Braintrust vs Humanloop vs LangSmith: Real LLM Evaluation Cost per 100,000 Eval Runs in 2026

The short answer: for evaluation work, human review time can dominate total cost once a workload samples a meaningful share of runs for human checking, while ordinary judge-model calls are often much smaller than the platform fee itself, and Humanloop cannot be priced at all. Anthropic acquired Humanloop in 2025, and its pricing is entirely custom-quoted (QUOTE-ONLY / UNKNOWN); this article prices Braintrust and LangSmith and treats Humanloop as a comparison point without numbers. At 100,000 eval runs a month with a deterministic grader, the platform cost is $324 on Braintrust Pro and $514 on LangSmith Plus once LangSmith's extended-retention upgrade fee is correctly included (every graded run, deterministic or judge-based, triggers that upgrade), and the judge and human-review cost is $0. Add one LLM judge per test and the judge model cost alone is about $48 per 100,000 runs. Add a three-judge ensemble plus a 5% human-review sample at 3 minutes each, and human review becomes the dominant line at $8,750 per 100,000 runs, roughly 14 times the $632 Braintrust platform fee at that volume.

What each vendor bills, and what it does not

Braintrust (OFFICIAL, Braintrust pricing page). Scoring is the product: Starter is free with 10,000 scores then $2.50 per 1,000, and Pro is $249 a month with 50,000 scores then $1.50 per 1,000, plus a separate gigabyte meter for processed data (1 GB free on Starter, 5 GB on Pro, then $4 or $3 per GB). Model credits are included ($10 on Starter, $249 on Pro) and consumed at token rates once exhausted. Every eval run that produces a score counts against the score meter regardless of whether the score came from a deterministic check, an LLM judge, or a human reviewer.

LangSmith (OFFICIAL, LangSmith pricing page). Tracing is the product, and an evaluation run is recorded as a trace: Plus is $39 per seat per month with 10,000 free base traces, then $2.50 per 1,000. Running the same test suite five times a day for a month at 200 test cases produces 30,000 base traces; because graded eval runs receive feedback (a score) and therefore incur the Extended Data Retention Upgrade described below, those 30,000 base traces are not necessarily billed the same as 30,000 ordinary production traces that receive no feedback — the graded set additionally carries the retention-upgrade charge that an unscored production trace would not. LangSmith does not meter judges or scores separately from traces: a targeted check of LangSmith's own documentation confirms a judge call does not create a new, separately billed trace. What it does instead is trigger LangSmith's data-retention upgrade, and this upgrade is not limited to production monitoring: LangSmith's own documentation states that dataset experiments follow the same retention rule as any other trace, upgrading to extended (400-day) retention whenever feedback is added to a run, and explicitly lists "the dataset has evaluators configured" as one of the ways that feedback gets attached — alongside the online-evaluator and manual-annotation paths this article's companion observability piece covers. Because every shape in this article (A's deterministic grader included) attaches a score to its eval-run trace, every graded run in this comparison triggers the same upgrade, regardless of how many judges are involved. The billing itself is additive, not a simple swap: LangSmith's invoice carries two separate line items, a Base Charge that applies to every trace and a separate Extended Data Retention Upgrade charge of $2.50 per 1,000 that applies only to the traces that get upgraded, so an upgraded trace costs $2.50 (base) + $2.50 (upgrade) = $5.00 per 1,000 in total. The 10,000 free base traces a month offset only the Base Charge line; LangSmith's documentation does not describe a separate free allowance for the upgrade charge, so this article treats every graded eval run's upgrade fee as fully billable from the first run, which is the conservative reading given what is confirmed publicly.

Humanloop (QUOTE-ONLY / UNKNOWN). Humanloop was acquired by Anthropic in 2025, and by 2026 its pricing page offers only a free trial and a custom Enterprise quote; no self-serve tier, per-run rate, or seat price was public at the time of review. Independent buyer guides list its evaluation tooling as less mature than Braintrust's or LangSmith's (manual eval generation, no fine-tuning, contact-sales issue tracking), which matters for a cost comparison because a quote-only enterprise product cannot be benchmarked against two self-serve platforms without inventing a number. This article does not invent one.

The three grading shapes

All are ILLUSTRATIVE. A: deterministic or code grader — a script checks output against a rule, no model call. B: one LLM judge per test — a single judge call per eval run, modeled at 500 input and 150 output tokens against a mid-tier model at $0.50 input and $1.50 output per million tokens. C: three-judge ensemble plus human sampling — three judge calls per run, plus 5% of runs also get a 3-minute human review at a $35-an-hour loaded rate.

Judge cost per run: $0.000475 (one judge) and $0.001425 (three judges). Human review cost per run at 5% sampled: $0.0875 (3 minutes × 5% × $35/60).

Platform, judge and human cost per volume

Assumptions: LangSmith seats scale with volume (1 seat at 10,000 and 100,000 eval runs a month, 3 at 1 million; ILLUSTRATIVE); Braintrust processed-data volume scales with grading complexity (0.001, 0.002 and 0.004 GB per 1,000 runs for shapes A, B and C respectively, ILLUSTRATIVE). For shape C, this article treats each of the three LLM judges as a separately metered Braintrust score, and treats the 5%-sampled human annotation as a fourth score on only those sampled runs, for an average of 3.05 scores per run; Braintrust's plan choice for each row below is whichever of Starter or Pro produces the lower total cost at that shape's actual score and gigabyte volume, not an assumption about which plan the workload "should" need.

At 10,000 shape-C runs, 30,500 scores (10,000 runs × 3.05) cost $51 on Starter's $2.50-per-1,000 overage against a flat $249 on Pro, so Starter is cheaper at this volume even though the ILLUSTRATIVE gigabyte line (0.04 GB) is trivial either way. At 100,000 runs, 305,000 scores cross into Pro's favor ($632 on Pro versus $738 on Starter), because Pro's lower $1.50-per-1,000 overage rate on scores above its larger 50,000-score allowance overtakes Starter's cheaper flat fee. The plan crossover here is driven by the score meter, not by processed-data gigabytes.

Total cost per 100,000 evals

Formula: total = platform + judge model cost + human review cost, scaled to a 100,000-run baseline.

At shape A, LangSmith runs about 59% higher than Braintrust ($514 versus $324) once the extended-retention upgrade is priced in correctly; at shape B the gap is similar (about 51%, $562 versus $372). The judge model itself is still a rounding error next to either platform fee at these two shapes. At shape C, human review is 92% of the total on Braintrust ($8,750 of $9,525) and 93% on LangSmith ($8,750 of $9,406), so the platform choice matters far less than the human-sampling rate. Excluding human review, Braintrust's non-human cost (platform plus judge, $774) is about 18% higher than LangSmith's ($656) at this shape, the reverse of the ranking at shapes A and B, where LangSmith is the pricier of the two once its retention-upgrade fee is correctly included.

Cost of repeated regression suites

A regression suite of 200 test cases run automatically on every merge is a different volume driver than ad hoc evaluation. Formula: monthly eval runs = test cases × suite runs per month.

A CI-gated regression suite is cheap on the judge-model line even run 100 times a month; it becomes expensive only when the suite itself is large (thousands of cases) or graded by an ensemble with human sampling, as shape C shows.

Break-even: QA and engineering hours saved

Formula: hours needed = monthly cost ÷ loaded hourly cost, with $85 an hour for a QA or ML engineer (ILLUSTRATIVE). This is a threshold: the eval pipeline pays for itself if it prevents or catches this many hours of manual investigation, not a claim that it will.

Shape C's break-even threshold is roughly two and three-quarter engineer-weeks a month on either platform, which is a real commitment; it reflects human-review labor that would otherwise have to happen manually regardless of which platform hosts it, and the platform cost itself, even with LangSmith's retention-upgrade fee correctly included, is still a small share of that number.

Sensitivity

1. Human sample rate. Dropping the human-review sample from 5% to 1% on shape C cuts human cost by 80%, from $8,750 to $1,750 per 100,000 runs, making the platform fee relevant again.
2. Judge model choice. A frontier judge model at 5–10 times the token rate used here multiplies the judge-cost lines proportionally; the platform fee does not change.
3. Plan tier crossover. Braintrust's Starter-to-Pro crossover depends on both score volume and processed-data volume, not on run count alone. In the shape-C model used here, score volume is what drives the crossover; a sufficiently data-heavy workload (larger payloads per run) could instead make processed gigabytes the deciding meter, so a token-heavy judge or verbose test cases can shift which meter forces the upgrade to Pro.
4. Seat count. LangSmith's per-seat fee means a 10-engineer team on Plus pays $390 a month in seats alone before any trace overage, versus Braintrust's unlimited users on any plan.

Budgeting traps

• Comparing a platform-only quote to a platform-plus-judge quote. Neither Braintrust nor LangSmith includes judge model calls in the platform fee; that is a separate, usage-based bill from the model provider.
• Ignoring human review as a "free" quality gate. At shape C volumes, human sampling costs more than either platform's entire fee.
• Treating Humanloop as comparable without a number. Its post-acquisition pricing is not public, and a custom quote for an enterprise product should not be benchmarked against a self-serve platform's list price.
• CI suite multiplication. Running the same suite many times a day multiplies eval-run volume linearly; check whether your platform's free tier or lower plan tier still covers it.

What to ask before you buy

Ask each vendor for a quote scoped to your real grading mix: the share of runs graded deterministically, by a single judge, and by an ensemble with human sampling. Ask Humanloop for a written quote if you are considering it, since no public rate exists to compare against.

ARTICLE 3