OpenMark AI vs
Vellum
Two tools for two different stages of the AI lifecycle. OpenMark AI helps you decide which model to use. Vellum helps you iterate on prompts and monitor production. Both are valuable, but they solve different problems.
Different Stages, Different Tools
Every AI-powered product moves through the same lifecycle. The tools you need change at each stage.
Vellum lives in the Build and Monitor stages. OpenMark AI lives in the Decide stage. Both are valuable, but at different points in your workflow. If you skip the Decide stage, you risk building an entire pipeline around the wrong model.
What Vellum Does Well
Vellum is a developer-focused platform built for teams that have already chosen a model and need to iterate on their prompts, manage test cases, and integrate evaluation into their development workflow.
- Test case management for prompt regression testing
- Prompt iteration and version control
- Evaluation reports with LLM-as-judge evaluators
- CI/CD integration for automated evaluation pipelines
- Custom Python evaluators for complex scoring logic
- Multi-step workflow evaluation
- Production monitoring and observability
Vellum is a strong choice for engineering teams already in production who need regression testing, prompt management, and continuous evaluation as part of their deployment pipeline.
What OpenMark AI Does Differently
OpenMark AI is a pre-deployment model selection tool. Instead of iterating on prompts for a model you have already chosen, OpenMark AI helps you figure out which model to choose in the first place.
- Define a task in the browser, no code required
- Benchmark 100+ models with real API calls
- Deterministic scoring (exact match, numeric, JSON schema, and more)
- No API keys needed, all calls handled via credits
- Cost per task and latency data for every model
- Stability tracking across multiple runs
- The decision layer before you commit to a model
OpenMark AI is for the question that comes first: "Which model should I use?" Once you have that answer, you can move into prompt engineering and production tooling with confidence. Try it free.
Feature Comparison
A side-by-side look at where each tool fits.
| Feature | Vellum | OpenMark AI |
|---|---|---|
| Setup required | SDK / code integration | Browser only |
| API keys | Required (your own keys) | Not needed (credits-based) |
| Model count | Multiple providers (limited) | 100+ models |
| Primary use case | Prompt iteration & regression | Model selection & comparison |
| Scoring | LLM-as-judge + custom Python | Deterministic (18 modes) |
| Stability tracking | Via test case reruns | Built-in across runs |
| Cost tracking | Via provider dashboards | Per-task cost per model |
| Target user | Developers in production | Anyone choosing a model |
LLM-as-Judge vs Deterministic Scoring
One of the core differences between the two tools is how they score model outputs.
Vellum: LLM-as-Judge
Vellum supports LLM-as-judge evaluators where another model grades the output. This is flexible and can handle subjective or open-ended tasks. It also supports custom Python evaluators for precise logic. The trade-off is that LLM judges introduce their own variance: the evaluator can disagree with itself across runs.
OpenMark AI: Deterministic
OpenMark AI uses deterministic scoring: exact match, numeric tolerance, JSON schema validation, regex, and more. The same output always gets the same score. No evaluator variance, no LLM grading costs, fully reproducible results across every run.
Neither approach is universally "better." LLM-as-judge handles nuance. Deterministic scoring handles reproducibility. For pre-deployment model selection, reproducibility matters more because you need to trust the comparison. Learn more about scoring approaches.
Frequently Asked Questions
What is the difference between OpenMark AI and Vellum?
Vellum is a developer platform for prompt iteration, test case management, CI/CD integration, and production monitoring. OpenMark AI is a pre-deployment model selection tool where you benchmark 100+ models on your task before writing any code.
Do I need both OpenMark AI and Vellum?
They serve different stages. Use OpenMark AI to decide which model to use. Use Vellum after you've picked a model and need regression testing, prompt management, and CI/CD integration.
Does Vellum require API keys?
Yes, Vellum requires your own API keys for the models you want to evaluate. OpenMark AI handles all API calls via credits, no keys needed.
Can I evaluate 100+ models in Vellum?
Vellum supports multiple providers but is designed for iterating on a few models with your prompt pipeline. OpenMark AI is designed for broad comparison across 100+ models in a single benchmark run.
Why Teams Use OpenMark AI
Choose before you build. Not monitoring, not observability. The decision layer that comes before your production stack.
No SDK, no CLI, no notebook. Describe your task in the browser and run. Works for developers, PMs, and founders.
No provider accounts required. OpenMark AI handles every API call via credits. Just describe your task and run.
Compare models from every major provider in a single benchmark run. Not a handful of options. Over 100.
Choose Your Model Before You Build
Benchmark 100+ models on your task with deterministic scoring, real costs, and stability data.
50 free credits. No API keys, no setup.