Notion AI and Atlassian’s Confluence AI both promise the same thing: ask a question, get an answer from your team’s knowledge base. Behind that promise is a RAG pipeline searching across hundreds of thousands — often millions — of document pages per organization. At enterprise scale, vector search across those document embeddings takes 2–10ms per query. Multiply that across millions of daily “Ask AI” interactions and the retrieval bottleneck quietly becomes the difference between an AI assistant that feels instant and one that feels sluggish. L1 vector caching eliminates this gap for 95% of queries.
The Scale of Enterprise Knowledge
A mid-size company with 5,000 employees typically accumulates 200,000–500,000 pages across Notion workspaces or Confluence spaces. A Fortune 500 enterprise pushes past 2 million. Each page gets chunked into 3–10 segments for embedding, producing 1.5–20 million vector embeddings per organization. When someone clicks “Ask AI” in Notion or types a question in Confluence’s AI search, the system embeds the query and runs a nearest-neighbor search against that entire corpus to find the most relevant document chunks.
The vector search itself is the retrieval step in Retrieval-Augmented Generation. It must happen before the LLM can generate any answer. At scale, this lookup takes 2–10ms per query using hosted vector databases — Pinecone, Weaviate, pgvector, or Elasticsearch’s kNN. That does not sound slow in isolation. But at 500,000 AI queries per day across a large enterprise deployment, those milliseconds compound into infrastructure cost, user-perceived latency, and — most critically — the feeling that the AI assistant is not quite as fast as a Google search.
The Power Law of Document Access
Enterprise knowledge bases follow a steep access distribution. Not every document is created equal. The onboarding guide gets viewed 200 times per month. The Q3 OKRs page gets viewed 500 times during planning season and then drops to near zero. The API documentation gets daily traffic from engineering. The company PTO policy gets searched every Monday morning. This is the power law at work: 5% of documents account for 60–70% of all retrievals, and the top 15% cover 95% of queries.
This distribution is the foundation of the L1 caching strategy. If Notion AI or Confluence AI keeps the embeddings for hot documents — recently accessed pages, frequently referenced policies, trending topics, actively edited projects — in an in-process HNSW index, then 95% of all “Ask AI” queries can be answered from L1 memory at 0.0015ms per lookup instead of 2–10ms from the vector database. The long tail — archived projects, old meeting notes, dormant spaces — stays in the vector DB and is fetched on the rare miss.
What This Looks Like for Notion AI
Notion has become the knowledge backbone for thousands of startups and a growing number of enterprises. When a product manager asks Notion AI “What did we decide about the pricing model?”, the system searches across meeting notes, PRDs, Slack imports, and decision logs. That query might touch 3–5 separate collections. Each collection search is a vector lookup.
Today, those 3–5 lookups run against Notion’s vector infrastructure at 2–5ms each. Total retrieval: 6–25ms. With L1 caching, the same lookups complete in 0.0045–0.0075ms total. The LLM begins generating the answer 6–25ms sooner. For streaming responses, the user sees the first token appear measurably faster. The subjective experience shifts from “the AI is thinking” to “the AI already knows.”
Notion’s workspace structure makes cache warming natural. Each workspace has a finite set of active pages. Notion already tracks page views, edits, and mentions. Pages that are actively being edited or frequently referenced get their embeddings promoted to L1 automatically. When a page goes stale — no views in 72 hours — its embeddings are evicted. The cache stays dense with the content people actually search for.
What This Looks Like for Confluence
Confluence operates at a different scale. Atlassian reports that large enterprises run Confluence instances with over 1 million pages. Each page averages 5–8 chunks after embedding segmentation. That is 5–8 million vectors per large deployment. Confluence’s AI assistant — powered by Atlassian Intelligence — searches across this corpus for every question.
Confluence’s access patterns are even more concentrated than Notion’s. Engineering runbooks, HR policies, product specs, and architecture decision records account for the vast majority of AI queries. These documents are stable (policies change quarterly, not daily) with extremely high read frequency. This means cache hit rates above 95% are realistic and the cached embeddings remain valid for extended periods without re-indexing.
| Metric | Vector DB Path | L1 Cache Path | Improvement |
|---|---|---|---|
| Single lookup | 2–10ms | 0.0015ms | 1,333–6,600x |
| 3-lookup query | 6–30ms | 0.0045ms | 1,333–6,600x |
| 500K queries/day | 3,000–15,000s total | 2.25s total | 1,333–6,600x |
| p99 retrieval | 15ms | 0.003ms (L1 hit) | 5,000x |
Memory Footprint and Feasibility
The hot set for a 1-million-page Confluence instance is approximately 150,000 pages (15% coverage, 95% query coverage). At 6 chunks per page and 768-dimensional embeddings in float32, that is 900,000 vectors at 3,072 bytes each: 2.76 GB of RAM. With int8 quantization, it drops to 691 MB. Both Notion and Confluence run substantial backend infrastructure per tenant. Sub-gigabyte RAM for a cache that accelerates 95% of AI queries by three orders of magnitude is a negligible marginal cost.
For Notion’s smaller per-workspace deployments, the numbers are even more favorable. A 50,000-page workspace with a 7,500-page hot set requires approximately 138 MB of quantized vector cache. That fits comfortably in a single container’s memory allocation.
The Consistency Challenge
Knowledge bases are living documents. Pages get edited, reorganized, and deleted. The L1 cache must reflect these changes without serving stale embeddings. The solution is event-driven invalidation. When a Notion page is edited, its chunks are re-embedded and the old L1 entries are replaced. When a Confluence page is published with updates, the same cycle fires. Both platforms already have real-time event systems (Notion’s webhooks, Confluence’s event listeners) that can trigger cache invalidation with sub-second latency.
For the vast majority of enterprise knowledge — policies, documentation, decision records, architecture specs — documents change infrequently. A page edited once per week means its cached embedding is valid for 604,800 seconds between invalidations. The cache-to-write ratio is astronomical. This is the ideal workload for an in-process L1 cache: overwhelmingly read-heavy, with predictable invalidation patterns.
From “Ask AI” to Instant Answer
The end goal for both Notion and Confluence is the same: the user asks a question and the answer appears as fast as they can read it. The LLM generation step is the floor — that takes 500ms–2s regardless. But the retrieval step is entirely within the platform’s control. At 2–10ms, retrieval is a measurable fraction of the total pipeline. At 0.0015ms, it vanishes. The user experience transitions from “AI searched your docs and found this” to “the AI just knew.” That perceptual shift is the difference between an AI feature and an AI product. And it comes down to where the vectors live: across a network hop, or in the same process that serves the query.
Related Reading
- AI Infrastructure Solutions
- Vector Search: In-Process HNSW
- Cachee Pricing
- Start Free Trial
- How Cachee Works
Also Read
Make Knowledge Retrieval Instant.
L1 vector caching delivers sub-millisecond document retrieval for 95% of enterprise AI queries. No infrastructure overhaul required.
Start Free Trial Schedule Demo