Aryan Agarwal
Vellum and Gravity attract different buyers. Vellum's natural buyer is an ML platform engineer at a company shipping AI features. Gravity's natural buyer is a founder or ops lead shipping internal agents.
What Vellum is, and where it actually shines
Vellum is a developer platform for managing prompts, running evaluations, and orchestrating workflows that include LLM calls. It treats prompts as code, with versioning, branching, evaluation suites, and an SDK for production deployment.
Where it shines:
- Managing dozens to hundreds of prompts that ship as part of a product.
- Running evaluation sets to compare prompt versions or models before shipping.
- A/B testing prompt variants in production.
- Workflow orchestration where prompts are stitched together with deterministic logic.
- Multi-tenant infrastructure where prompt artifacts are first-class.
It is genuinely good at what it does. If your job is to ship a generative AI feature into an existing app and not regress quality on every change, Vellum is one of the better tools available.
What Gravity does differently
Gravity is for a different job. Operational agents. You write one sentence and the runtime composes the agent. There is no prompt artifact to manage, no eval suite to maintain, no branching strategy.
Example Gravity prompt:
"Every weekday at 10am IST, scan our HubSpot pipeline for deals stuck for more than 14 days. Draft a polite nudge email to the owner. Put drafts in a Linear ticket for me to review."
The runtime handles the orchestration. The change cadence is editing a sentence, not managing a prompt registry. Describe the outcome, not the workflow is the design ethos.
Side-by-side capability comparison
| Capability | Vellum | Gravity |
|---|---|---|
| Primary job | Ship AI features in a product | Ship internal action-taking agents |
| Buyer persona | ML or platform engineer | Founder, ops lead, marketer |
| Setup model | Prompt editor plus workflow canvas | One sentence |
| Prompt versioning | First-class | Implicit, every edit is a version |
| Evaluation suites | Deep, with golden datasets | Basic, more coming |
| Scheduling and triggers | External | First-class in prompt |
| Connectors | BYO via SDK | Native catalogue |
| Pricing model | Tiered SaaS | Bundled monthly fee |
The evals-vs-outcomes split
Vellum's worldview is that the LLM is a component inside a larger product and the question is how to keep it from breaking when you change something. The answer is rigorous evaluation. Test, measure, ship, roll back.
Gravity's worldview is that the agent is the product, and the question is whether it accomplished the outcome. The answer is observable runs. Did the agent do what the sentence said. If yes, ship. If not, edit the sentence.
Neither worldview is wrong. They serve different jobs. If you have an ML platform team and a customer-facing AI feature, you want Vellum-style rigour. If you have an ops team and internal agents that just need to work, you want Gravity-style simplicity. How we test AI agents covers our own approach in detail.
Pricing reality
- Vellum: Tiered SaaS, with usage-based pricing on top. Aimed at engineering teams.
- Gravity: Bundled monthly fee. Self-serve.
Vellum is typically purchased by an engineering org for an AI feature. Gravity is typically purchased by an individual or small team for operational agents. Different buyers, different procurement patterns.
When Vellum is the right choice
- You are shipping AI features inside an existing product.
- You need disciplined evals to avoid regressions.
- You manage many prompts as artifacts.
- Your engineering team wants prompt management as part of their stack.
- You want to A/B test prompts in production.
When Gravity is the right choice
- You are an operator, not a platform engineer.
- Your agents are internal and do work.
- You want zero infra.
- You change agents by editing a sentence.
- You want one runtime that schedules, triggers, escalates, and logs.
Migration: what changes if you switch
Migration between these tools is rare because the use cases differ. More common: teams add Gravity for the operational layer while keeping Vellum for the product-AI layer. If you do want to move an operational agent off Vellum:
- Identify the workflow you want to migrate.
- Write the outcome sentence.
- Connect OAuths in Gravity.
- Run a dry run.
- Cut over.
Frequently asked questions
Is Vellum an agent platform or a prompt platform?
Both. Vellum started as a prompt engineering, versioning, and evaluation suite, then added workflow and agent capabilities. The historical strength is in prompt management and evals, which it does very well.
Who uses Vellum?
Engineering teams at AI product companies who need to manage many prompts, run evaluation sets, and ship updates without regressions. The audience skews technical.
Does Gravity have prompt versioning?
Yes. Every change to an outcome prompt is a version. You can compare versions and roll back. It is simpler than Vellum's full prompt management surface because the unit of work is the whole agent, not individual prompts.
Can I run evals on a Gravity agent?
Yes, we ship a basic eval surface. For very structured eval workflows with golden datasets and multi-model comparisons, Vellum still has a deeper offering.
Which one is better for ops teams vs ML teams?
Ops teams should pick Gravity. ML teams shipping a customer-facing AI product probably want Vellum for the evaluation depth, and may pair it with a runtime.
Three takeaways before you close this tab
- Vellum is for the product-AI job. Gravity is for the operations-AI job.
- Evaluation rigour matters when you have customers. Outcome rigour matters when you have a process.
- They can coexist. Many real stacks use Vellum for the model layer and Gravity for the agent layer.
Sources
- Vellum. "Official documentation and product overview." docs.vellum.ai
- Vellum. "Pricing page." vellum.ai/pricing
- Vellum. "Evals and workflow documentation." vellum.ai