TextSynth is a hosted API for text generation, chat, embeddings, speech-to-text, text-to-speech, and image generation. It runs optimized inference for open models on standard GPUs and CPUs, and includes a free tier for light usage. Use simple REST endpoints in production or self-host with TextSynth Server, which supports many Transformer families plus Stable Diffusion. Credit-based pricing and straightforward parameters make scaling predictable as workloads grow.
Call chat or completion endpoints to draft copy, summarize text, answer questions, or run tool-assisted flows behind your product UI. Control temperature, top-p, and max tokens, stream partial outputs for responsiveness, and log prompts for evaluation. The playground accelerates iteration, and the same parameters port cleanly into clients and backends, reducing surprises between development, staging, and production deployments.
Access families like Llama, Mistral, Falcon, MPT, T5, RWKV, and GPT-2 variants; add Whisper for transcription and Stable Diffusion for images. Swap models to balance cost, latency, and quality without rewriting code, adopt new arrivals as they land, and mix capabilities per step in a pipeline so summarization, extraction, drafting, and vision can cooperate with clear boundaries that simplify debugging when behavior shifts.
Generate vector embeddings for retrieval-augmented generation, semantic search, clustering, or recommendations and store them in your preferred database. Combine embeddings with chat to ground answers, throttle context windows with approximate nearest neighbor search, and measure improvements with offline tests. Clear request and response formats make it straightforward to monitor drift and adjust chunking as content libraries expand.
Run the server as a single binary on Linux or Windows with minimal dependencies. Expose a REST API for completions, chat, embeddings, translation, TTS, and STT; serve multiple models concurrently; and deploy behind your own auth and observability. This lets teams keep data in-region, align with procurement and privacy requirements, and tune hardware for predictable costs under steady or spiky traffic.
Start with a free plan for light use, then scale via pay-per-use credits when you remove rate limits. Keep costs predictable while validating workloads and moving to production. Straightforward operations and transparent parameters reduce surprises for finance and engineering, creating space to experiment with model mixes before hardening SLAs and rolling out customer-facing features.
Developers and platform teams needing a practical API over open models; startups piloting multi-modal features; product groups that may begin hosted and later self-host for privacy or control; researchers building retrieval pipelines; and companies standardizing on predictable, portable endpoints that keep options open across vendors and hardware, without committing to one proprietary stack.
Standing up multi-model inference from scratch is complex and expensive. TextSynth centralizes endpoints for chat, generation, embeddings, audio, and images, and offers a compatible self-hosted server so teams can prototype, ship, and control costs without building their own serving stack. This preserves flexibility as models evolve, keeps data governance clear, and shortens time from idea to production.
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