AI Cost Trends: Where We're Headed
A pragmatic look at AI cost trends in early February 2026, plus what to do about them.
Optimization
Definition
Optimization coverage in this archive spans 6 posts from Aug 2017 to Feb 2026 and deals with structural tradeoffs: coupling, failure boundaries, and long-term change cost. The strongest adjacent threads are performance, cost, and ai. Recurring title motifs include ai, cost, go, and trends.
What the archive argues
- Most pieces recommend choosing the simplest architecture that can be operated confidently.
- Early posts lean on stop and guessing, while newer posts lean on ai and cost as constraints shifted.
- This topic repeatedly intersects with performance, cost, and ai, so design choices here rarely stand alone.
Execution checklist
- Define failure domains and data boundaries before introducing additional services or protocols.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read performance and cost before committing implementation details.
Common failure modes
- Breaking systems into many parts without clear ownership of cross-service behavior.
- Choosing architecture for trend alignment rather than workload constraints.
- Applying guidance from 2017 to 2026 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): AI Cost Trends: Where We’re Headed
- Then read (operating middle): PostgreSQL Performance: Measure First, Tune Second
- Finish with (foundational context): Stop Guessing: How I Fix Slow Databases
Related posts
- AI Cost Trends: Where We’re Headed
- AI Cost Benchmarking: What Your Bill Actually Tells You
- LLM Prompt Caching in Go: Cut Costs Without Breaking Things
- PostgreSQL Performance: Measure First, Tune Second
- Making Go Services Fast: What Actually Matters
- Stop Guessing: How I Fix Slow Databases
References
AI Cost Benchmarking: What Your Bill Actually Tells You
Price-per-token is the least useful number on your AI bill. Real cost benchmarking starts with your workload, not a provider's pricing page.
LLM Prompt Caching in Go: Cut Costs Without Breaking Things
Caching LLM responses is the highest-leverage optimization most teams are not doing. Here is how I implement it in Go, with real patterns for keys, invalidation, and safety.
PostgreSQL Performance: Measure First, Tune Second
Most Postgres performance problems are indexing problems. The rest are vacuum problems. Here's how to find and fix both.
Making Go Services Fast: What Actually Matters
Practical patterns for squeezing performance out of Go services — profiling, allocation control, bounded concurrency, and HTTP/DB tuning from real production work.
Stop Guessing: How I Fix Slow Databases
The repeatable process I use at the fintech startup to diagnose and fix database performance problems instead of throwing random indexes at the wall.