AI Knowledge Stack — Quick Reference
Last updated: April 2026. Pricing and ratings current as of this date.
What the knowledge layer does
Gives AI agents and reps access to institutional knowledge (call transcripts, playbooks, CRM data, documented processes) rather than generic LLM output. Four components: ingestion → chunking + indexing → retrieval → delivery.
US Stack
Buy path
| Use case |
Tool |
Price |
| Enterprise knowledge search |
Glean (best-in-class) |
$50+/user/mo, 100-seat min |
| Revenue team KM |
Guru |
$25/seat/mo |
| Team knowledge base |
Notion AI Agents |
$12-27/user/mo |
| Agent platform |
Dust.tt |
Custom |
Build path (custom RAG)
| Component |
Tool |
Price |
| Orchestration |
LlamaIndex (retrieval) or LangChain (agents) |
Free |
| Vector DB |
Pinecone |
$25-500/mo |
| Embeddings |
OpenAI text-embedding-3-small |
$0.02/1M tokens |
| Reranking |
Cohere Rerank |
$50-200/mo |
| Total: $900-2,000/mo + 2-4 week build |
|
|
EU Stack (GDPR-first)
Buy path
| Use case |
Tool |
Data residency |
| AI platform + knowledge |
Langdock |
EU-hosted, GDPR-native |
| M365 shops |
Microsoft Copilot + SharePoint |
EU DC available |
| Team KB with DPA |
Slite or Notion + EU DPA |
US-hosted, DPA covered |
Build path (EU sovereign)
| Component |
Tool |
Data residency |
| Orchestration |
LlamaIndex |
Self-hosted EU |
| Vector DB |
Qdrant Cloud EU |
EU cloud |
| Embeddings |
Mistral (EU) or local all-MiniLM |
EU-native or on-premise |
| LLM |
Mistral Large or Claude via Langdock |
EU |
| Total: €800-1,500/mo + 2-4 week build |
|
|
Decision tree
GDPR personal data involved?
├── No → US stack fine
├── Yes + unregulated → US tools with EU DPA (or Langdock)
└── Yes + regulated OR works council → EU sovereign stack
Stage guide
| ARR |
US |
EU |
| €1-5M |
Notion AI |
Notion + EU DPA |
| €5-15M |
Guru or Notion AI Agents |
Langdock |
| €15-50M |
Glean or custom RAG (Pinecone) |
Custom RAG (Qdrant EU) |
| €50-100M |
Glean + custom RAG |
Copilot EU + custom RAG |
| €100M+ |
Glean Enterprise |
Aleph Alpha or Copilot EU |
Top vendor ratings (G2 / Capterra)
| Vendor |
Rating |
Reviews |
Note |
| Glean |
4.8/5 |
130+ |
Gartner eMQ Emerging Leader |
| Guru |
4.8/5 |
624 (Capterra) |
#1 KM satisfaction |
| Dust.tt |
4.9/5 |
19 |
Small sample, very positive |
| Pinecone |
4.6/5 |
39 |
#1 vector DB on G2 |
| Weaviate |
4.8/5 |
30 |
Best knowledge graphs |
| Qdrant |
~12 reviews |
— |
EU sovereign option |
| ChromaDB |
Limited |
— |
Prototype only, not production |
| Mem.ai |
1/5 |
2 |
Avoid |
Critical insight
Chunking quality > embedding model choice. Semantic chunking: 0.79-0.82 faithfulness. Naive chunking: 0.47-0.51. A 60% improvement. Design chunking first, pick tools second.
Full vault docs
- Full AI knowledge stack guide (US/EU reference)
- 14-platform landscape analysis (2025-2026)
- G2/Capterra review data (April 2026)