Braintrust vs Vellum vs PromptLayer

TL;DR

Braintrust is for engineers who own a rigorous offline eval loop: datasets, scorers, run-to-run diffs, and regression gates in CI, metered by scores. Vellum and PromptLayer are prompt-ops platforms where product teams and domain experts manage and ship prompts, with lighter eval around the edges. Braintrust and PromptLayer publish prices; Vellum keeps its developer-platform price behind a sales form.

Pick Braintrust when engineers own evaluation and the bottleneck is "is this prompt or model change better" on fixed datasets ($0 Starter, $249/mo Pro). Pick Vellum when a cross-functional team builds and ships an LLM feature end to end in one platform (free five-user Startup tier, Pro reported at $500/mo, sales-led above that). Pick PromptLayer when prompt iteration is stuck behind engineering and a PM or domain expert needs to ship prompts without a pull request (free 5-user tier, flat $49/mo Pro, $500/mo Team). All three are closed source. For the eval-vs-trace axis of this decision, see Braintrust vs LangSmith; for the full field, see Braintrust alternatives.

Quick Comparison

The three split on two axes: who owns the work and how deep the eval goes. Braintrust centers on engineering-owned evaluation. Vellum and PromptLayer center on product-owned prompt operations with eval bolted around them. On price, Braintrust posts $0 and $249, PromptLayer posts $0, $49, and $500, and Vellum routes its developer platform through sales above a free five-user tier.

Pricing Transparency: Published vs Behind Sales

Two of the three publish their numbers. Braintrust posts $0 Starter and $249/mo Pro. PromptLayer posts $0 Free, $49/mo Pro, and $500/mo Team. Vellum keeps its developer-platform price behind a sales form, and the one figure that surfaces jumps from a free five-user tier straight to $500/mo. So part of this decision is which vendor states a number before a call.

Braintrust meters scores, the output of evaluating. The free Starter plan ships $10 of credits, 1 GB of processed data, 10k scores, 14-day retention, and unlimited users. Pro is $249/mo for 5 GB, 50k scores, 30-day retention, and RBAC, with tokens billed separately at $0.06/M input and $0.40/M output. PromptLayer meters requests. The free plan covers 5 users and 2.5k requests a month; Pro is a flat $49/mo with unlimited prompts and $0.003 per extra request; Team is $500/mo for 25 users, 100k+ requests, and $0.002 per request overage.

Vellum prices its developer platform through sales. A ZenML teardown reports that "Vellum does not publicly list pricing details on its website", with a free five-user Startup tier, a jump "from free to $500/month" for Pro, and daily execution caps where "hitting the limit means either you upgrade or you're done for the day." Note the public vellum.ai site now shows a consumer personal-assistant product; the developer-platform pricing moved off the public page, which is why the dev-platform number is quote-based above the free tier.

Engineering-Owned Eval vs Product-Owned Prompts

Name who edits a prompt in your company and the choice narrows. In a Braintrust shop, engineers own the eval loop: prompts live next to scorers and datasets, and changes move through code review and CI. In a Vellum or PromptLayer shop, a PM, QA tester, or domain expert edits a prompt in a visual workspace and ships it without a pull request.

That ownership change pulls eval depth in opposite directions. Braintrust treats evaluation as the engine: datasets, scoring functions, and the diff between two runs are first-class, and a regression gate sits in CI. The two prompt-ops platforms treat the prompt as the engine and eval as a feature around it. PromptLayer's prompt-management docs spell out the no-pull-request workflow it is built around: a registry, a visual editor, and release labels that promote a version without a deploy. Vellum wraps the prompt in a full build-and-ship platform, a prompt playground plus a visual workflow builder plus deployment and monitoring, with an evaluation module inside it.

So the comparison is not feature-for-feature. A team where engineers already run a rigorous offline pass gets depth from Braintrust and would feel a prompt-ops platform as eval it has to bolt onto. A team where prompt iteration is stuck behind engineering availability gets unblocked by Vellum or PromptLayer and would feel a code-and-CI eval engine as a wall. Buy the tool whose engine matches your bottleneck, not the one whose weak axis you will pay for and never use.

When Braintrust Wins

Braintrust wins when engineers own the loop and the bottleneck is rigorous offline evaluation. You run fixed datasets through scoring functions, diff run against run, and gate regressions in CI, billed against the scores that work produces. Prompts live in version control next to the scorers that test them, not in a separate visual editor.

• Engineers own the loop and the bottleneck is rigorous offline evaluation, not prompt logistics.
• You run large eval suites and want billing tied to scores, the unit you actually generate.
• You want run-to-run diffs and CI regression gates as first-class objects.
• Prompts belong in version control next to the scorers and datasets that test them.

When Vellum Wins

Vellum wins when a cross-functional team builds and ships an LLM feature in one product. A PM drafts and compares prompts in a playground, an engineer wires a workflow node, and the same place handles eval, deployment, and monitoring. The price is sales-led above a free five-user tier, accepted as the cost of standardizing one team on one platform.

• A cross-functional team of PMs and engineers builds and ships LLM features end to end.
• Non-engineers need a visual prompt playground and a workflow builder, not a code-only eval harness.
• You want playground, workflow builder, eval, deployment, and monitoring as one platform.
• A sales-led contract is acceptable in exchange for the whole lifecycle in one place.

When PromptLayer Wins

PromptLayer wins when prompt iteration is stuck behind engineering availability and you want to remove that handoff. A PM, QA tester, or domain expert edits a prompt in a visual registry, tests variations, and promotes a release label with no pull request. A flat, published $49/mo Pro plan metered on requests fits a team where prompt management is the work.

• Prompt iteration is bottlenecked on engineering availability, and you want to remove that handoff.
• PMs, QA, or domain experts should edit and ship prompts in a visual registry with release labels, no pull request.
• You want a published, flat price ($49/mo Pro) metered on requests, not a sales quote.
• Prompt management is the real work, and lighter eval around it is enough.

The Fourth Option: Own the Stack

A fourth path skips all three meters: instrument the stack yourself and own the data end to end. All three approximate quality with LLM-as-judge evals that run offline on samples, so for the always-on signals you wire up open telemetry, store the spans in your own columnar database, and dashboard them. You trade managed retention and a polished UI for no per-unit bill and no sales contract.

Concretely: instrument with OpenLLMetry (Apache-2.0, ~7.2k stars, free), store the spans in your own ClickHouse (Cloud Basic around $66/mo, or self-host the Apache-2.0 build for free), and dashboard it with Grafana OSS. You give up the prompt registry, the build platform, and the experiment UI, and you take on the operational weight in return. We walk through the whole build in build your own LLM observability.

What They All Miss: Semantic Signals in Production

Everything above is the offline experiment and the prompt registry. None of the three catches the meaning of a turn while it happens. A response that quotes the wrong refund policy returns a 200 with normal latency. A quietly angry user produces the same log line as a delighted one. A looping agent looks like one doing work. The eval that would flag it runs tonight, on a sample, after the user already left.

That timing gap is structural. Braintrust, Vellum, and PromptLayer all approximate semantic checks with LLM-as-judge evals, and those run offline on samples: a fraction of traffic, scored after the fact. A Reflex runs inline on every turn instead. It is a classifier that returns the label as an API response, in under 90 milliseconds, cheap enough to score every turn rather than a sample.

The labels are the ones an eval would otherwise check tonight: is_user_frustrated, stuck-in-a-loop, leaked-thinking, jailbreak, or a signal specific to your product. The built-in signals cover jailbreak, guardrail, leaked-thinking, stuck-in-a-loop, incomplete-thought, ambiguity, difficulty, and domain, and you can train a custom signal in under an hour in the Reflex dashboard. Pricing is realtime at $0.0005 per event, where one event is 2,048 tokens, which is what makes scoring every turn affordable instead of sampling. It is live and self-serve today.

curl -X POST "https://api.morphllm.com/v1/reflex/predict" \
  -H "Authorization: Bearer $MORPH_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "stuck-in-a-loop", "text": "<the agent turn>"}'

# {
#   "model": "stuck-in-a-loop",
#   "mode": "single_label",
#   "classes": [
#     { "class_id": 0, "label": "progressing", "score": 0.04, "selected": false },
#     { "class_id": 1, "label": "looping",     "score": 0.96, "selected": true }
#   ],
#   "inference_time_ms": 88
# }

The label with "selected": true is the answer; there is no separate top-level field to read. Because it comes back as an API response and not a dashboard panel, it composes with whichever tool you picked: write it onto a Braintrust span as a score, attach it to a Vellum monitoring log, push it to a PromptLayer request log, alert on it in Slack, or route on it inline. It complements an eval, build, or prompt-management platform; it does not replace one. Full docs are at docs.morphllm.com.

Frequently Asked Questions

Braintrust vs Vellum vs PromptLayer: what is the core difference?

They split into two jobs. Braintrust is engineering-owned evaluation that meters scores. Vellum and PromptLayer are product-owned prompt-ops platforms with lighter eval: Vellum is an end-to-end build-and-ship platform, PromptLayer is prompt-management-first. See engineering-owned eval vs product-owned prompts.

How much do they cost?

Braintrust Starter is free ($10 credits, 10k scores); Pro is $249/mo for 50k scores. PromptLayer Free is $0 (5 users, 2.5k requests); Pro is a flat $49/mo; Team is $500/mo. Vellum has a free five-user Startup tier and a Pro tier reported at $500/mo, but its dev-platform pricing is sales-led. The pricing section has the full math.

Are any of them open source?

No. Braintrust, Vellum, and PromptLayer are all closed source, with self-hosting on Enterprise only. If open source is a requirement, look at Braintrust vs Langfuse or the DIY stack.

Which one should non-engineers use to own prompts?

Vellum or PromptLayer. Both let a PM or domain expert edit and ship a prompt in a visual workspace without a pull request. Braintrust assumes engineers own the loop in code and CI. See who owns the prompts.

Do they catch wrong answers and frustrated users in production?

Not in real time. All three rely on offline, sampled LLM-as-judge evals, so a regression shows up in the next batch, not on the turn it happened. Catching it inline needs a per-turn classifier, covered in semantic signals.