What Braintrust, PromptHub, Vellum, and Promptfoo Each Do
📍 In One Sentence
Braintrust scores, PromptHub versions, Vellum A/B tests, Promptfoo regression-tests — four prompt tools that overlap but don't replace each other.
💬 In Plain Terms
Think of it like building software: you need a test framework (Promptfoo), a quality dashboard (Braintrust), a deployment pipeline (Vellum), and a code repository (PromptHub). Most teams need two of these, not all four.
Braintrust, PromptHub, Vellum, and Promptfoo solve different prompt team problems. Braintrust is an evaluation platform (score outputs). PromptHub is a version control system (organize and share prompts). Vellum is a deployment platform with A/B testing (run experiments on real traffic). Promptfoo is a test automation tool (catch regressions in CI/CD). They overlap but do not replace each other.
The reason teams struggle to pick one: all four claim to "optimize prompts," but they optimize at different stages. Braintrust optimizes by measuring; Vellum optimizes by splitting traffic; Promptfoo optimizes by catching regressions; PromptHub optimizes by organizing. A team might use Braintrust to discover a better prompt, Promptfoo to test it in CI/CD, and Vellum to deploy it.
This guide is a head-to-head comparison of four specific tools. For a broader ranking of all prompt engineering tools, see Best Prompt Engineering Tools 2026. For team optimization features including DSPy and Helicone, see Best Prompt Optimization Tools for Teams.
How We Compared These Tools
We evaluated the four tools against five criteria that matter in real team workflows: how well they support team collaboration, whether they include A/B testing or experimentation, scoring or evaluation capabilities, CI/CD integration, and pricing transparency.
| Criterion | What It Measures | Why It Matters |
|---|---|---|
| Team collaboration | Role-based access, branching, shared dashboards | Multiple engineers must edit prompts without overwriting each other |
| A/B testing | Side-by-side variant comparison, traffic splitting | Compare variants on the same input set or production traffic |
| Evaluation/scoring | Custom metrics, LLM-based scorers, quality gates | Measure output quality, not just look at outputs visually |
| CI/CD integration | CLI, API, GitHub Actions, automated testing | Catch regressions before deployment; automate quality checks |
| Pricing transparency | Public pricing page, clear per-unit costs | Budget predictability for 3–10 person teams |
Braintrust: Evaluation Depth at $249/Month (Pro)
Braintrust is an AI evaluation platform that logs every API call, scores outputs with custom metrics, and runs A/B experiments in a shared lab — best for teams that measure output quality systematically. Braintrust is not a prompt builder or version control system; it is a shared evaluation laboratory.
Free tier includes 1M trace spans and 10K scores with unlimited users — enough for most pre-production evaluation workflows. Pro plan is $249/month. Braintrust added Loop agent in 2026: an autonomous evaluator that generates test cases and iterates on prompts without manual setup. MCP server connects Claude Code and Cursor directly to Braintrust evaluation stack from your IDE. The logging proxy integrates with OpenAI, Anthropic, and Google APIs without code changes. You define custom scoring functions in TypeScript or Python. GitHub integration lets you version prompts alongside code. SOC 2 Type II certification now available. Tradeoff: Pro plan requires engineering expertise to design and maintain scoring functions; free tier is excellent for evaluation baselines.
Best team features: shared experiment dashboards (all members see eval results in real time), role-based access (admin/member/viewer), git-like commit history for prompts, and production logging (every API call logged with inputs, outputs, and scores).
For the metrics behind custom scoring, see Prompt Evaluation Metrics: Accuracy, Relevance, Latency.
- Shared experiment dashboards: all team members see eval results live
- Role-based access: admin/member/viewer roles
- Prompt versioning via git-like commit history
- Production logging: every API call logged with inputs/outputs/scores
- Loop agent: autonomous evaluator that generates test cases and iterates on prompts (new in 2026)
- MCP server: direct integration with Claude Code and Cursor for IDE-based evaluation
- SOC 2 Type II certified for enterprise deployments
📌 Did You Know
Braintrust's free tier includes 1M trace spans and 10K scores with unlimited users — more evaluation capacity than most teams use in their first 3 months. You can run a complete prompt evaluation workflow without paying anything.
⚠️ Scoring Function Complexity
Braintrust Pro custom scorers require TypeScript or Python. If no one on your team writes scoring functions, Braintrust's main differentiator is unusable. However, the free tier and Loop agent reduce this barrier. Check team capability before committing to Pro.
PromptHub: Version Control at $50–200/Month
PromptHub is a prompt version control and sharing platform — teams store prompts in a central library, tag versions, and share across the organization without juggling spreadsheets or Slack messages. Simplest to onboard of the four.
Starter ~$50/month; Pro ~$200/month. Web UI for non-technical users. Version history for each prompt, tags for organization, deployment workflows. Supports OpenAI, Anthropic, and custom APIs. Tradeoff: no custom evaluation scoring; limited to built-in quality checks; not suitable for teams running live A/B experiments.
Vellum: Production Traffic Splitting at $200–500/Month
Vellum is a prompt deployment platform with built-in A/B testing that splits real production traffic between prompt variants and measures real-world output quality — best for teams running live LLM features. Vellum is a control plane, not a testing tool.
Starter $200/month; Growth $500/month; Enterprise custom. Routes production traffic by percentage between variants. Evaluation compares variants on test datasets. Team features: shared workspace, PR-style prompt reviews, deployment approval workflows. Tradeoff: most expensive option; overkill for pre-production teams or teams not yet handling real user traffic.
For understanding when A/B testing adds value vs. manual optimization, see Manual vs Automated Prompt Optimization.
Promptfoo: Free Open-Source CI/CD Testing
Promptfoo is an open-source CLI tool that runs automated prompt test suites against multiple LLMs — teams integrate it into CI/CD pipelines to catch prompt regressions before deployment. Free (MIT license). Define test cases in YAML, commit to Git, and Promptfoo runs them on every PR.
Supports 40+ LLM providers. GitHub Actions integration available. You provide inputs, expected output patterns, and custom LLM-based assertions. Team-friendly: test configs committed to Git, run in CI, no account or monthly bills. Tradeoff: no UI; engineers only; no built-in collaboration features beyond Git.
prompts:
- "Summarize in 3 bullets: {{text}}"
providers:
- openai:gpt-5.5
- anthropic:claude-opus-4-7
tests:
- vars:
text: "Long document..."
assert:
- type: contains
value: "•"
- type: llm-rubric
value: "Exactly 3 bullets"💡 Promptfoo + GitHub Actions
Promptfoo YAML test configs commit directly to Git. On every PR, GitHub Actions runs the test suite against all configured models and blocks merge on failure. Zero monthly cost, full CI/CD integration.
PromptQuorum: Cross-Model Comparison Before Optimization
Before committing to Braintrust, Vellum, PromptHub, or Promptfoo for a specific LLM provider, use PromptQuorum to dispatch one prompt to 25+ models simultaneously and see which performs best — a model-agnostic first step. Free tier available.
Unlike the four tools above (which optimize for a single model at a time), PromptQuorum answers "which model handles this prompt best?" in one run. After you discover the optimal model with PromptQuorum, then route to Braintrust for deeper evaluation, Vellum for production A/B testing, or Promptfoo for CI/CD regression prevention.
- 25+ models including GPT-4o, Claude Opus 4.7, Gemini 3.1 Pro, and local models via Ollama and LM Studio
- 9 built-in prompt frameworks — TRACE, CO-STAR, CRAFT, and more
- Side-by-side response comparison with consensus scoring
- Token count per model — see cost differences before committing
- Free tier — no engineering setup required
Head-to-Head: All 4 Tools Compared
No single tool excels on all five criteria. Braintrust leads on evaluation depth; Vellum leads on production traffic splitting; Promptfoo leads on free CI/CD; PromptHub leads on simplicity.
| Tool | Primary Use | Collaboration | CI/CD | Pricing | Best For |
|---|---|---|---|---|---|
| Braintrust | Output evaluation | ✅ Roles + dashboards | ✓ API + MCP | Free / $249 Pro | Quality-focused teams |
| PromptHub | Version control | ✅ Team workspace | ✗ None | $50–200/mo | Content teams |
| Vellum | Production A/B | ✅ PR reviews | ✓ Webhooks | $200–500/mo | Live features |
| Promptfoo | CI/CD testing | Git-based | ✅ GitHub Actions | Free | DevOps teams |
| PromptQuorum | Cross-model comparison | ✓ Shared workspace | ✗ None | Free + credits | Model selection |
📌 Two-Tool Stack Rule
Most teams waste money on 3–4 tools. The optimal stack is two: one for evaluation (Braintrust or Promptfoo) and one for deployment/versioning (Vellum or PromptHub). Total spend: $250–700/month instead of $1,000+.
Tool Selection by Team Type
Match the tool to your team's primary bottleneck and technical depth.
Do not use Braintrust if your team cannot write custom scoring functions — it will sit unused. Do not use Vellum if you have no live users yet — buy it after reaching production. Do not use PromptHub alone if you need to measure output quality — it organizes prompts but cannot score them.
For the full team setup workflow including ownership and review rules, see Prompt Engineering Setup for Small Teams.
- 1Engineering teams with quality concerns → Braintrust
Why it matters: Design custom scoring functions; run repeatable evaluations; measure impact of prompt changes. - 2Content/marketing teams needing version control → PromptHub
Why it matters: Simple web UI; no code required; centralized prompt library. - 3Product teams with live LLM features → Vellum
Why it matters: A/B test on real traffic; approval workflows; measure real-world impact. - 4DevOps/platform teams preventing regressions → Promptfoo
Why it matters: Free; YAML-based; integrates with GitHub; catches regressions in CI. - 5All teams (first step) → PromptQuorum
Why it matters: Benchmark your prompt across 25+ models before committing to optimize for one provider.
Common Mistakes
❌ Buying all four tools to cover all bases
Why it hurts: Total spend reaches $700+/month; you maintain four systems; team confusion about which tool to use for what.
Fix: Pick two: one for evaluation (Braintrust or Promptfoo) and one for deployment (Vellum or PromptHub). Add PromptQuorum as a free first step.
❌ Not evaluating the free tiers first
Why it hurts: Both Braintrust (1M traces, 10K scores free) and Promptfoo (fully free) offer enough capacity to run a real evaluation before paying. Teams that skip the free tier waste the first month learning what they should have measured.
Fix: Start with Promptfoo (free CLI) or Braintrust free tier. Build your evaluation dataset. Define your quality metrics. Only then evaluate paid tools against your established baseline.
❌ Choosing a tool by brand reputation instead of workflow fit
Why it hurts: You buy Braintrust Pro but your team is non-technical and cannot write scoring functions; or you buy PromptHub when your actual bottleneck is measuring quality.
Fix: Identify your primary bottleneck first (evaluation, versioning, A/B testing, regression prevention) before evaluating tools.
❌ Adopting a tool without building an evaluation dataset
Why it hurts: You sign up for Braintrust or Vellum but have no labeled input/output pairs to score against. Tools sit unused; you see no ROI.
Fix: Build a test set of 20–50 labeled examples before paying for any platform. Use Braintrust free tier or Promptfoo to validate your metrics first.
❌ Using Vellum without a quality metric
Why it hurts: You A/B test two prompts on production traffic but have not defined "good output." Sales variant gets routed to users; no one can explain why.
Fix: Define 3–5 quality criteria and implement them as assertions (in Promptfoo) or custom scorers (in Braintrust) before running A/B tests.
How to Choose Between These 4 Tools
- 1Identify your primary bottleneck: is it output quality, cost, latency, or team velocity?
- 2Assess technical depth: non-technical team → PromptHub; mixed → Braintrust + Vellum; engineering-heavy → Promptfoo.
- 3Build a labeled evaluation dataset (20–50 input/output pairs) before evaluating any paid tool.
- 4Start with one free tool (Promptfoo or PromptQuorum) to establish baseline metrics.
- 5Run a 2-week trial with the team's actual prompts before committing to a SaaS platform.
- 6Plan for two tools: one for evaluation and one for deployment/versioning.
💡 Pro Tip: Build a Test Dataset First
Build a test set of 20–50 labeled input/output pairs BEFORE evaluating any paid tool. Without a baseline dataset, you can't measure whether the tool actually improves your prompts — you're just paying for a dashboard with no data in it. Use Braintrust free tier or Promptfoo (free) to validate your metrics first.
💡 Free First, Paid Second
Start with Promptfoo (free) + PromptQuorum (free tier) to establish baselines. Only add Braintrust Pro or Vellum after you have 20+ labeled test cases and a defined quality metric. Paid tools without baselines = wasted budget.
FAQ
What is the main difference between Braintrust and PromptHub?
Braintrust is an evaluation platform: you log API calls, define custom scoring functions, and run A/B experiments to measure output quality. PromptHub is a version control system: you store prompts in a library, tag versions, and share across the team. Use Braintrust when your bottleneck is measuring quality; use PromptHub when your bottleneck is organizing prompts.
Is Promptfoo really free?
Yes. Promptfoo is open-source (MIT license) and has no paid tier. You run it as a CLI tool on your own infrastructure or in GitHub Actions. There are no monthly fees, API call limits, or freemium restrictions.
Should I choose Braintrust or Vellum?
Choose Braintrust if your primary goal is measuring and improving output quality with custom metrics. Choose Vellum if your primary goal is A/B testing on real production traffic. Braintrust works best pre-production; Vellum works best with live users.
How much more expensive is Vellum than Braintrust?
Braintrust Pro is $249/month (free tier also available with 1M spans + 10K scores). Vellum Starter is $200/month; Growth is $500/month. At the Pro level, Braintrust is slightly more expensive than Vellum Starter but includes significantly more evaluation capacity. Both have free or low-cost entry points. Promptfoo is free; PromptHub is $50–200/month.
How do I integrate Promptfoo with GitHub Actions?
Promptfoo provides a GitHub Actions template. Define your test cases in YAML, commit the config to Git, and use the official promptfoo-github-action in your workflow file. On every PR, Promptfoo runs your tests against all configured models and reports pass/fail status.
Can PromptHub replace Braintrust?
No. PromptHub stores and versions prompts. Braintrust evaluates and scores prompts. You can use PromptHub alone if your only need is organizing prompts; you cannot use it alone if you need to measure output quality or run experiments.
Is Vellum the same as a prompt management platform?
No. Vellum is a deployment and A/B testing platform. It does include basic version control, but its primary strength is splitting production traffic between prompt variants and measuring real-world impact. True prompt management tools (PromptHub) focus on organizing and sharing prompts, not testing.
Are there alternatives beyond these 4 tools in 2026?
Yes. The prompt evaluation market expanded significantly in 2025-2026. Confident AI offers 50+ built-in evaluation metrics at $19.99–49.99/seat/month with lower tracing costs than Braintrust ($1/GB vs $3/GB). Galileo AI provides runtime guardrails via their Luna-2 evaluation models ($100+/month). Arize Phoenix is a free, open-source LLM observability platform. For most teams, the four tools in this comparison plus Confident AI cover all practical needs.
Sources
- Braintrust — AI Evaluation Platform — official documentation; basis for Loop agent, MCP integration, SOC 2 certification, and $249/month Pro plan pricing (restructured March 2026)
- PromptHub — Prompt Version Control — product homepage; basis for version control, web UI, and $50–200/month pricing claims
- Vellum — LLM Deployment and A/B Testing — product overview and pricing page; basis for traffic splitting, approval workflow, and $200–500/month claims
- Promptfoo — Open-Source Prompt Testing — GitHub repository and documentation; basis for MIT license, YAML config, and GitHub Actions integration claims
- PromptQuorum — Multi-Model Dispatch — multi-model comparison tool; basis for 25+ model dispatch and cross-model comparison claims
- Confident AI — Emerging evaluation platform offering 50+ built-in metrics at $19.99–49.99/seat/month
- Galileo AI — Luna-2 evaluation models and runtime guardrails for LLM applications
- Arize Phoenix — Open-source LLM observability platform for tracing and evaluation