{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "Slancha Blog",
  "home_page_url": "https://slancha.ai/blog",
  "feed_url": "https://slancha.ai/feed.json",
  "description": "Technical deep dives, tutorials, and insights on AI inference: intelligent routing, automated fine-tuning, inference optimization, and the closed-loop AI pipeline.",
  "icon": "https://slancha.ai/favicon.svg",
  "language": "en-US",
  "items": [
    {
      "id": "https://slancha.ai/blog/reduce-llm-inference-latency",
      "url": "https://slancha.ai/blog/reduce-llm-inference-latency",
      "title": "How to Cut LLM Inference Latency in Half: 8 Production Techniques",
      "summary": "High latency kills AI products. Here are 8 battle-tested techniques to slash LLM inference latency in production (from speculative decoding to intelligent routing) with code examples, benchmark data, and architecture patterns.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "latency",
        "inference",
        "optimization",
        "production",
        "performance"
      ]
    },
    {
      "id": "https://slancha.ai/blog/build-vs-buy-ai-gateway",
      "url": "https://slancha.ai/blog/build-vs-buy-ai-gateway",
      "title": "Build vs Buy: The AI Gateway Decision Framework for Engineering Teams",
      "summary": "Your team needs an AI gateway. Should you build one in-house or use a managed platform? We break down the true cost, timeline, and complexity of both paths, with real code, architecture decisions, and a decision matrix.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "ai-gateway",
        "build-vs-buy",
        "architecture",
        "engineering",
        "decision-framework"
      ]
    },
    {
      "id": "https://slancha.ai/blog/ai-inference-optimization-qat-mig-multi-token",
      "url": "https://slancha.ai/blog/ai-inference-optimization-qat-mig-multi-token",
      "title": "AI Inference Optimization: Complete Guide to QAT, MIG, and Multi-Token Prediction",
      "summary": "Three techniques are reshaping how production AI inference runs: Quantization-Aware Training, Multi-Instance GPU, and Multi-Token Prediction. Here's how each works, when to use them, and how they compound to cut inference costs by 60-75%.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "optimization",
        "QAT",
        "MIG",
        "multi-token-prediction",
        "inference",
        "technical"
      ]
    },
    {
      "id": "https://slancha.ai/blog/zero-config-ai-inference",
      "url": "https://slancha.ai/blog/zero-config-ai-inference",
      "title": "Zero-Config AI Inference: Why the Black Box Wins",
      "summary": "Every AI infrastructure platform gives you more knobs. Slancha took them away. Here's why the black box approach to AI inference consistently outperforms teams with \"full control\", and what the data says about how engineering teams actually manage model selection.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "strategy",
        "black-box",
        "inference",
        "positioning"
      ]
    },
    {
      "id": "https://slancha.ai/blog/introducing-slancha",
      "url": "https://slancha.ai/blog/introducing-slancha",
      "title": "Introducing Slancha: The AI Inference Platform That Gets Better While You Sleep",
      "summary": "Today we are opening early access to Slancha, a BYOK routing layer for AI inference. One OpenAI-compatible API picks the right model for each request. Drop-in via base_url override.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "launch",
        "announcement",
        "platform"
      ]
    },
    {
      "id": "https://slancha.ai/blog/the-case-for-black-box-ai-inference",
      "url": "https://slancha.ai/blog/the-case-for-black-box-ai-inference",
      "title": "The Case for Black Box AI Inference: Why Your Team Should Stop Picking Models",
      "summary": "Every AI platform promises transparency and control. Slancha bets on the opposite: a black box that handles everything. Here's why that's the right call for 90% of teams using LLM APIs.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "philosophy",
        "inference",
        "platform",
        "strategy"
      ]
    },
    {
      "id": "https://slancha.ai/blog/slancha-vs-databricks-ai-infrastructure-comparison",
      "url": "https://slancha.ai/blog/slancha-vs-databricks-ai-infrastructure-comparison",
      "title": "Slancha vs. Databricks: The AI Infrastructure Showdown",
      "summary": "Databricks gives you the tools. Slancha does the work. A detailed comparison of two fundamentally different approaches to AI infrastructure, full control vs. automatic results.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "comparison",
        "infrastructure",
        "enterprise"
      ]
    },
    {
      "id": "https://slancha.ai/blog/from-prototype-to-production-ai-deployment-checklist",
      "url": "https://slancha.ai/blog/from-prototype-to-production-ai-deployment-checklist",
      "title": "From Prototype to Production: The AI Deployment Checklist",
      "summary": "Most AI projects that \"work\" in prototype never make it to production. This checklist covers what actually breaks and how to fix it, routing, data curation, quantization, GPU efficiency, and more.",
      "date_published": "2026-03-30T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "production",
        "deployment",
        "checklist",
        "engineering"
      ]
    },
    {
      "id": "https://slancha.ai/blog/building-a-production-ai-router-architecture-patterns",
      "url": "https://slancha.ai/blog/building-a-production-ai-router-architecture-patterns",
      "title": "Building a Production AI Router: Architecture Patterns That Scale",
      "summary": "Routing requests to the right model is the easy part. The hard part is doing it at scale with sub-millisecond overhead, graceful degradation, and zero downtime deploys. Here are the architecture patterns that make it work.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "router",
        "architecture",
        "infrastructure",
        "engineering"
      ]
    },
    {
      "id": "https://slancha.ai/blog/the-complete-guide-to-ai-model-routing",
      "url": "https://slancha.ai/blog/the-complete-guide-to-ai-model-routing",
      "title": "The Complete Guide to AI Model Routing: Strategies, Architecture, and Cost Optimization",
      "summary": "Not every request needs GPT-4. Learn how intelligent model routing cuts inference costs 40-70% while maintaining quality, with architecture patterns, routing strategies, and real benchmarks.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "router",
        "architecture",
        "cost-optimization",
        "tutorial"
      ]
    },
    {
      "id": "https://slancha.ai/blog/how-eval-data-should-drive-fine-tuning-technical-deep-dive",
      "url": "https://slancha.ai/blog/how-eval-data-should-drive-fine-tuning-technical-deep-dive",
      "title": "How Eval Data Should Drive Fine-Tuning: A Technical Deep Dive",
      "summary": "A hands-on guide to building a closed-loop pipeline where evaluation failures automatically become training examples, with code, architecture patterns, and real metrics.",
      "date_published": "2026-03-30T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "post-training",
        "fine-tuning",
        "engineering",
        "tutorial"
      ]
    },
    {
      "id": "https://slancha.ai/blog/5-signs-your-ml-team-needs-an-evaluation-platform",
      "url": "https://slancha.ai/blog/5-signs-your-ml-team-needs-an-evaluation-platform",
      "title": "5 Signs Your ML Team Needs an Evaluation Platform",
      "summary": "Spreadsheets, vibes-based deployment, and \"it works on my laptop\" are not an eval strategy. Here's how to know you've outgrown ad-hoc testing.",
      "date_published": "2026-03-30T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "evaluation",
        "best-practices",
        "team"
      ]
    },
    {
      "id": "https://slancha.ai/blog/why-eval-data-should-drive-fine-tuning",
      "url": "https://slancha.ai/blog/why-eval-data-should-drive-fine-tuning",
      "title": "Why Eval Data Should Drive Fine-Tuning",
      "summary": "Most teams treat evaluation and fine-tuning as separate workflows. That disconnect is costing you model quality and engineering hours.",
      "date_published": "2026-03-29T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "post-training",
        "evaluation",
        "fine-tuning"
      ]
    },
    {
      "id": "https://slancha.ai/blog/the-real-cost-of-stitching-ai-tools-together",
      "url": "https://slancha.ai/blog/the-real-cost-of-stitching-ai-tools-together",
      "title": "The Real Cost of Stitching AI Tools Together",
      "summary": "You're paying for 4-6 tools that don't talk to each other. The integration tax is higher than you think.",
      "date_published": "2026-03-28T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "platform",
        "infrastructure",
        "cost"
      ]
    },
    {
      "id": "https://slancha.ai/blog/introducing-the-slancha-router",
      "url": "https://slancha.ai/blog/introducing-the-slancha-router",
      "title": "Introducing the Slancha Router: Free Intelligent Model Routing",
      "summary": "Route requests to the best model for the job, automatically. Free to use, no lock-in.",
      "date_published": "2026-03-27T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "router",
        "product",
        "launch"
      ]
    },
    {
      "id": "https://slancha.ai/blog/slancha-vs-openrouter-beyond-the-model-marketplace",
      "url": "https://slancha.ai/blog/slancha-vs-openrouter-beyond-the-model-marketplace",
      "title": "Slancha vs OpenRouter: Beyond the Model Marketplace",
      "summary": "OpenRouter gives you access to every model through one API. Slancha gives you one API that makes model selection irrelevant. A detailed comparison of two fundamentally different approaches to multi-model AI.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "comparison",
        "routing",
        "inference",
        "openrouter"
      ]
    },
    {
      "id": "https://slancha.ai/blog/how-to-reduce-llm-api-costs",
      "url": "https://slancha.ai/blog/how-to-reduce-llm-api-costs",
      "title": "How to Reduce Your LLM API Costs by 60% Without Sacrificing Quality",
      "summary": "LLM API bills are growing faster than usage. Here are five proven techniques (from intelligent routing to automated fine-tuning) that cut costs dramatically while maintaining or improving output quality.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "cost-optimization",
        "inference",
        "routing",
        "fine-tuning",
        "guide"
      ]
    },
    {
      "id": "https://slancha.ai/blog/the-multi-model-future",
      "url": "https://slancha.ai/blog/the-multi-model-future",
      "title": "The Multi-Model Future: Why One LLM Won't Rule Them All",
      "summary": "The era of picking one model and routing everything through it is ending. MoE architectures, task-specific fine-tuning, and intelligent routing are converging on a multi-model future. Here's what that means for your AI stack.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "strategy",
        "architecture",
        "inference",
        "MoE"
      ]
    },
    {
      "id": "https://slancha.ai/blog/fine-tuning-vs-rag-when-to-use-each",
      "url": "https://slancha.ai/blog/fine-tuning-vs-rag-when-to-use-each",
      "title": "Fine-Tuning vs RAG: When to Use Each (And How to Stop Choosing)",
      "summary": "The fine-tuning vs RAG debate is one of the most common questions in production AI. Here's a practical decision framework based on real workloads, plus why the best systems use both, automatically.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "fine-tuning",
        "RAG",
        "architecture",
        "tutorial"
      ]
    },
    {
      "id": "https://slancha.ai/blog/enterprise-ai-inference-buyers-guide-2026",
      "url": "https://slancha.ai/blog/enterprise-ai-inference-buyers-guide-2026",
      "title": "The Enterprise AI Inference Buyer's Guide 2026",
      "summary": "A practical framework for evaluating AI inference vendors, covering latency architecture, cost transparency, security requirements, TCO calculations, and migration playbooks. No fluff, no vendor bias.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "enterprise",
        "buyer-guide",
        "comparison",
        "TCO",
        "security",
        "migration"
      ]
    },
    {
      "id": "https://slancha.ai/blog/how-to-build-a-self-improving-ai-pipeline",
      "url": "https://slancha.ai/blog/how-to-build-a-self-improving-ai-pipeline",
      "title": "How to Build a Self-Improving AI Pipeline (Eval → Fine-Tune → Deploy Loop)",
      "summary": "Most AI pipelines are static: deploy a model, hope it works, manually retrain when it drifts. Here's how to build a pipeline that evaluates, fine-tunes, and redeploys automatically, closing the loop so your models get better with every request.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "pipeline",
        "fine-tuning",
        "evaluation",
        "automation",
        "MLOps",
        "closed-loop"
      ]
    },
    {
      "id": "https://slancha.ai/blog/lora-fine-tuning-guide",
      "url": "https://slancha.ai/blog/lora-fine-tuning-guide",
      "title": "The Complete Guide to LoRA Fine-Tuning: From Data Preparation to Production Deployment",
      "summary": "LoRA has become the default fine-tuning method for production LLMs, but most teams get the implementation wrong. This guide covers adapter architecture, data preparation, hyperparameter tuning, evaluation, quantized variants (QLoRA), and deployment patterns with production benchmarks.",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Engineering"
        }
      ],
      "tags": [
        "LoRA",
        "fine-tuning",
        "QLoRA",
        "production",
        "MLOps",
        "training",
        "adapters"
      ]
    },
    {
      "id": "https://slancha.ai/blog/ai-inference-cost-optimization-cfo-guide",
      "url": "https://slancha.ai/blog/ai-inference-cost-optimization-cfo-guide",
      "title": "AI Inference Cost Optimization: A CFO's Guide to GPU Economics",
      "summary": "Your API bill is the tip of the iceberg. This guide breaks down the real total cost of ownership for AI inference, build vs. buy analysis with concrete numbers, ROI framework for the board, and three real-world scenarios showing what happens when you get optimization right (or wrong).",
      "date_published": "2026-03-31T00:00:00.000Z",
      "authors": [
        {
          "name": "Slancha Team"
        }
      ],
      "tags": [
        "cost-optimization",
        "business",
        "cfo",
        "gpu-economics",
        "build-vs-buy",
        "roi"
      ]
    }
  ]
}