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Braintrust vs Datadog Agent Observability: Eval Platform or Observability Suite?

Fintool runs both: release evals in Braintrust and production monitoring in Datadog. A Source.ag engineer found Datadog's cost rollup overcounting spend about 2.2 times, and at 200,000 traces a month the two bills land within US$10 of each other.

Reading time
7 min read
Sources
17 cited
In this piece

GILD verdict

Choose Braintrust when evals decide what ships, Datadog when operators already run it, and both when each group owns one job.

The workflow
a team deciding where release evals and production agent monitoring live, and who reads each
The decisive evidence
  • ReportedFintool's founder wrote on Jan 24, 2026 that every model, prompt and skill change is evaluated against a Braintrust test set, and Datadog's customer story says Fintool monitors its LLM application with Agent Observability.12
  • ReportedA Source.ag engineer reported on Jun 16, 2026 that duplicate LLM spans made Datadog's cost rollup overcount spend about 2.2 times with LangChain over the Anthropic SDK.3
  • DocumentedDatadog bills only LLM spans, from US$160 a month for 100,000 on an annual commitment, while Braintrust Pro bills US$249 plus processed data and scores.512
  • GILD analysisCost does not separate them at this volume, and no team has published a head-to-head of the two. GILD's calculation from published rates puts 200,000 traces a month at US$474 on Braintrust Pro and US$465 on Datadog's annual commitment.
The tradeoff
Fintool runs both: product people grade results in Braintrust and operators read agent traces beside APM in Datadog. Datadog keeps agent traces next to the rest of production but keeps them 15 days unless you buy retention; Braintrust keeps evals, datasets and human review in one place but runs no APM for the wider application.1271213
What would change the call
The call moves when a team publishes the same eval suite run on both, or when your bill shows scores or retained LLM spans growing faster than the other side's unit.

Named teams use the pair for different jobs. Fintool gates every model and prompt change on Braintrust experiments and, in Datadog's customer story, monitors its LLM application with Datadog; Dropbox blocks merges on Braintrust evals. Source.ag found Datadog's cost rollup overcounting spend about 2.2 times in June 2026. At 200,000 traces a month, GILD's calculation puts Braintrust Pro at US$474 and Datadog at US$465 on an annual commitment.

What teams report

Failures and incidents 1

  1. Source.agOn Datadog Agent Observability

    With LangChain over the Anthropic SDK, dd-trace-js emitted two LLM spans for each model call, and Datadog's cost rollup summed both, overcounting Source.ag's spend about 2.2 times (GitHub issue, Jun 16, 2026).

    “…cost rollup sums both layers and overcounts spend (~2.2× in our case)…”
    Tomas Alabes, Engineer, Source.agGitHub, DataDog/dd-trace-js, , post

Run both 1

  1. Fintool

    Fintool's founder wrote on Jan 24, 2026 that every model, prompt and skill change is evaluated in Braintrust, and Datadog's undated customer story says Fintool monitors its LLM application with Datadog Agent Observability, so the team runs both.

    “We use Braintrust for experiment tracking. Every model change, every prompt change, every skill change gets evaluated against a test set.”
    Nicolas Bustamante, Founder, Fintoolnicolasbustamante.com, , write-up

Adopted 1

  1. DropboxOn Braintrust

    Dropbox's Dash team adopted Braintrust for datasets, scorers and experiments, and runs about 150 judged queries on every pull request in under 10 minutes (Oct 2, 2025).

    “…we turned to internal datasets by collecting production logs from Dropbox employees dogfooding Dash.”
    Ranjitha Gurunath Kulkarni, Dropbox Dash engineer, DropboxDropbox Tech blog, , write-up

Choosing between Braintrust and Datadog Agent Observability

Choose Braintrust if

  • Evals gate your releases, as at Fintool, which evaluates every model, prompt and skill change against a Braintrust test set, and Dropbox, which blocks merges on about 150 judged queries.
  • Product managers and specialists will curate datasets and grade outputs alongside engineers.
  • You need traces kept in a data plane in your own cloud account.

Choose Datadog Agent Observability if

  • You already run Datadog for APM, infrastructure, or real user monitoring, and want agent traces correlated with services and user sessions under one trace ID.
  • You want to pay by model call: Datadog bills LLM spans only, so GILD's 200,000-trace workload comes to US$465 a month on an annual commitment. Check the cost rollup against your provider invoice, since Source.ag found duplicate LLM spans overcounting spend about 2.2 times.
  • You want sensitive data scanning of prompts and outputs included in the price.

Side by side

DimensionBraintrustDatadog Agent Observability
What teams reportNo team GILD found published a head-to-head of the two. Fintool's accounts come from its founder's blog and from Datadog's own customer story.Fintool evaluates every model, prompt and skill change against about 2,000 Braintrust test cases (Jan 2026). Dropbox Dash runs about 150 judged queries on every pull request in under 10 minutes and blocks merges on red-line misses (Oct 2025).14Datadog's undated customer story says Fintool monitors its multi-provider LLM application with Agent Observability. Source.ag found dd-trace-js emitting two LLM spans per model call with LangChain over the Anthropic SDK, so the cost rollup overcounted spend about 2.2 times (GitHub, Jun 16, 2026).23
PlansList prices in US dollars for Braintrust and for Datadog's default US site. Datadog prices differ by site.Starter US$0 and Pro US$249 a month, both with unlimited users; Enterprise custom, invoiced annually5Free with 40,000 LLM spans a month; Pro from US$160 a month on an annual commitment, US$200 month-to-month, or US$240 on-demand for the first 100,000 LLM spans12
Billing unitAn agent run with 4 model calls and 10 tool and retrieval steps is 4 Datadog LLM spans. On Braintrust it is the bytes of all 14 spans plus every score.Processed data, every byte ingested and measured at ingestion, plus each score recorded; built-in models draw on monthly model creditsLLM spans: each call to a model provider; tool, retrieval, and workflow spans are not billed
Usage priceStarter: 1 GB and 10,000 scores included, then US$4 per GB and US$2.50 per 1,000 scores. Pro: 5 GB and 50,000 scores included, then US$3 per GB and US$1.50 per 1,000 scores5US$3.50 per 10,000 further LLM spans billed annually, US$4.20 month-to-month, or US$5.00 on-demand12
200,000 traces a month, 10 usersGILD's calculation from published rates, Oct 28, 2026. Inputs: 200,000 traces a month, 10 spans, 2 model calls and 1 code score per trace, 20 KB per trace, 10 users, 30 days retained, no judge calls.US$474 a month on Pro: US$249, plus US$225 for 150,000 scores past the 50,000 included, at US$1.50 per 1,000. The 4 GB of data stays inside the 5 GB included.56US$465 a month on an annual commitment: US$160 for the first 100,000 LLM spans, US$105 for the next 300,000 at US$3.50 per 10,000, and US$200 to keep 400,000 LLM spans for 30 days. Month to month, US$554.12
RetentionLogs 14 days on Starter and 30 days on Pro, extendable to 180 days at US$0.50 per GB per month. Up to 365 days on Enterprise. Experiments up to 365 days on every plan7Traces 15 days and trace metrics 15 months by default; a 30-day add-on at US$5 per 10,000 LLM spans a month on an annual commitment, US$5.70 month to month or US$6.50 on demand, with 60 and 90 days at US$8 and US$9 on demand; datasets kept 3 years12
EvaluationDatasets, experiments in code, the UI, or CI, code, LLM-as-a-judge, and human scorers, playgrounds, and online scoring rules with samplingVersioned datasets from production traces, experiments comparing prompts, models, and agent logic, and online evaluations, with unlimited evaluations on every tier
Where it runsBraintrust-run SaaS with a US or EU data plane, fixed at sign-up. Enterprise adds BYOC and a self-hosted data plane on AWS, GCP, or Azure. The control plane always runs at BraintrustDatadog's SaaS, in the Datadog site an organization uses; Datadog lists sites in the US, Germany, Japan, Australia, and the UK, plus US government sites
Sensitive dataSDK masking and span customizers before upload; ingestion redaction for a limited set of customers on requestSensitive Data Scanner included, with 1 GB of scanning per 10,000 LLM spans
Rest of the stackFocused on AI features: no APM, infrastructure monitoring, or log management for the wider applicationCorrelates agent traces with Datadog APM, infrastructure, and real user monitoring, and can be bought on its own

What practitioners report, side by side

Each product's figures come from its own practitioners, for the group you pick.

BraintrustBraintrust+50 Strong positiveHow this figure is made
Datadog Agent ObservabilityDatadog+32 PositiveHow this figure is made
Whose sources to count
Compare Braintrust with
  • Datadog Agent Observability

Overall signal

Teams and leaders rate Braintrust +50, 18 points above Datadog Agent Observability at +32. Most of the gap is in praise, with 20 of Braintrust's 34 sources positive against 9 of Datadog Agent Observability's 26. Braintrust has 3 negative and Datadog Agent Observability 1.

The score counts the authors from teams and leaders that take a side, one vote each with their latest view, and is how far positive votes lead negative ones, per 100 votes, so Braintrust's +50 rests on votes split 19 positive, 10 mixed and 3 negative and Datadog Agent Observability's +32 on 9, 15 and 1.

A lead of a fifth of the votes reads positive and a lead of half strong positive, with the same lines below zero. The dots show each source, positive, mixed, negative. Run a pilot on one production app to measure which failures it surfaces and how long tracing them takes.

Footprint on the AI Application Platform stack

They differ on three of 9 layers. Coverage is each product's placement on this stack, as its product page shows it. Core is a job the product owns; Supports and Touches are jobs it helps with. Coverage says nothing about how well a product does them.

Layers where Braintrust and Datadog Agent Observability differ
LayerBraintrustDatadog Agent Observability
Model
Model gatewaySupportsTouches
Build
Tool useSupportsTouches
Operate
GuardrailsTouchesSupports
The 6 layers where they match
Layers where Braintrust and Datadog Agent Observability match
LayerBraintrustDatadog Agent Observability
Model
Model APIsTouchesTouches
Build
OrchestrationTouchesTouches
Data
RetrievalTouchesTouches
Evaluate
EvaluationCoreCore
Operate
ObservabilityCoreCore
Cloud deploymentTouchesTouches

What practitioners praise

Among teams and leaders, Braintrust alone has three praise themes that reach 5 sources from 3 authors. Datasets, Experiments & Scorers and Trace-Level Debugging lead, at 20 of 21 and 7 of 8. One Trace With APM and Infrastructure and Auto-Instrumentation of Libraries reach 5 sources from 3 authors only for Datadog Agent Observability, at 6 of 6 and 4 of 5. Braintrust needs a pilot on the same production traffic before you choose on either.

What practitioners praise, Teams and leaders
Praised forBraintrustDatadog Agent Observability
Datasets, Experiments & Scorers20 of 21Not a theme in its sources
Trace-Level Debugging7 of 8Not a theme in its sources
Instrumentation & SDK Coverage3 of 5Not a theme in its sources
One Trace With APM and InfrastructureNot a theme in its sources6 of 6
Auto-Instrumentation of LibrariesNot a theme in its sources4 of 5

Each count is the sources that praise a theme, or raise it as a concern, out of those that discuss it. A row pairs the two products when both carry a theme under the same or an equivalent name.

A count ending in "so far" has under 5 sources from 3 authors among teams and leaders. "Not a theme in its sources" means GILD found no theme of that name in that product's sources.

What practitioners raise as concerns

Among teams and leaders, SDK & Ingestion Reliability and Build It Yourself reach 5 sources from 3 authors only for Braintrust, at 6 of 6 and 2 of 6. Datadog Agent Observability needs a pilot on the same production traffic before you choose on either. Missing or Dropped Traces and Double-Counted Spans & Wrong Token Totals reach 5 sources from 3 authors only for Datadog Agent Observability, at 7 of 7 and 5 of 5. Braintrust needs a pilot on the same production traffic before you choose on either.

What practitioners raise as concerns, Teams and leaders
Raised as a concernBraintrustDatadog Agent Observability
SDK & Ingestion Reliability6 of 6Not a theme in its sources
Build It Yourself2 of 6Not a theme in its sources
Missing or Dropped TracesNot a theme in its sources7 of 7
Double-Counted Spans & Wrong Token TotalsNot a theme in its sources5 of 5
Braintrust SignalsDatadog Agent Observability Signals

17 sources

  1. Nicolas Bustamante (Fintool): Lessons from Building AI Agents for Financial Services (Jan 24, 2026)Public sourcenicolasbustamante.com
  2. Datadog customer story: Fintool reduces customer churn by 84% with Datadog Agent Observability (undated)Official documentationdatadoghq.com
  3. GitHub: DataDog/dd-trace-js#8936, duplicate LLM spans overcount spend (Jun 16, 2026)Public sourcegithub.com
  4. Dropbox Tech blog: A practical blueprint for evaluating conversational AI at scale (Oct 2, 2025)Public sourcedropbox.tech
  5. Braintrust pricingOfficial documentationbraintrust.dev
  6. Braintrust docs: Plans and limitsOfficial documentationbraintrust.dev
  7. Braintrust docs: Configure data retentionOfficial documentationbraintrust.dev
  8. Braintrust docs: Deployment options (SaaS, BYOC, self-hosted)Official documentationbraintrust.dev
  9. Braintrust docs: ExperimentsOfficial documentationbraintrust.dev
  10. Braintrust docs: Score production tracesOfficial documentationbraintrust.dev
  11. Braintrust docs: Protect sensitive dataOfficial documentationbraintrust.dev
  12. Datadog pricing: Agent Observability (LLM Observability)Official documentationdatadoghq.com
  13. Datadog Agent Observability product pageOfficial documentationdatadoghq.com
  14. Datadog docs: Agent ObservabilityOfficial documentationdocs.datadoghq.com
  15. Datadog docs: Agent Observability ExperimentsOfficial documentationdocs.datadoghq.com
  16. Datadog docs: Agent Observability evaluationsOfficial documentationdocs.datadoghq.com
  17. Datadog docs: Getting started with Datadog sitesOfficial documentationdocs.datadoghq.com
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