Alyph launches manual LLM context tool with 300+ model access

Alyph enters a crowded space of AI interfaces and prompt management tools by focusing on a specific friction point: the accumulation of noise in iterative AI work. The core insight—that a conversation thread carrying failed debugging steps, detours, and errors poisons the model's next attempt—is grounded in how LLM context windows work. By letting users manually prune irrelevant messages, Alyph shifts control away from automatic context management and toward deliberate curation, treating the conversation history as an editable artifact rather than an immutable log.

The tool's emphasis on simultaneous multi-model comparison (sending the same question to ChatGPT, Claude, Gemini, and 300+ others side by side) addresses a practical need for teams evaluating different providers without committing to a single vendor. This is especially relevant as the LLM landscape fragments across multiple capable models with different strengths, pricing, and trade-offs. The shared canvas feature—allowing clients, co-founders, and assistants to write and edit in a single space—positions it as a collaboration layer, not just a personal AI notebook.

Pricing is deliberately simple: pay for inference at provider rates plus margin, with no subscription friction and hard limits the user controls. This model aligns with the stated use case (someone spending $2,000 a month on AI funneling it all through Alyph), and the commitment to not training public models on user data distinguishes it from first-party AI vendors. The platform's explicit refusal to "do your work for you"—no autonomous agents, no auto-pruning—positions it as a tool for intent-driven users who want to stay in control of their outputs.