How does per-tenant cost gating change the context-engineering policy in a multi-tenant product?

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Different tenants get different effective budgets. The free tier might get a 4k working budget with a cheap model; the enterprise tier might get 32k with a top-tier model. Context engineering is parameterised by tenant: top-k size, summary depth, scratchpad retention, and whether expensive features like reranking or query expansion are enabled. The system prompt and persona stay the same so behavior is consistent, but the dynamic slots scale with the tenant's plan. Without this discipline, either the free tier costs more than it earns, or the enterprise tier under-uses the budget it paid for.

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Quick reference

TL;DR

Context engineering becomes parameterised by tenant: same persona and system prompt, but top-k, summary depth, rerank, and model class scale with the plan so the free tier is profitable and the enterprise tier uses

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Easy to grasp

Think of an all you can eat restaurant that has two memberships. The basic membership gets a small plate, no dessert station, and house-brand soft drinks. The premium membership gets a big plate, the dessert station, and the imported drinks. The kitchen is the same. The recipes are the same. The waiter is the same. What changes is how much each member can put on the plate and which stations they can visit. If you gave everyone the big plate the restaurant would lose money on the basic members. If you gave everyone the small plate the premium members would be paying for things they cannot use. The agent works the same way. The persona is the kitchen. The plate size and the dessert station are the tenant-tuned context knobs.

Key concepts

Context engineering LLM frameworks

Quick scan · 2–4 min

Everything important, quickly.

4 minutes: name what stays constant (persona, schema, safety) vs what scales (top-k, rerank, summary, model, budget), give an envelope for each of three tiers, and call out cost per user as the monitoring metric.

Side-by-side

Knob	Free tier	Pro tier	Enterprise tier
Working context budget	4-8k tokens	16-24k tokens	32-64k tokens
Top-k retrieval	3-5	8-12	15-25
Reranker	Off	Local / cheap	Cohere Rerank v3 / Voyage Rerank-2
Summary depth	Single rolling	2-level	3-level + semantic
Model class	GPT-5-mini / Haiku 4	GPT-5.5 / Sonnet 4.6	GPT-5.5 Pro / Opus 4.7
Persistent memory	Session-only	Week-long	Persistent with admin controls

Real-world examples

Real products, models, and research that use this idea.

Notion AI Q&A's free preview uses a smaller retrieval window and cheaper model than the Business and Enterprise tiers, with the same persona and output style.
GitHub Copilot Business gives access to Claude Sonnet 4.6 and GPT-5.5 with longer context than the Individual plan, with Enterprise unlocking Claude Opus 4.7 and proprietary code retrieval.
Linear's AI features gate their RAG over team docs at the paid plan level; the free plan gets the agent without internal retrieval enrichment.
Glean and Hebbia (enterprise search) parameterise rerank depth and result count by tier; large enterprise contracts unlock Cohere Rerank v3 or Voyage Rerank-2 on top of base retrieval.
OpenAI ChatGPT Free vs Plus vs Pro vs Business in 2026 is a tier ladder: model class (GPT-5-mini / 5.5 / 5.5-Pro), memory depth, and tool budget all scale with the plan.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QHow do you keep the free tier's quality high enough to drive conversions while staying profitable?

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QWhat is the failure mode of letting tier knobs be exposed as user-facing toggles?

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QHow does prompt caching change the cost gating math?

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References

Primary sources. Skim if you want the original framing.