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.
Performance
Definition
Performance coverage in this archive spans 10 posts from Aug 2017 to Mar 2024 and deals with structural tradeoffs: coupling, failure boundaries, and long-term change cost. The strongest adjacent threads are go, backend, and optimization. Recurring title motifs include go, caching, part, and postgresql.
Working claims
- Most pieces recommend choosing the simplest architecture that can be operated confidently.
- Early posts lean on go and stop, while newer posts lean on go and caching as constraints shifted.
- This topic repeatedly intersects with go, backend, and optimization, so design choices here rarely stand alone.
How to apply this
- 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 go and backend before committing implementation details.
Where teams get burned
- 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 2024 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): LLM Prompt Caching in Go: Cut Costs Without Breaking Things
- Then read (operating middle): Your Load Tests Are Lying to You
- Finish with (foundational context): Stop Guessing: How I Fix Slow Databases
Related posts
- LLM Prompt Caching in Go: Cut Costs Without Breaking Things
- Caching: The Easy Part Is Adding It, the Hard Part Is Everything Else
- PostgreSQL Performance: Measure First, Tune Second
- Rust for Cloud Services: A Go Developer’s Honest Take
- eBPF Is Interesting. I Am Not Sold Yet.
- Your Load Tests Are Lying to You
- The PostgreSQL Tuning Playbook I Actually Use
- Making Go Services Fast: What Actually Matters
References
Caching: The Easy Part Is Adding It, the Hard Part Is Everything Else
Cache-aside, write-through, invalidation strategies, and the failure modes that will wake you up at night. With Go examples.
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.
Rust for Cloud Services: A Go Developer's Honest Take
I write Go for a living. Rust is not replacing it. But I have to be honest about where Rust wins.
eBPF Is Interesting. I Am Not Sold Yet.
eBPF promises kernel-level observability without the pain of kernel modules. The tech is real. The hype-to-adoption ratio concerns me.
Your Load Tests Are Lying to You
Most load tests produce comforting numbers instead of useful answers. Here's what I learned the hard way about getting honest results.
The PostgreSQL Tuning Playbook I Actually Use
Battle-tested PostgreSQL tuning from running fintech and startup workloads: connection pooling, memory sizing, index discipline, vacuum management, and the queries that tell you what's broken.
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.
A Go Developer Looks at Rust for Backend Work
I write Go every day at the fintech startup. Here's why I've been spending evenings with Rust, what impressed me, and where it still hurts.
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.