Alai 2.0 vs OpenAI Evals: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Alai 2.0 and OpenAI Evals — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Alai 2.0
Alai
AI design partner that creates on-brand presentations, social posts, and infographics from a prompt, exportable to PDF and PPT.
Key features
- AI Slide Generation: Create presentation slides from a single text prompt
- On-Brand Design: Keep colors, themes, and styling consistent across an entire deck
- Multi-Format Output: Produce presentations, social posts, and infographics in one tool
- Export to PDF and PPT: Download finished presentations as PDF or PowerPoint files
- Themes and Elements Library: Access design themes and visual elements for slides
- Enterprise Support: Dedicated support for teams building decks at enterprise scale
Best for
- A founder generates a polished pitch deck from a prompt without hiring a designer
- A marketer creates on-brand social posts and infographics that match company styling
- An early-stage team keeps visual consistency across a deck during conceptualization
- A consultant exports AI-generated slides to PPT to finish edits in PowerPoint
- An enterprise team produces presentations at scale with dedicated support
OpenAI Evals
OpenAI
Open-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
Key features
- Registry of Benchmarks: A curated, open registry of existing evals and benchmarks for common LLM tasks, enabling quick comparison across models and tasks.
- Custom & Private Evals: Author and run custom evals using your own datasets and grading logic; private evals let teams evaluate proprietary workflows without exposing data publicly.
- Grader Framework: Build rubric-driven automated graders, model-based graders, or human-in-the-loop grading pipelines to produce consistent, repeatable scoring.
- CLI/SDK & API Integration: Python-first SDK and CLI that integrate with the OpenAI API, support threaded execution, detailed logs, and programmatic control for batch runs.
- Continuous Evaluation (CE): Integrate evals into development workflows to run on changes, detect regressions, and track performance over time across model versions.
- Detailed Reporting & Metrics: Produces sample-level logs, aggregated counts and metrics, and final reports that summarize correctness, rubric scores, and other custom metrics.
- Extensibility & Reproducibility: Templates and examples in the repository make it straightforward to extend eval types (e.g., classification, generation, instruction following) and reproduce results.