Latest AI posts
Claude 4.7 vs GPT-5 vs Gemini 3: which LLM wins for production SaaS in 2026
Three flagship models, three different strengths. Here's how we pick between Claude 4.7, GPT-5, and Gemini 3 when wiring an LLM into a production SaaS — and the tradeoffs nobody talks about.
From prototype to production: shipping an AI feature that doesn't hallucinate
The prototype impressed the stakeholders. The production version invented a policy number and sent it to a customer. Here's the layered defence we ship now so that doesn't happen.
The real cost of running LLMs at scale: token economics for SaaS founders
What LLMs actually cost at production scale in 2026 — per-model pricing, cache math, batch savings, input/output ratios, and the trap that sinks more SaaS margins than any other.
Vector database showdown 2026: Pinecone vs Weaviate vs pgvector vs Qdrant
Four vector databases, four different sweet spots. Here is how our team picks between Pinecone, Weaviate, pgvector, and Qdrant — with the pricing, latency, and filtering tradeoffs that matter in production.
AI agents in production: the architectural patterns that survived 2025
The 2024–2025 agent hype cycle shipped a lot of demos and very few production systems. Here are the architectural patterns that actually survived — and the failure modes that killed the rest.
Building production RAG systems in 2026: vector DBs, hybrid search, and eval frameworks
Vector DBs, embedding models, hybrid search, rerankers, and evals — the production RAG stack the team actually ships in 2026, with the numbers and pitfalls that shape the decision.
AI observability: logging, tracing, and evals for LLM apps
Production LLM apps fail silently. Here is how our team wires up traces, evals, cost tracking, and drift detection with the tools that actually earned their place in 2026.
How to add AI features to an existing SaaS without burning your runway
Ship AI features that earn their token budget. A pragmatic playbook for bootstrapped SaaS — picking the right first feature, tier-gating usage, capping spend, and designing for graceful failure.
Prompt caching strategies that cut Claude API bills by 70%
Prompt caching is the single biggest cost lever on RAG and agent workloads. Here's the math, the right cache_control placement, and the traps that quietly tank cache hit rates.
Fine-tuning vs RAG vs prompting: a decision framework for 2026
Three tools, three jobs. Here is the framework our team uses to decide when to fine-tune, when to reach for RAG, and when a well-designed prompt is genuinely all the problem needs.