Reduce AI Latency in Production: Optimize Token Usage, Caching, and Serving for Faster LLM Performance
Feb 24, 2026
Reduce AI Latency and Optimize Token Usage in Production
If you’re trying to reduce AI latency in production, you’ve probably had the same experience as everyone shipping LLM features: the demo feels snappy, early tests look fine, and then real traffic hits and p95 turns into a horror show. It’s tempting to blame the model or the provider, but most slowdowns come from a more fixable root cause: token volume plus serving and architecture choices.
This guide is a practical playbook to reduce AI latency while you optimize token usage, improve time to first token (TTFT), increase tokens per second (TPS), and bring p95/p99 latency back under control. The focus is on tactics you can apply today across prompts, RAG, caching, routing, and inference.
Before you change anything, define success in numbers
The teams that consistently reduce AI latency track:
TTFT (how fast users see the first token)
Generation speed (TPS)
End-to-end latency (especially p95/p99)
Cost per request (strongly correlated with total tokens)
What Actually Causes AI Latency in Production?
Latency isn’t one number. It’s a chain of small delays, and one expensive step (or a burst of traffic) can dominate the user experience. The good news: once you can see the chain, you can reduce AI latency systematically.
Break latency into a simple budget
A typical LLM request path looks like this:
Client → API gateway → retrieval → prompt build → model prefill → decode → post-processing
Here’s a mini “latency budget” view you can use during triage:
The two model phases matter most:
Prefill: processing the input tokens (strong driver of TTFT)
Decode: generating output tokens (strong driver of total latency)
Token count is a latency multiplier
If you want to reduce AI latency, treat tokens like weight in a backpack. Every extra token increases compute, and input tokens hurt twice: they raise prefill time and can reduce throughput under load.
Common hidden token taxes:
Repeated system prompts across turns
Verbose tool/function schemas included even when unused
Long chat history pasted back every request
RAG over-retrieval (“just in case” context dumps)
Overly wordy outputs when a structured answer would do
Tail latency (p95/p99) is your real UX
Average latency is a feel-good metric. p95/p99 is what users remember.
Tail latency usually comes from:
Queueing during bursts (even if average QPS is fine)
A few requests with extremely long outputs
Retrieval stragglers and reranking spikes
Tool-using agents that loop or branch unpredictably
A system can “improve on average” and still feel worse if the stragglers get slower or more frequent.
Definitions (use these in dashboards)
TTFT (time to first token): time from request start until the first token is streamed back
TPS (tokens per second): generation speed once decoding starts
p95/p99 latency: the slowest 5% / 1% of requests, end-to-end
Prefill vs decode: input processing vs output generation (different optimization levers)
Measure First: The Metrics & Traces You Need
If you can’t explain where time went, you can’t reduce AI latency reliably. You’ll end up guessing: “maybe we need a faster model” or “maybe we need more GPUs.” Instrumentation is cheaper than wrong infrastructure.
Minimum viable observability for LLM endpoints
At a minimum, log these per request:
Input tokens, output tokens, total tokens
TTFT, total latency, TPS
Cache hit/miss (prompt caching, semantic caching, retrieval caching)
Model name and provider, temperature, max_tokens, stop_reason
Tool calls: count, total tool time, failure/retry counts
Add traces (spans) so you can see where p99 comes from:
Retrieval query → vector DB time → rerank time
Prompt assembly time
Model call time (split into TTFT and total)
Post-processing and tool execution
This is also where production governance becomes practical: knowing who ran what, when, and how, and having per-step traces makes it far easier to debug regressions, catch anomalies, and manage risk.
SLOs to set (example targets)
Different product surfaces need different budgets. A single global SLO will either be too strict for heavy jobs or too loose for interactive UX.
Use targets like these as a starting point:
Interactive chat: TTFT < 500–900ms, p95 end-to-end < 3–6s, strict max output tokens
Autocomplete/coding assistant: TTFT < 200–400ms, very high TPS expectations, short outputs
Background summarization: TTFT less important, p95 can be higher, but cost/request must be bounded
Agent workflows: per-step budgets plus hard caps on tool iterations and generation length
Create a benchmark harness
To reduce AI latency without breaking quality, you need repeatable tests:
Build a fixed suite of representative prompts (easy, medium, worst-case)
Replay traffic patterns (bursty load matters more than steady load)
Track latency, tokens, and quality (even lightweight heuristics help)
Add regression gates in CI so a “helpful prompt update” doesn’t double your token count
Token Optimization: Cut Tokens Without Hurting Quality
Token work is the fastest path to reduce AI latency because it shrinks prefill, reduces decode, and improves throughput under load. Done well, it also improves answer quality by removing distracting context.
Token budgeting framework (the hard cap strategy)
Create budgets per route, not per application. The right budget depends on intent.
For each endpoint or workflow step, set:
Input cap: maximum allowed input tokens (truncate, summarize, or refuse)
Output cap: max_tokens tuned to what the UI can actually display
Tool call cap: maximum tool iterations to avoid agent loops
Retrieval cap: maximum number of chunks and maximum context tokens injected
Then enforce it in code. Budgets that are “guidelines” don’t survive traffic.
Use stop sequences and explicit length constraints. Even small changes like “Answer in 5 bullets max” can reduce AI latency materially by preventing rambling.
Prompt slimming techniques (high ROI)
Most production prompts are bloated because they evolved through trial and error. Slimming doesn’t mean dumbing down; it means removing repeated and low-signal text.
Practical prompt compression tactics:
Make the system prompt short and stable; move volatile instructions to the user or developer message
Replace 5–10 examples with 1–2 high-signal exemplars
Don’t include every tool schema in every request; expose only what the route needs
Prefer structured outputs for predictable tasks (JSON-like formats) to avoid long prose
Remove duplicated policy blocks and long “do not” lists that the application already enforces
If you’re integrating tools, schema bloat is often a silent killer. Tool descriptions, parameter docs, and examples can dominate your input tokens unless you actively constrain them.
Conversation history management
Long chat history is one of the most common reasons teams fail to reduce AI latency. It feels safe to include everything, but it’s expensive and often irrelevant.
Use a layered approach:
Sliding window: keep only the last N turns
Summarize older turns into a compact “memory” block
Store durable facts separately (preferences, account info, project constraints)
The goal is to preserve what matters while keeping the prompt short and stable.
RAG token control (the common production leak)
RAG is a frequent source of runaway tokens. Over-retrieval hurts latency twice: retrieval time increases, and the model must prefill a larger prompt.
To keep a tight RAG token budget:
Reduce top-k aggressively, then add reranking instead of injecting more chunks
Chunk smarter: smaller, denser passages beat large blobs of text
Truncate context by token count, not character count
Summarize retrieved content before the final call when documents are long (map-reduce style)
Add “context relevance” filters so you don’t inject near-duplicates or irrelevant passages
Reranking is especially helpful when your vector search is noisy. It’s usually cheaper than passing extra context into the model.
Token Budget Checklist (copy/paste into your runbook)
Set max output tokens for every route (no exceptions)
Add stop sequences for common endpoints
Hard cap conversation history tokens; summarize overflow
Cap RAG context tokens; reduce top-k; rerank
Remove unused tools and shrink tool schemas
Normalize prompts to enable caching later
Caching Strategies That Reduce Latency and Token Spend
Caching reduces AI latency by avoiding repeat work. It also reduces token spend by preventing duplicate generations. The trick is choosing the right cache type for the right workload.
Prompt/response caching (exact match)
This is the simplest win for deterministic routes:
Temperature 0
Stable prompt templates
Stable model versions
Key design matters. A robust key includes:
Normalized prompt template (whitespace, ordering, stable fields)
Parameters (model, temperature, max_tokens, system prompt version)
Tool schema version (if applicable)
Retrieval versioning (if the answer depends on documents)
If you don’t normalize, your hit rate will be terrible even when requests “look the same.”
Semantic caching (similar queries)
Semantic caching uses embeddings and a similarity threshold to reuse prior outputs for near-duplicate requests. It can dramatically reduce AI latency for:
FAQs and support flows
Internal knowledge assistants with repetitive queries
Standard operating procedures and repeated workflows
Practical safeguards:
Use TTLs so stale answers expire
Version cached entries when underlying content changes
Add a confidence gate: only serve semantic-cache answers when similarity is high and intent matches
KV cache / prefix caching in serving stacks
Prefix caching reuses shared prompt prefixes (system prompt, instructions, long policy blocks). It primarily improves TTFT and throughput because prefill becomes cheaper when the prefix is already cached.
This is most effective when:
Many requests share a stable, long prefix
You run your own serving stack or use one that supports prefix/KV caching
Prompts are well-normalized (again, stability drives cacheability)
Retrieval caching
Retrieval can be a bigger bottleneck than the model call, especially with reranking and hybrid search.
Cache:
Query embeddings
Top document IDs for common queries
Reranked results (IDs + scores)
Chunk text fetches from storage
Also consider warming caches for predictable peak periods (weekday mornings, batch report times, shift changes).
Serving & Inference Optimizations (When You Control the Runtime)
Once you’ve reduced tokens and enabled caching, you’ll often find that the next constraint is serving efficiency. This is where throughput and queueing behavior decide p95/p99.
Batching (static vs continuous)
Batching improves throughput by amortizing overhead across requests. But it can hurt per-request latency if you wait too long to form batches.
Two common approaches:
Static batching: build batches at fixed intervals or batch sizes (simple, but can add delay)
Continuous batching: dynamically schedule tokens from different requests per iteration to keep the GPU busy with less waiting
To avoid making tail latency worse, tune:
Maximum queue time (hard cap)
Maximum batch token budget per iteration
Max concurrent sequences
Priority lanes for interactive traffic
Batching is a throughput tool. If your goal is to reduce AI latency for interactive endpoints, keep a tight queue limit and watch p99 carefully.
Streaming and partial rendering
Streaming doesn’t always reduce total latency, but it reduces perceived latency by shrinking the time users wait in silence. If your TTFT is healthy, streaming can make a 6-second response feel like 2 seconds.
UX patterns that work well:
Skeleton answer first, details later
Progressive sections (“Summary” → “Details” → “Next steps”)
Defer heavy post-processing until after the first chunk is streamed
Server considerations:
Flush strategy (don’t buffer too long)
Backpressure handling
Timeouts for slow clients
Clear stop conditions so the model doesn’t drift
Speculative decoding (where it fits)
Speculative decoding uses a small draft model to propose tokens and a larger target model to verify them. When it works well, it can increase TPS and reduce AI latency for high-volume completion patterns.
Best fit:
Low-risk completions with predictable style
Large traffic volumes where even small speedups matter
Well-instrumented systems with quality checks
Quantization, smaller models, distillation
Switching to a smaller or quantized model can be the biggest single lever when you have strict latency targets, but it’s also the easiest way to degrade quality subtly.
Make it safe:
Run A/B tests with a fixed eval set
Track quality metrics plus human spot checks
Apply model routing so only simple requests use the smaller model
Architectural Patterns for Production Latency (Beyond the Model Call)
A lot of efforts to reduce AI latency fail because they focus on the model while ignoring everything around it: routing, network placement, tool execution, and control loops.
Model routing (tiering) to meet latency budgets
Different models excel at different tasks. Some are better at broad reasoning, others are optimized for reliability and safety, and smaller local models can be cost-efficient for high-volume workloads. A practical production strategy is to treat models as interchangeable components and route by need.
A proven pattern is the model cascade:
Try a fast, cheap model first for straightforward requests
Escalate to a larger model only when confidence is low or complexity is high
Optionally add a safety-focused model for sensitive workflows
Routing signals can include:
User tier or endpoint
Intent classification
Retrieval confidence (did RAG find strong context?)
Output risk level
Prior failure modes (fallback after refusal or tool errors)
This approach reduces AI latency for the majority of requests without sacrificing quality for the hard ones.
Reduce network overhead
Network decisions show up as TTFT pain. Common fixes:
Co-locate retrieval and inference in the same region
Use connection pooling and keep-alive
Avoid extra hops through multiple gateways unless you need them
Keep payloads small (don’t ship huge contexts between services)
When you’re chasing p99, shaving 200–400ms of network overhead is often easier than squeezing 5% more TPS from inference.
Guardrails that reduce cost (not increase it)
Guardrails can either bloat prompts and slow everything down, or they can reduce AI latency by preventing pathological requests.
Good guardrails:
Input validation to reject huge or adversarial prompts early
Output constraints that stop the model from rambling
Tool-use limits to prevent loops and runaway workflows
Time budgets per tool and per step, with graceful degradation
When “don’t call the LLM” is the best optimization
The fastest model call is the one you never make.
Great candidates for deterministic alternatives:
Static templates for known intents (password resets, status updates)
Classic search/FAQ responses
Cached summaries and precomputed answers (especially for reports and dashboards)
Treat “LLM optionality” as a feature, not a failure.
A Practical Playbook: Optimize in This Order (90-Minute Triage)
When you’re under pressure, you need an order of operations that reliably reduces AI latency quickly.
Step-by-step sequence
Add per-request logging for tokens + TTFT + p95/p99
Cap output tokens; add stop sequences
Slim prompt templates (remove repetition and unused tools)
Fix RAG: reduce top-k, rerank, and truncate context by token budget
Turn on caching (exact match first, then semantic caching)
Add streaming to improve perceived latency
Add batching or serving upgrades with strict queue limits
Introduce model routing, speculative decoding, or quantization where appropriate
This ordering works because early steps reduce tokens and variability, which makes later infrastructure improvements more effective and safer.
Before/after template (use in sprint notes)
Change:
Effort (S/M/L):
Risk (Low/Med/High):
Expected latency impact (TTFT / p95 / p99):
Expected token impact (input/output/total):
Rollback plan:
Common Mistakes That Keep Latency High
A lot of teams do “optimization work” and still don’t reduce AI latency because of a few predictable mistakes:
Measuring only average latency instead of p95/p99
Shipping without output caps, leading to runaway generations
Over-retrieval in RAG and injecting irrelevant context
Caching without prompt normalization, leading to low hit rates
Batching without SLO-aware queue limits, making tail latency worse
Optimizing inference while retrieval or network overhead is the real bottleneck
If you fix just the output cap and RAG context budget, you’ll often see immediate p95 improvements.
Tooling & Stack Suggestions (Non-Prescriptive)
What to look for in a production LLM gateway/serving layer
When evaluating tooling to reduce AI latency in production, focus on capabilities that keep token and latency decisions observable and enforceable:
Token accounting by route, user, and workflow step
Rate limiting, retries, and fallbacks that don’t amplify p99
Caching primitives: prompt caching and semantic caching
Observability hooks (OpenTelemetry-compatible tracing)
Routing across multiple model providers (model cascade support)
Controls for tool execution (limits, timeouts, logging)
Where StackAI can fit (light mention)
For teams building agentic workflows that combine LLM calls, retrieval, and tool execution, StackAI can be a practical option to prototype and productionize workflows with controls around models, tools, and deployment. In production environments, the ability to swap models per task and keep detailed workflow traces helps prevent vendor lock-in and makes performance and governance improvements easier to sustain as models and pricing change.
Conclusion
To reduce AI latency in production, stop treating it like a mystery inside the model. Latency is usually a token economics problem plus a system design problem. The fastest wins come from measuring correctly, enforcing hard caps, and removing variability.
If you take one plan into your next on-call week, make it this: measure → cap tokens → slim prompts → fix RAG → cache → stream → batch/route.
Book a StackAI demo: https://www.stack-ai.com/demo
