Tech Stack for AI Apps 2026: Constraints to Architectures to Scorecards
There is no "best AI stack" — only the right architecture for your constraints. This guide presents three battle-tested patterns based on scale, budget, and compliance needs.
Choose by Constraint
Low-Ops, Fast Iteration
Budget < $100/mo, team < 3 devs, time-to-market priority
Scale & Performance
1M+ vectors, < 100ms p99 latency, multi-tenant
Compliance & Control
GDPR/HIPAA, data sovereignty, audit requirements
Key Technologies Overview
MVP RAG: Low-Ops, Fast Iteration
When to use: Budget < $100/mo, team < 3 devs, time-to-market priority
Recommended Stack
Advantages
- Single database (no vector DB vendor)
- SQL joins between vectors and business data
- Familiar PostgreSQL tooling
- Low operational overhead
- Predictable costs
Trade-offs
- Performance ceiling at ~1M vectors
- Limited to HNSW/IVFFlat indexes
- No built-in hybrid search
- Self-managed embedding pipeline
Scaling Architectures
Scale & Performance
1M+ vectors, < 100ms p99 latency, multi-tenant
- Sub-50ms query latency at scale
- Built-in hybrid search (sparse + dense)
- Multi-tenancy support
- Additional vendor dependency
- Data sync complexity
- Higher cost at scale
Compliance & Control
GDPR/HIPAA, data sovereignty, audit requirements
- Full data sovereignty
- Audit trail for all LLM calls
- Custom security policies
- Highest operational complexity
- Requires dedicated DevOps
- Longer time to production
Vector Database Comparison
| Feature | pgvector | Weaviate | Pinecone |
|---|---|---|---|
| Deployment | PostgreSQL extension | Docker/K8s/Cloud | Managed only |
| Max Vectors | ~1-5M practical | 1B+ | Unlimited |
| Query Latency | 10-100ms | 5-50ms | 5-50ms |
| Index Types | HNSW, IVFFlat | HNSW | Proprietary |
| Hybrid Search | Manual (tsvector) | Built-in (BM25) | Built-in |
| Filtering | SQL WHERE | GraphQL filters | Metadata filters |
| Multi-tenancy | Schema separation | Native | Namespaces |
| Self-host | Yes | Yes | No |
| Pricing | Free (DB cost) | Free / Cloud | $70+/mo |
Quick Decision Guide
Frequently Asked Questions
Should I start with pgvector or a dedicated vector database?
Start with pgvector if you have < 1M vectors and value simplicity. The ability to JOIN vectors with business data in a single query is powerful for MVPs. Migrate to a dedicated vector DB when you hit performance limits or need features like built-in hybrid search.
LangChain vs LlamaIndex: which orchestration framework?
LangChain has broader ecosystem and more integrations (100k+ GitHub stars). LlamaIndex is more focused on data indexing and complex RAG pipelines. For simple chatbots, either works. For production RAG with multiple data sources, LlamaIndex often provides cleaner abstractions.
Do I need observability for AI apps?
Yes, especially in production. LLM calls are non-deterministic and expensive. Tools like Langfuse or LangSmith help you trace chains, measure latency, track costs, and debug hallucinations. Self-host Langfuse for compliance requirements.
How do I handle embedding costs at scale?
Cache embeddings aggressively (Redis or your vector DB). Use smaller embedding models for less critical use cases (text-embedding-3-small vs large). Batch embedding requests. Consider open-source models (sentence-transformers) for high-volume, non-critical embeddings.
Can I switch vector databases later?
Yes, but plan for it. Store original text alongside vectors. Use an abstraction layer (LangChain/LlamaIndex) that supports multiple backends. The embedding model is the harder lock-in - changing it requires re-embedding all data.
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