Ashr vs Maxim AI: Observability, Prompts & Evals Comparison

Ashr is a test and evaluation platform purpose-built for AI agents. Part of YC W2026, it was founded by Shreyas Kaps (Fortune 100 AI agent experience) and Rohan Kulkarni (CTO, ex-Berkeley AI startup exit). Since agents cannot be unit tested like traditional APIs — inputs are unstructured, outputs are probabilistic, and failure modes are creative — Ashr generates synthetic but authentic user stories that flow through your product.

The platform works across voice, text, image, file generation, and multimodal interactions, catching errors that would take hours of manual testing. It includes prompt versioning with inline diffs and pass-rate tracking per version, full test timelines showing every speaker turn, tool call, and response, plus side-by-side comparison of expected vs. actual results.

Teams integrate via SDK and can run evaluations both pre-production and post-production. Users at UC Berkeley and Stanford are already on the platform. Ashr fills the critical gap of systematic, repeatable testing for probabilistic AI systems.

Maxim AI is an end-to-end LLM evaluation and observability platform designed for engineering teams building production AI agents and copilots. The platform's pitch is that quality, observability, and evaluation should live in one tool rather than being split across three vendors. Maxim provides distributed tracing across LLM applications, both automated and human evaluators, prompt playground and versioning, and human-in-the-loop review workflows. Deployment options span managed cloud and self-hosted, making it accessible to teams with various compliance requirements. Maxim competes with Langfuse and Phoenix in the open observability space, with Galileo and Confident AI in the enterprise eval space, and increasingly with full-platform offerings from larger vendors. The end-to-end positioning resonates with smaller teams that prefer fewer tools to integrate.