Synexa

Synexa is a serverless AI inference API offering 100+ hosted models for image, video, speech and 3D generation through one call, with scale-to-zero billing and A100 pricing it positions below Fal.ai and Replicate.

Synexa is a serverless inference platform that gives developers a single API for running hosted AI models. A request names a model, passes an input object, and returns a prediction, so there is no GPU provisioning, container building or server maintenance involved. The homepage shows the same call in HTTP, Node and Python, and the platform also lists SDKs for Python and JavaScript alongside a plain REST interface. The catalog covers more than 100 production-ready models, with new ones added weekly according to the site. Listed examples include FLUX Pro, Ideogram v2 and Hunyuan Video, and the gallery highlights Google's nano-banana-pro for image generation and editing, Tencent's hunyuan3d-2 for textured 3D assets, Google's veo3.1 for video with context-aware audio, and Tongyi's z-image-turbo, a fast 6B-parameter text-to-image model. Supported tasks include generating images, generating videos, restoring images, captioning images, generating speech and fine-tuning models. On the infrastructure side, Synexa describes automatic scaling that drops to zero when idle and grows under load, so billing follows actual usage. It cites A100 and H100 GPUs across three continents, smart routing aimed at sub-100ms latency, a 99.9% uptime guarantee, and an optimized inference engine claimed to run diffusion models up to 4x faster. The homepage also compares A100 hourly pricing against Fal.ai and Replicate, listing Synexa at $2.49 per hour and claiming savings of up to 62%. The product suits developers and product teams who want to add image, video, audio or 3D generation to an application without owning GPU infrastructure. It competes directly with other model-hosting APIs such as Replicate and Fal.ai, and its pitch rests on price, a broad model gallery and a minimal integration surface. Teams that need to host fully custom stacks or want deep control over the runtime may find a managed catalog approach more limiting, while those who mainly need quick access to popular open and partner models will find the workflow short.