How to use this guide: Work through each decision tree based on what you're trying to choose. Answer the questions honestly based on your requirements, not what you think you "should" want. There are no wrong answers—just different use cases.
Decision Tree: Choosing Your LLM Platform
Start: What's your primary constraint?
Cost is my biggest concern
Go to Question 2
I need specific compliance (HIPAA, data residency)
Go to Question 3
I'm already on Azure or AWS infrastructure
Go to Question 4
I want the best performance/quality regardless of price
Go to Question 5
Question 2: Cost-focused selection
What's your expected monthly volume?
Low volume (<100K tokens/day)
→ Recommendation: GPT-3.5 Turbo or Claude Haiku
Both are cost-effective for low volumes. GPT-3.5 has broader tooling support.
Medium volume (100K-1M tokens/day)
→ Recommendation: GPT-4o or Claude Sonnet 3.5
Best balance of cost and performance. GPT-4o is slightly cheaper, Claude has longer context.
High volume (>1M tokens/day)
→ Recommendation: Azure OpenAI or AWS Bedrock
Enterprise platforms offer volume discounts and better cost management tools.
Question 3: Compliance requirements
What compliance do you need?
HIPAA compliance required
→ Recommendation: Azure OpenAI, AWS Bedrock, or Claude (Enterprise)
All offer BAAs. Azure if you use Microsoft, AWS if you're on AWS, Claude for best model quality.
Data must stay in specific region (EU, etc.)
→ Recommendation: Azure OpenAI or AWS Bedrock
Both offer regional deployment. Azure has more regions, AWS has better AWS integration.
Standard SOC 2 / enterprise security
→ All platforms are acceptable
Choose based on other factors (cost, features, existing infrastructure).
Question 4: Existing infrastructure
Heavily invested in Microsoft Azure
→ Recommendation: Azure OpenAI Service
Native integration with Azure services, unified billing, Azure AD authentication.
Running everything on AWS
→ Recommendation: AWS Bedrock
Deep AWS integration, multiple model options, unified AWS billing and IAM.
Question 5: Performance-focused selection
What's your primary use case?
Long document analysis (research papers, legal contracts)
→ Recommendation: Claude 3.5 Sonnet or Claude 3 Opus
200K context window, excellent at long-form reasoning and document comprehension.
Code generation and technical tasks
→ Recommendation: GPT-4o or Claude 3.5 Sonnet
Both excel at code. GPT-4o is faster, Claude may produce more thoughtful code.
Creative writing and content generation
→ Recommendation: Claude 3 Opus
Best creative writing quality, more nuanced and stylistically varied outputs.
Fast, high-volume customer support or classification
→ Recommendation: GPT-4o
Excellent speed, good quality, strong function calling for structured tasks.
Decision Tree: Choosing Your Vector Database
Start: What's most important to you?
Simplicity - I want something that just works
→ Recommendation: Pinecone
Fully managed, great docs, works in 5 minutes. Best for teams that want to focus on features, not infrastructure.
Cost - I need to minimize expenses
Go to Question VDB-2
Control - I need to self-host or have complex requirements
Go to Question VDB-3
Just prototyping / learning
→ Recommendation: ChromaDB
Runs locally, zero setup, perfect for development. Switch to production DB later.
Question VDB-2: Cost optimization
What's your data scale?
Small scale (<1M vectors)
→ Recommendation: Pinecone or Weaviate Cloud
At small scale, managed services are cost-effective. Similar pricing, choose based on features you need.
Medium scale (1M-50M vectors)
→ Recommendation: Self-hosted Weaviate or Qdrant
At this scale, self-hosting saves 50-70% vs. managed. Weaviate if you need features, Qdrant for performance.
Large scale (>50M vectors)
→ Recommendation: Self-hosted Weaviate or pgvector
Self-host is essential at this scale. Weaviate for dedicated vector DB, pgvector if you already use Postgres.
Question VDB-3: Advanced requirements
What do you need?
Hybrid search (vector + keyword)
→ Recommendation: Weaviate
Built-in BM25 + vector search with adjustable weighting. Best hybrid search implementation.
Complex metadata filtering
→ Recommendation: Weaviate
GraphQL API with powerful filtering, nested conditions, and boolean logic.
Multi-tenant SaaS application
→ Recommendation: Weaviate
First-class multi-tenancy with isolated shards per tenant.
Must use Postgres (existing infra)
→ Recommendation: pgvector
Postgres extension for vector search. Convenient if you already use Postgres, but less performant than dedicated DBs.
Maximum performance/throughput
→ Recommendation: Qdrant
Written in Rust, optimized for speed. Best benchmarks for query performance.
Decision Tree: Choosing Your Workflow Automation Platform
Start: Who will build the workflows?
Non-technical users (marketing, sales, operations)
→ Recommendation: Zapier
Easiest interface, most integrations, zero learning curve. Worth the premium for non-tech teams.
Technical team or power users
Go to Question AUTO-2
Question AUTO-2: What's your priority?
Cost efficiency - high volumes
Go to Question AUTO-3
AI integration is primary use case
→ Recommendation: n8n
Best AI/LLM nodes, supports RAG workflows, vector DB integrations. Self-host for unlimited AI operations.
Complex visual workflows, good debugger
→ Recommendation: Make
Best visual flow builder, excellent debugger, good balance of power and usability.
Question AUTO-3: Volume-based selection
How many operations per month?
Low (<10K operations/month)
→ Recommendation: Make or Zapier
At low volumes, cost difference is minimal. Choose based on ease of use vs. features needed.
Medium (10K-100K operations/month)
→ Recommendation: Make
Best value at this range. 5-10x cheaper than Zapier, still fully managed.
High (>100K operations/month)
→ Recommendation: Self-hosted n8n
At high volumes, self-hosting becomes very cost-effective. Run unlimited workflows for server cost (~$20-50/mo).
Special Cases
Need to access internal systems behind firewall
→ Recommendation: Self-hosted n8n
Deploy in your VPC/network to access internal APIs and databases.
Need version control and CI/CD
→ Recommendation: n8n
Workflows are JSON files you can commit to Git and deploy through pipelines.
Need niche SaaS integrations
→ Recommendation: Zapier
5,000+ integrations. If Make/n8n don't have your app, Zapier probably does.
Quick Reference Summary
| Category | Simplest | Best Value | Most Powerful |
|---|---|---|---|
| LLM Platform | ChatGPT API | GPT-4o / Claude Sonnet | Claude Opus / GPT-4 |
| Vector Database | Pinecone | Self-hosted Weaviate | Weaviate (features) / Qdrant (speed) |
| Automation | Zapier | Make | Self-hosted n8n |
| AI Framework | OpenAI SDK | LangChain | LangChain / Custom |