Eval Framework Selection Scorecard

A structured template for evaluating which LLM eval framework(s) to adopt. Fill this in before making a final recommendation to your team or leadership.

Copy this template. Score each framework you’re evaluating. The weights are pre-set for three team types — adjust to match your context.

Step 1 — Define Your Context

Fill these in first. They determine which weights to use.

Field Your answer
Team type ☐ Startup / ☐ Growth-stage / ☐ Regulated enterprise
Primary use case ☐ RAG / ☐ Conversational / ☐ Red teaming / ☐ Production monitoring / ☐ Safety/compliance
EU AI Act scope? ☐ Yes — Annex III high-risk / ☐ Possibly / ☐ No
Python team? ☐ Yes / ☐ Mixed / ☐ No (YAML preferred)
CI/CD gate needed? ☐ Yes — on every PR / ☐ Yes — on releases only / ☐ No
Data residency requirement? ☐ EU only / ☐ Self-host required / ☐ No constraint
Who will maintain evals? ☐ Engineering / ☐ ML team / ☐ PM / ☐ Data team

Step 2 — Score Each Framework

Rate each criterion 1–5 for each framework you’re evaluating.
Then multiply by the weight column that matches your team type.

Scoring Key

Score Meaning
5 Excellent — best-in-class for this criterion
4 Good — strong coverage, minor gaps
3 Adequate — meets minimum needs
2 Weak — requires significant workarounds
1 Poor — does not address this criterion

Criteria Weights by Team Type

Criterion Startup weight Enterprise weight EU-regulated weight
1. Setup time to first eval 3× 1× 1×
2. Metric coverage for your use case 2× 3× 2×
3. CI/CD integration 2× 3× 2×
4. Adversarial / red team coverage 1× 2× 3×
5. Production observability 2× 3× 3×
6. EU AI Act audit trail quality 0× 1× 4×
7. Data residency / self-host option 0× 2× 4×
8. Cost at your expected scale 3× 2× 1×

Scoring Sheet

Replace [Framework A] and [Framework B] with the frameworks you’re evaluating. Add columns as needed.

Criterion Weight (your type) [Framework A] score [Framework A] weighted [Framework B] score [Framework B] weighted
1. Setup time to first eval          
2. Metric coverage          
3. CI/CD integration          
4. Adversarial coverage          
5. Production observability          
6. EU AI Act audit trail          
7. Data residency / self-host          
8. Cost at scale          
Total          

Step 3 — Qualitative Assessment

Scores don’t capture everything. Answer these for each framework before deciding.

Dealbreakers (any Yes = eliminate framework):

Question [Framework A] [Framework B]
Does it send data to a third party by default without opt-out?    
Is it hosted outside the EU when EU data residency is required?    
Does it require a skill set our team doesn’t have and can’t hire?    
Is it abandoned or has it had no commits in 6+ months?    
Does it have a vendor lock-in mechanism that’s unacceptable?    

Positive signals:

Question [Framework A] [Framework B]
Can we get a working eval running in under 2 hours?    
Does the output format fit into our existing CI/reporting stack?    
Is there an active community or commercial support option?    
Does the documentation match what we actually need to do?    

Step 4 — Pilot Evaluation

Before final decision, run a 1-week pilot with the top 1–2 candidates.

Pilot protocol:

  1. Select a representative eval task — choose something you’ll actually need to run in production, not a toy example
  2. Set a time budget — max 4 hours to get a working eval running per framework
  3. Run the eval — document exactly what you ran, what the output was, and what it cost
  4. Score the output — does the result tell you something actionable?
  5. Estimate ongoing cost — extrapolate from pilot to your expected eval frequency and volume

Pilot output template:

Framework: _______
Pilot task: _______
Time to working eval: _____ hours
Blocker(s) encountered: _______
Output format: _____ (useful / adequate / confusing)
Estimated cost per 100 samples at [judge model]: $_____
Estimated monthly cost at [X] eval runs/week: $_____
Team reaction: _______
Would we adopt this? Yes / No / Conditional on _______

Step 5 — Decision Output

Fill in after scoring and pilot.

Selected framework(s): ___

Rationale (2–3 sentences): ___

What we’re NOT using and why: ___

Known gaps we’re accepting: ___

Review trigger: Re-evaluate if any of the following occur:

  • Framework releases a major version with breaking changes
  • Our use case expands significantly (e.g., from RAG to agentic)
  • EU AI Act enforcement creates new audit requirements we can’t satisfy
  • Cost at scale exceeds $__/month
  • A new framework emerges with 5k+ stars that addresses our gaps

Reference: Framework Quick-Score Guide

Use this to calibrate your scores against the frameworks in this guide.

Criterion Strongest framework Notes
Setup time promptfoo, OpenAI Evals YAML-first, no Python required
RAG metric coverage RAGAS Purpose-built retrieval metrics
CI/CD integration DeepEval Native pytest plugin
Adversarial coverage promptfoo OWASP LLM Top 10 + auto-generation
Production observability Langfuse Only production-capable framework
EU AI Act audit trail inspect_ai .eval logs, government-backed
Data residency (EU) Langfuse EU Cloud Frankfurt; self-host option
Cost at scale promptfoo Deterministic assertions are free

→ Full matrix for the complete 6 × 10 comparison.

This template is intentionally framework-agnostic. It works for any eval framework, not just the six covered in this guide.