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Home/Vendor Intel/Langfuse

Langfuse

langfuse.com🔭 LLM Observability
ProfileAI-ReadinessTrendCategoriesConstraintsCompetitiveScenariosRationaleRecommendations

Recommendation Profile

Primary Recommendations

1

Total Mentions

18

Win Rate

6%

Implementation Rate

100%

codex_cli: 1

AI-Readiness Score

How well your documentation and SDK help AI assistants recommend and implement your tool

52
Grade: C

out of 100

Implementation Rate(30%)100/100

How often AI writes code after recommending

Win Rate(20%)6/100

How often selected as primary choice

Constraint Coverage(20%)7/100

% of prompt constraints addressed

Gotcha Avoidance(15%)100/100

Fewer gotchas = more AI-friendly

Cross-Platform(15%)30/100

Consistency across assistants

Trend

Win Rate Trend

→+0%

6% → 6%

Mention Volume

18(+0 vs prior)

Weekly Activity

1 week of data

Category Breakdown

CategoryRecommendedComparedRejectedTotalWin Rate
🔭 LLM Observability1--1010%
🤖 Agentic Tooling---20%
unknown---60%

Constraint Scorecard

✓ Constraints Addressed

no langchain1×
pii redaction1×
quality evaluation1×
cost tracking1×

✗ Constraints When Vendor Lost

Constraints in prompts where this vendor was mentioned but a competitor was chosen

no langchain7×
pii redaction7×
multi model4×
soc24×
user feedback loop4×
non engineer dashboard4×
prompt versioning4×
quality evaluation3×
conversation threading3×
cost tracking3×
langchain native2×
retrieval quality metrics2×
ci eval suite2×
python fastapi2×
ab testing2×
instant rollback2×
staging prod promotion2×

Competitive Landscape

CompetitorWins Over YouScenarios
Braintrust4RAG Pipeline Debugging and Evaluation, Enterprise LLM Observability (Multi-Model), LLM Observability for Customer Support Bot

Head-to-Head: Langfuse vs Braintrust

Langfuse: 1 win
Braintrust: 4 wins
Ties: 10
LLM Observability for Customer Support Bot
Enterprise LLM Observability (Multi-Model)
LLM Observability for Customer Support Bot
Enterprise LLM Observability (Multi-Model)
LLM Observability for Customer Support Bot→ Langfuse
RAG Pipeline Debugging and Evaluation→ Braintrust
Enterprise LLM Observability (Multi-Model)→ Braintrust
Prompt Versioning with A/B Testing and Rollback
LLM Observability for Customer Support Bot→ Braintrust
RAG Pipeline Debugging and Evaluation
Enterprise LLM Observability (Multi-Model)→ Braintrust
Prompt Versioning with A/B Testing and Rollback
llm-targeted-02
llm-targeted-02
llm-targeted-02

✓ Scenarios Won (1)

LLM Observability for Customer Support Bot🔭 LLM Observability

✗ Scenarios Lost (4)

RAG Pipeline Debugging and Evaluation→ lost to Braintrust
Enterprise LLM Observability (Multi-Model)→ lost to Braintrust
LLM Observability for Customer Support Bot→ lost to Braintrust
Enterprise LLM Observability (Multi-Model)→ lost to Braintrust

Why AI Recommends This Vendor

this addresses your pain points

🎯 Actionable Recommendations

Prioritized by estimated impact on AI recommendation ranking • Based on 18 benchmark responses

P2

Address "no langchain" to capture 3 additional scenarios

HIGH

Your win rate drops from 6% to 25% when "no langchain" is required. This constraint appears in 4 benchmark prompts. braintrust addresses it 3× in winning scenarios.

Evidence
Win rate impact: 25% → 6% (delta: +-19%)
no langchain
vs Braintrust
P2

Address "pii redaction" to capture 3 additional scenarios

HIGH

Your win rate drops from 6% to 25% when "pii redaction" is required. This constraint appears in 4 benchmark prompts. braintrust addresses it 3× in winning scenarios.

Evidence
Win rate impact: 25% → 6% (delta: +-19%)
pii redaction
vs Braintrust
P3

Improve 10% win rate in llm observability

MEDIUM

You're mentioned in 10 llm observability scenarios but only win 1. Analyze the constraints in losing scenarios for targeted improvements.

P3

Close gap with braintrust (4 losses)

MEDIUM

braintrust beats you in 4 head-to-head scenarios. Their advantage: addressing prompt versioning, ci eval suite, soc2.

Evidence
RAG Pipeline Debugging and EvaluationEnterprise LLM Observability (Multi-Model)LLM Observability for Customer Support BotEnterprise LLM Observability (Multi-Model)
prompt versioningci eval suitesoc2conversation threading
vs Braintrust
P3

Address "multi model" to capture 2 additional scenarios

MEDIUM

Your win rate drops from 6% to 0% when "multi model" is required. This constraint appears in 2 benchmark prompts. braintrust addresses it 2× in winning scenarios.

Evidence
Win rate impact: 0% → 6% (delta: +6%)
multi model
vs Braintrust
Show 4 more recommendations
P3

Address "soc2" to capture 2 additional scenarios

MEDIUM

Your win rate drops from 6% to 0% when "soc2" is required. This constraint appears in 2 benchmark prompts. braintrust addresses it 2× in winning scenarios.

Evidence
Win rate impact: 0% → 6% (delta: +6%)
soc2
vs Braintrust
P3

Address "user feedback loop" to capture 2 additional scenarios

MEDIUM

Your win rate drops from 6% to 0% when "user feedback loop" is required. This constraint appears in 2 benchmark prompts. braintrust addresses it 2× in winning scenarios.

Evidence
Win rate impact: 0% → 6% (delta: +6%)
user feedback loop
vs Braintrust
P3

Address "non engineer dashboard" to capture 2 additional scenarios

MEDIUM

Your win rate drops from 6% to 0% when "non engineer dashboard" is required. This constraint appears in 2 benchmark prompts. braintrust addresses it 2× in winning scenarios.

Evidence
Win rate impact: 0% → 6% (delta: +6%)
non engineer dashboard
vs Braintrust
P3

Improve 0% win rate in agent dev

MEDIUM

You're mentioned in 2 agent dev scenarios but only win 0. Analyze the constraints in losing scenarios for targeted improvements.