A brutalist approach to LLM experimentation. Compare models side-by-side, track every prompt and response, optimize costs in real-time, and build reproducible research pipelines. No abstraction, no magic – just raw experimental power.
A systematic approach to LLM experimentation that scales from weekend projects to production research pipelines.
Run the same prompt across multiple models simultaneously. Capture responses, latency, token usage, and costs in a unified view. Side-by-side comparison reveals which model excels at which task. Stop guessing – start measuring. Export results as structured data for analysis.
Every prompt variation is logged with full context. Track which prompt version produced which result. Reproduce any experiment from its prompt configuration.
Watch costs accumulate as experiments run. Set budgets per experiment. Identify expensive prompt patterns before they drain your API credits.
Real-world performance data from our experiment repository. Updated weekly based on 1000+ test prompts.
| Model | Reasoning | Creative | Code | Cost/1K tokens |
|---|---|---|---|---|
| GPT-4-Turbo | ★★★★★ | ★★★★☆ | ★★★★★ | $0.01 / $0.03 |
| Claude 3 Opus | ★★★★★ | ★★★★★ | ★★★★☆ | $0.015 / $0.075 |
| Gemini Pro | ★★★★☆ | ★★★★☆ | ★★★★☆ | $0.00025 / $0.0005 |
| Claude 3 Sonnet | ★★★★☆ | ★★★★☆ | ★★★★☆ | $0.003 / $0.015 |
| GPT-3.5-Turbo | ★★★☆☆ | ★★★☆☆ | ★★★★☆ | $0.0005 / $0.0015 |
| Mixtral 8x7B | ★★★★☆ | ★★★★☆ | ★★★☆☆ | $0.0006 / $0.0006 |
From hypothesis to conclusion in 60 minutes or less.
Define your hypothesis. What do you want to test? Which models will you compare? What metrics matter? Configure your experiment parameters in a structured YAML file that lives alongside your code.
Run prompts across selected models. Every request, response, token count, latency measurement, and cost is logged automatically. No manual tracking required.
Visualize differences in model outputs. Export data to Jupyter for deeper analysis. Share findings with your team. Archive experiments for future reference.
See where your API budget goes. Optimize spend without sacrificing quality.
Monthly API costs for a mid-size research project. Repeated prompts, inefficient model selection, and zero visibility into spend patterns.
Same project with intelligent caching, automatic model routing, and real-time cost visibility. 66% reduction through optimization.
Resources freed for more experiments, larger datasets, or longer research cycles. Good experimentation shouldn't require a corporate budget.
What researchers are building with experimental API gateways.
Systematically test prompt variations across models. Discover which prompting strategies generalize and which are model-specific. Build a knowledge base of effective prompt patterns.
Use large model outputs to train smaller models. Compare teacher-student relationships across model families. Reduce inference costs while preserving quality.
Test how different models perform on domain-specific tasks. Identify which models excel at medical, legal, technical, or creative writing. Build domain-specific leaderboards.
Compare RAG implementations across models. Test different chunking strategies, retrieval methods, and context window utilizations. Optimize your RAG pipeline systematically.
Probe models with adversarial inputs. Compare safety guardrails across providers. Document failure modes and build robustness into your applications.
Cost-effective personal API management with self-hosted solutions and privacy-focused approaches for individual developers.
Build weekend projects with AI using practical implementation guides, budget breakdowns, and rapid prototyping strategies.
Transform experiments into production-ready SaaS products with scalable infrastructure and enterprise features.
Enterprise-grade API gateway solutions for B2B applications with advanced security, analytics, and compliance features.