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OpenGround is OpenLIT’s AI model comparison tool - an LLM playground built for a specific job: run one prompt across many providers and models at once, then compare the results side-by-side. Pick a prompt, pick the providers you want to test, click Evaluate providers, and see cost, speed, and quality laid out next to each other. Every run is saved automatically to a searchable history, so you can build up a record of what you’ve tested and reopen or reload any past run later.

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What you get

Run a Comparison

Configure a prompt, pick providers and models, tune settings, and evaluate

Results & History

Read the metrics, response-time waterfall, and cost breakdown, then find any past run again later

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Model and provider management

Model and provider management itself - adding providers and editing pricing - lives on Monitor → Costs → Manage models. OpenGround and Manage models share the same underlying providers and models, so anything you configure in one is immediately available in the other.

Manage models

Add custom models, edit pricing, and manage providers used across OpenGround, Chat, and Pricing

Manage LLM secrets

Centrally store LLM API keys that applications can retrieve remotely without restarts

Manage prompts

Version, deploy, and collaborate on prompts with centralized management and tracking

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Frequently asked questions

OpenGround runs the same prompt across multiple LLM providers and models at once, so you can compare cost, speed, and response quality side-by-side before choosing a model for production.
14: OpenAI, Anthropic, Google, Mistral, Groq, Perplexity, Azure, Cohere, Together AI, Fireworks, DeepSeek, xAI, Hugging Face, and Replicate. See Run a Comparison for how to configure one.
Yes. When configuring a provider in OpenGround, switch to “Use Custom Model” and type any model ID directly, with its own input/output pricing. There’s no field for a custom endpoint or base URL - only the model ID and its pricing are configurable.
Yes. Every run is saved to a searchable history with cost, speed, and token-efficiency summaries - there’s no separate save step. See Results & History for how to find and reopen past runs.