AI Inference Cost Trends 2026: Model Pricing and Token Costs
AI inference costs are falling, but durable savings come from routing, caching, context control, and cost per outcome.
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.