Building Exeta: A High-Performance LLM Evaluation Platform

Why We Built This

LLMs are everywhere, but most teams still evaluate them with ad-hoc scripts, manual spot checks, or “ship and hope.” That’s risky when hallucinations, bias, or low-quality answers can impact users in production. Traditional software has tests, observability, and release gates; LLM systems need the same rigor.

Exeta is a production-ready, multi-tenant evaluation platform designed to give you fast, repeatable, and automated checks for your LLM-powered features.

What Exeta Does

1. Multi-Tenant SaaS Architecture

Built for teams and organizations from day one. Every evaluation is scoped to an organization with proper isolation, rate limiting, and usage tracking so you can safely run many projects in parallel.

2. Metrics That Matter

• Correctness: Exact match, semantic similarity, ROUGE-L
• Quality: LLM-as-a-judge, content quality, hybrid evaluation
• Safety: Hallucination/faithfulness checks, compliance-style rules
• Custom: Plug in your own metrics when the built-ins aren’t enough.

3. Performance and Production Readiness

• Designed for high-throughput, low-latency evaluation pipelines.
• Rate limiting, caching, monitoring, and multiple auth methods (API keys, JWT, OAuth2).
• Auto-generated OpenAPI docs so you can explore and integrate quickly.

Built for Developers

The core evaluation engine is written in Rust (Axum + MongoDB + Redis) for predictable performance and reliability. The dashboard is built with Next.js 14 + TypeScript for a familiar modern frontend experience. Auth supports JWT, API keys, and OAuth2, with Redis-backed rate limiting and caching for production workloads.

Why Rust for Exeta?

• Predictable performance under load: Evaluation traffic is bursty and I/O-heavy. Rust lets us push high throughput with low latency, without GC pauses or surprise slow paths.
• Safety without sacrificing speed: Rust’s type system and borrow checker catch whole classes of bugs (data races, use-after-free) at compile time, which matters when you’re running critical evaluations for multiple tenants.
• Operational efficiency: A single Rust service can handle serious traffic with modest resources. That keeps the hosted platform fast and cost-efficient, so we can focus on features instead of constantly scaling infrastructure.

In short, Rust gives us “C-like” performance with strong safety guarantees, which is exactly what we want for a production evaluation engine that other teams depend on.

Help Shape Exeta

The core idea right now is simple: we want real feedback from real teams using LLMs in production or close to it. Your input directly shapes what we build next.

We’re especially interested in:

• The evaluation metrics you actually care about.
• Gaps in existing tools or workflows that slow you down.
• How you’d like LLM evaluation to fit into your CI/CD and monitoring stack.

Your feedback drives our roadmap. Tell us what’s missing, what feels rough, and what would make this truly useful for your team.

Getting Started

Exeta is available as a hosted platform:

1. Visit the app: Go to exeta.space and sign in.
2. Create a project: Set up an organization and connect your LLM-backed use case.
3. Run evaluations: Configure datasets and metrics, then run evaluations directly in the hosted dashboard.

Conclusion

LLM evaluation shouldn’t be an afterthought. As AI moves deeper into core products, we need the same discipline we already apply to tests, monitoring, and reliability.

Try Exeta at exeta.space and tell us what works, what doesn’t, and what you’d build next if this were your platform.