What people build on NeuronGate

Each guide covers the problem, how the requests flow, which models fit which role, example code and cost control.

• Coding agentsAgents that read code, call tools and edit files, with a strong model planning and a cheaper one doing the legwork.
• Customer supportStreaming chat assistants that answer from your help content and hand off to a person when they should.
• RAG and searchAnswer questions from your own documents: your retrieval layer finds the passages, a model writes the cited answer.
• Data extractionTurn invoices, emails, PDFs and screenshots into validated JSON you can load straight into a database.
• Content generationDraft product copy, summaries and translations in bulk, with a cheap model drafting and a stronger one editing.
• Model evaluationRun the same prompts against several models with one key and compare quality, latency and cost side by side.

The workloads differ, but they run on the same three pieces. Set them up once and every new workload is a new key, not a new vendor.

Prompts and completions aren't stored. We keep the usage metadata above for your dashboard; the content of requests and responses passes through.

Whatever the workload, the change to an existing OpenAI-style client is the same: point the base URL at NeuronGate, use a NeuronGate key, and pick a model id from the catalog.

If one host of a model is unavailable, the request is retried on another host of the same model. NeuronGate never swaps in a different model behind your back; when you want a fallback model, you choose it in your code, as the guides show.