TPToolpazar

Global Araç

Ai Tool Evaluation Scorecard

Gizlilik + veri işleme

Verileriniz üzerinde eğitim yapıyor mu? Veriler nerede saklanıyor? Başka kimler erişebilir?

ağırlık × 3
Kabul edilebilir

Entegrasyon maliyeti

Mevcut altyapınıza bağlamak için tahmini mühendislik saati

ağırlık × 2
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12 aylık TCO

Lisans + koltuk başı + çağrı başı + operasyon ücretleri tam yıl boyunca

ağırlık × 2
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Çıktı kalitesi (sizin testlerinizde)

Kendi gerçek verilerinizle çalıştırın — satıcı demolarıyla değil

ağırlık × 3
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Satıcı istikrarı

Fonlama aşaması, nakit süresi, müşteri sayısı, son işten çıkarmalar (Crunchbase + LinkedIn)

ağırlık × 2
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Uyumluluk uyumu

SOC 2, HIPAA, GDPR, gerçekten ihtiyacınız olan sektöre özel sertifikalar

ağırlık × 2
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Geçiş maliyeti

Veri dışa aktarma formatı, sözleşme kilitlenmesi, satıcı kaybolursa istem taşınabilirliği

ağırlık × 1
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Puan

60 / 100 (45 / 75 ağırlıklı puan)

Taahhüt etmeden önce pilot yapın

Ağırlıklar, AI alıcı anketlerinde satın alma sonrası pişmanlıkta her faktörün ne sıklıkta ortaya çıktığını yansıtır. Bağlamınıza göre zihinsel olarak ayarlayın — ağır düzenlemeye tabi sektörler uyumluluk + gizliliği daha yüksek ağırlıklandırır; mühendislik açısından hafif ekipler entegrasyon maliyetini daha yüksek ağırlıklandırır.

Score any AI vendor across 7 weighted criteria — privacy, integration cost, recurring cost, output quality, vendor stability, compliance fit, switching cost. AI tooling decisions are increasingly real budget decisions for individuals and teams.

What looks expensive at low scale (frontier model API) becomes cheap at very high scale via batch APIs and prompt caching. The gap between “rough estimate” and “defensible number” is exactly where good tooling earns its keep — the math is reproducible, but knowing which inputs matter and what the result means is half the work.

Output tokens cost 3-5x more than input across all major vendors — constrain max_tokens and ask models to be terse. A common pitfall: rolling out frontier models when mid-tier would suffice. Treat the tool’s output as a starting point and validate against authoritative sources for any consequential decision.

Nasıl Kullanılır

  1. Open the tool and review the interface.
  2. Enter or paste your input.
  3. Configure any relevant options.
  4. Run the tool and review the output.
  5. Iterate or refine based on the result.

Ne Zaman Kullanılır

  • Vendor selection between OpenAI, Anthropic, Google, and open-source.
  • Pre-launch budget planning for an LLM-powered feature.
  • Comparing API costs vs self-hosting for high-volume workloads.
  • Production cost forecasting based on traffic projections.

Ne Zaman Kullanılmaz

  • When you have negotiated enterprise pricing not reflected in public rate cards.
  • For hyper-bursty traffic where peak load determines architecture, not average.
  • When the workload is unique enough that public benchmarks don’t apply.
  • For non-frontier image, video, or audio model pricing (those use per-asset billing).

Yaygın Kullanım Senaryoları

  • A researchers comparing model quality working through ai tool evaluation scorecard for a real decision.
  • A enterprise teams managing AI budgets working through ai tool evaluation scorecard for a real decision.
  • A freelancers using AI in client work working through ai tool evaluation scorecard for a real decision.
  • A product managers scoping AI capabilities working through ai tool evaluation scorecard for a real decision.

Sık Sorulan Sorular

What about prompt caching and batch discounts?

Prompt caching saves 50-90% on cached input tokens (OpenAI: 50%; Anthropic: up to 90% with 5-minute cache). Batch API: 50% off async jobs. Combined, can drop bills 70-80% for cache-friendly workloads.

Is this calculation accurate at scale?

Public-rate-card calculators are accurate within 10-15% for typical workloads. Variance comes from prompt-cache hit rates, batch-API usage, and rate-limit retry overhead.

How does this compare to GPT-4o or Claude Opus 4?

GPT-4o, Claude Opus 4, and Gemini 2.5 Pro are roughly comparable on quality for general tasks; their pricing differs by 30-50% so test on your specific workload before locking in.

What hidden costs am I missing?

Output tokens (3-5x input cost), rate-limit retry overhead (20-40% extra), failed-request charges, and the engineering time to maintain the integration. Budget 1.5-2x the headline rate.

How does self-hosting change the math?

Self-hosting Llama 3.3 70B on AWS p4d ($32/hr) costs ~$16/M tokens at full utilization. DeepSeek V3 API is $0.30/M tokens. Self-hosting wins only at 1B+ tokens/month consistent.

Should I switch to a smaller model?

Probably yes, after testing. Mini / Haiku tier handles 60-70% of production tasks adequately at 5-10x lower cost. Test on your specific workload, then route only failures to the larger model.