--- title: "Claude Integration Services: Kensink Labs" description: "Direct Anthropic Claude integration from a senior lab: evals, prompt governance, cost control, vendor-neutral abstraction. Full source ownership." source: "https://www.kensink.com/models/claude/" canonical: "https://www.kensink.com/models/claude/" --- ★ Claude LLM Models 8-week engagement ANTHROPIC CLAUDE · DIRECT INTEGRATION # Direct Claude integration. Eval-gated, vendor-neutral, full source ownership. Anthropic's Claude is strong at long-context reasoning, careful instruction-following, and tool use. We integrate it directly against the API, with evals and a thin abstraction so you are never locked in. LLM API Eval pipelines TypeScript Prompt governance [Start a conversation →](https://www.kensink.com/contact) [All llm models →](https://www.kensink.com/models) Cycle 8 weeks · fixed price Stack Claude API, direct Output Production code + eval suite Handoff Full source ownership \[THE SHORT VERSION\] ## A frontier model with a careful temperament. Claude is particularly good at long documents, structured reasoning, and following nuanced instructions, with strong tool-use support. As with every model, the engineering that matters is around it: prompt design, evals, retries, cost control, and a vendor-neutral abstraction. We integrate directly, no LangChain in the path. When it fits - Long-context tasks: documents, transcripts, large codebases - Agentic tool use and structured, careful reasoning - Workloads where instruction-following quality matters When it does not - Cases where an open-weight model on-prem is mandated - Tasks a much cheaper or smaller model handles just as well \[HOW WE BUILD IT\] ## How we build with Claude. 01 ### Direct API, thin abstraction We call the Claude API directly behind a small provider interface. Swapping to another model is a config change, not a rewrite. 02 ### Prompts as versioned artifacts Prompts are code: version-controlled, reviewed, and tied to the eval suite that measures them. 03 ### Evals before you trust it An eval set that reflects your real tasks. We measure quality and regressions on every prompt or model change. 04 ### Cost, latency, and fallback Token budgets, caching, streaming, and a fallback model path. Observability on every call. \[ WHAT YOU GET \] ## What the engagement leaves behind. Direct No orchestration framework Eval-gated Quality measured, not assumed 1 swap Vendor change is config Observed Every call, cost and latency \[TIERS + VERSIONS\] ## Pick the tier that fits. Fable, Opus, Sonnet, Haiku. We integrate Claude directly behind a vendor-neutral abstraction, then route by task difficulty. Swapping tiers or versions is a config change, not a rewrite. Eval-gated, either way. [ Flagship 4 Jun 2026 ### Claude Fable 5 claude-fable-5 Input $10 / 1M tokens Output $50 / 1M tokens - Anthropic's most powerful model. A new tier that sits above Opus - 1M context, 128K max output. Frontier reasoning and agentic depth - Premium pricing. Route to it only for the hardest, highest-value work Read the technical brief ](https://www.kensink.com/models/claude/fable-5)[ Current 28 May 2026 ### Claude Opus 4.8 claude-opus-4-8 Input $5 / 1M tokens Output $25 / 1M tokens - Around 4× less likely than 4.7 to let flaws pass in code it writes - Dynamic workflows: hundreds of parallel subagents in one Claude Code session - Same standard pricing as 4.7. Fast mode is cheaper than before Read the technical brief ](https://www.kensink.com/models/claude/opus-4-8) Previous Q1 2026 ### Claude Opus 4.7 claude-opus-4-7 Input $5 / 1M tokens Output $25 / 1M tokens - Solid production baseline. Still supported in the API - Strong long-context reasoning and tool use - Move to 4.8 is a config change behind our vendor-neutral abstraction Still supported · brief not yet published Current Q1 2026 ### Claude Sonnet 4.6 claude-sonnet-4-6 Input $3 / 1M tokens Output $15 / 1M tokens - The best balance of speed and intelligence in the family - Near-frontier on routine work at a fraction of Opus cost - Our default for high-volume steps. We route the hard ones to Opus Still supported · brief not yet published Current Oct 2025 ### Claude Haiku 4.5 claude-haiku-4-5 Input $1 / 1M tokens Output $5 / 1M tokens - Fastest and most cost-effective tier for simple, high-volume tasks - 200K context. Right for classification, extraction, and routing - We use it for the cheap steps inside a larger agentic workflow Still supported · brief not yet published \[METHODOLOGY · K-FRAMEWORK\] ## Integrated through the K-Framework. Every model we integrate runs through the same operating system. Three pillars, sixteen layers, one Compound Growth Loop. The methodology that keeps AI work from rotting after the first ship. [Read the K-Framework](https://www.kensink.com/k-framework) 01 ### Foundations Direct API integration with the model. No LangChain, no orchestration vendor, no agent framework built on quicksand. Typed contracts, the same way we wire up Postgres. 02 ### Amplification An eval suite built from your real tasks gates every prompt and model change. Quality is measured before it ships, not vibed in a demo. 03 ### Judgment Governance, audit, and oversight wired in from day one. Who called what, with which prompt version, at what cost. Your auditors get answers, not screenshots. \[OBSERVABILITY\] ## Observability your team can read. A model in production without observability is roulette. We instrument every integration so engineering and finance can see the same numbers, and so a regression at 3am surfaces before a customer opens a ticket. Instrumented ### Cost per call Tokens in, tokens out, dollars spent. Sliced by feature, tenant, and route. Budgets enforced where it matters. Instrumented ### Latency p50 / p95 / p99 Real distributions, not averages. We know which routes are slow, and why. Instrumented ### Eval pass rates The same eval suite that gates a release runs continuously in production. A regression on real traffic surfaces fast. Instrumented ### Prompt + completion logs PII scrubbed at the proxy, shipped to your SIEM. Retention controls match your compliance window. Dashboards your team owns, not ours. At handoff you get the queries, the alerts, and the runbook. We are not in the path to read your metrics. \[COMMON QUESTIONS\] ## Questions we get asked. Claude or GPT? It depends on the task. We pick per workload using an eval set built from your real inputs, and the abstraction lets you run both or switch later. Long-context and careful tool use often favor Claude; we still measure rather than assume. Do you use LangChain? No. We integrate against the model API directly, the same way we integrate against Postgres. Frameworks add abstraction and breakage we do not need for production reliability. Share[](https://twitter.com/intent/tweet?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fclaude%2F&text=Claude%20%C2%B7%20LLM%20Models)[](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fwww.kensink.com%2Fmodels%2Fclaude%2F) [View .md](https://www.kensink.com/models/claude.md) \[RELATED\] ## Worth a look next. [ LLM Models OpenAI GPT Read more ](https://www.kensink.com/models/openai-gpt)[ Design Patterns RAG Read more ](https://www.kensink.com/llm/rag)[ Design Patterns Multi-agent systems Read more ](https://www.kensink.com/llm/agents) APPLIED K-FRAMEWORK ## Bring the problem. We’ll bring the build. Senior engineers, eval suite at handoff, full source ownership. Sprint, program, or ongoing. We shape the engagement to the work. [Start a conversation →](https://www.kensink.com/contact) [Read the K-Framework](https://www.kensink.com/k-framework)