/code generation from a single API surface instead of wiring separate services together.
Live model catalogue
The catalogue lists live models for multilingual transcription, natural-sounding speech, dense embeddings, and code-optimized text generation.
Per-request metering
Pricing is based on the unit that matches the request type, such as per hour of audio, per 1M characters, or per 1M tokens, with no subscription or minimum spend stated.
Flexible audio workflows
The docs explain that audio files can be uploaded in common formats including WAV, MP3, M4A, FLAC, WebM, and OGG, and that compressed audio can reduce upload and end-to-end latency.
Language-aware transcription
The speech-to-text docs include ISO-639-1 language guidance for non-English audio and mention support for `/v1/audio/translations` for speech-to-English translation.
Typical use cases
Add speech-to-text with minimal code changes
Integrate transcription into a product that already uses OpenAI SDKs. Alvoff lets you switch the base URL and key rather than rewriting your client code.
Build text-to-speech features
Generate voice responses or narrated content from text using the Kokoro model and a simple `/v1/audio/speech` request.
Power search and RAG pipelines
Create dense vectors for semantic search, retrieval-augmented generation, or other embedding-based workflows with the qwen embedding model.
Generate and complete code
Use the code-optimized chat model for fast code generation and completion inside developer tools or internal assistants.
Prototype before scaling
Run small experiments or prototypes with free credits before committing to production usage and ongoing per-request billing.
Pros and Cons
Pros
- OpenAI-compatible shape makes it easier to integrate with existing clients.
- The documented workflow is simple: sign up, create a key, and call the API.
- Pricing is transparent and metered per request rather than packaged into subscriptions.
- The docs include practical guidance for audio formats, language codes, and error handling.
- New accounts receive free credits to test the service before paying.
Cons
- The public catalogue is limited to a small set of models, so it may not cover every workload.
- Some details such as team plans or enterprise terms are not shown in the provided sources.
FAQ
How do I get started with Alvoff Inference?
You create an account, generate an API key, and send requests to the base URL https://inference.alvoff.ai/v1. The docs show that any OpenAI SDK can work by overriding the base URL and using your Alvoff key as a bearer token.
Which model types does it support?
The documented models are speech-to-text, text-to-speech, embeddings, and chat/code generation. The current catalogue includes Whisper large v3 turbo, Kokoro 82M, qwen3-embedding-0.6b, and qwen2.5-coder:1.5b.
Can I use an OpenAI SDK with it?
Yes. The site says the API is OpenAI-compatible, the endpoints live under /v1, and the examples use the OpenAI client with a custom base_url.
How is usage billed?
Pricing is per request and metered by unit: audio by the second or hour, text by characters or tokens, and embeddings by tokens. New accounts start with free credits, and billing is handled in INR while prices are also shown in USD at the live rate.
What happens when I run out of credits or hit a limit?
The documentation says the API supports standard HTTP status codes and OpenAI-shaped error bodies. It also notes that requests return 402 when credits run out and 429 when rate limited.
Quick Facts
- Category
- Developer Tool
- API style
- OpenAI-compatible
- Primary workloads
- Speech-to-text, text-to-speech, embeddings, chat/code
- Base URL
- https://inference.alvoff.ai/v1
- Billing
- Per request; billed in INR with USD shown at the live rate
- Free credits
- New accounts start with free credits
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