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How to reduce AI inference costs without sacrificing latency: measure time to first token (TTFT), time between tokens (TBT), throughput, quality and cost on your real request mix before changing anything. Then remove duplicated serving work—continuous batching, prefix-aware routing and caching, chunked prefill and right-sized contexts—before reducing model precision. Quantization, speculative decoding and prefill/decode disaggregation can deliver further gains, but each changes a different resource or quality variable.
There is no universally optimal setting. An interactive assistant, a long-context retrieval service and an offline summarizer stress different parts of the stack. Treat every optimization as a controlled experiment at your production concurrency, prompt lengths, hardware, latency objective and quality threshold.
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Start with a cost-and-latency baseline
Cost and latency are coupled, but they are not the same metric. A change that lowers dollars per token can increase queueing; a faster decoder can leave prefill, cache misses or model loading as the dominant cost. Record both phases of generation and the conditions under which they were measured.
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Prefill processes the input prompt and is commonly compute-bound. Decode generates output tokens sequentially and is often limited by memory bandwidth. TTFT includes queueing and prompt processing until the first output token. TBT measures the interval between subsequent tokens and is the clearest user-facing signal for streaming smoothness. Report p50 and tail values (for example, p95 or p99) for each metric rather than one average.
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- TTFT: time from request admission to the first generated token. Break it into queue time, model execution and any network transfer you can observe.
- TBT: time between generated tokens after the first token. Also report tokens per second as its reciprocal over a defined window.
- Throughput: completed requests, input tokens and output tokens per accelerator-hour under a stated concurrency.
- Cost: dollars per successful request and dollars per output token, including accelerator time, orchestration, storage and network charges that your service actually incurs.
- Quality: task-specific accuracy, groundedness, refusal behavior, format validity or human preference. A cheaper answer that fails the task is not a successful request.
Capture request shape and operating conditions
Log input and output token counts, concurrency, arrival pattern, cache-hit status, model and checkpoint, quantization mode, hardware type, software versions and context limit. Keep prompt-length buckets separate: a 200-token request and a 20,000-token request can have opposite bottlenecks. Record warm and cold starts independently, because compilation and model loading can dominate short tests.
Use a replay or load generator that preserves the distribution of prompt lengths, output lengths and inter-arrival times. A synthetic test with constant short prompts can make batching look excellent while hiding long-context stalls and cache misses.
Improve serving efficiency before changing the model
Serving changes often reduce duplicated work without altering model weights. AWS discusses these techniques and their trade-offs in its model optimization guidance and inference architecture guidance. Validate every setting against your own arrival pattern and service-level objective (SLO).
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Use continuous or in-flight batching
Static batching waits for a group of requests, runs them together and may leave accelerator capacity idle when one sequence finishes early. Continuous batching admits new sequences as others complete, keeping execution slots occupied. This usually improves tokens per second per accelerator and can lower cost at sustained concurrency.
Batch size is a latency-throughput trade-off. Larger batches amortize kernel and scheduling overhead but can make a newly arrived request wait behind existing work and can increase TBT when memory bandwidth is saturated. Smaller batches generally favor interactive latency; larger batches generally favor throughput. Tune the admission policy, maximum sequences and token budget together, then inspect p95 TTFT and TBT rather than relying on average throughput.
Cache repeated prefixes and route for locality
Many requests share a system prompt, policy text, tool definitions or a retrieval preamble. Prefix caching stores the key-value (KV) state produced for that common prefix so later requests do not recompute it. The cache is useful only when the prefix is byte-for-byte compatible with the cached representation and remains resident long enough to be reused.
Random load balancing can defeat the cache by sending each request to a different worker. Context-aware routing steers a request to a worker that already holds its prefix. Measure hit rate, reused-token count, cache memory and eviction rate; a cache with low reuse can consume memory without reducing TTFT. Avoid putting per-request data before a shared prefix, because even a small change can invalidate the reusable segment.
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Protect decoding with chunked prefill
A very long prompt can monopolize compute while existing streams wait for their next token. Chunked prefill divides prompt processing into smaller pieces and interleaves those pieces with decode work. This can reduce TBT spikes and tail latency in mixed workloads, although it may increase total prefill overhead if chunks are too small.
Test chunk size with realistic mixtures of short and long prompts. Examine both the first-token latency of new requests and the token cadence of requests already streaming. A setting that looks good for isolated long prompts may be harmful when traffic is mostly short interactive turns.
Set a context limit you can actually serve
Maximum context length determines memory reservation and the size of each request’s KV cache. Provisioning every worker for a theoretical maximum can lower utilization when production requests are much shorter. Set a limit that covers supported use cases, reject or summarize requests above it explicitly, and monitor the fraction of requests near the ceiling.
Do not silently truncate user or retrieved text to meet a lower limit. If you change the limit, test answer quality and retrieval coverage as well as latency. Long-context traffic may need a separate pool so it cannot consume all memory or queue slots allocated to short requests.
Consider prefill/decode disaggregation at sufficient scale
Prefill is commonly compute-intensive, while decode repeatedly reads model weights and KV state and is often memory-bandwidth-bound. Disaggregating the stages places them on resource pools sized for their different behavior. A prefill worker can process prompts in parallel while decode workers maintain predictable token cadence.
The split introduces transport, scheduling and state-transfer overhead. KV movement across machines consumes network bandwidth and can erase the gain for short prompts. Use it when the workload has enough sustained concurrency and a clear imbalance between prompt processing and generation; otherwise a single pool with good batching is simpler and may be cheaper.
Compile and use an optimized runtime
Compilation and specialized inference runtimes can fuse operations, select hardware-specific kernels and reduce launch overhead. Warmup time, graph-capture constraints, unsupported operators and model updates affect the result. Benchmark steady-state and startup separately, and include the engineering cost of maintaining a compiled artifact.
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Current vLLM documentation lists continuous batching, chunked prefill, prefix caching, quantization, speculative decoding and prefill/decode disaggregation among its capabilities. Exact support varies by release, model, hardware and configuration, so verify compatibility before committing to a deployment design.
Reduce precision without losing the task
Weight quantization
Quantization stores model weights with fewer bits. Smaller weights reduce memory footprint and the bandwidth required to move them during inference; that can increase throughput or let you run a larger model on the same accelerator. Common formats and kernel support differ by model and runtime, so a nominal bit width does not predict real performance.
Evaluate the exact checkpoint and quantization implementation. Compare TTFT, TBT, throughput, memory use and quality on representative prompts. Check structured outputs, tool calls, multilingual inputs and safety behavior if they matter to your application. Keep an unquantized or previously validated fallback so a quality regression can be rolled back quickly.
KV-cache quantization
For long sequences or high concurrency, the KV cache can occupy more memory than the weights. Quantizing that cache reduces memory per active sequence, potentially allowing more simultaneous requests or longer contexts. It can also change the numerical behavior of attention over the generated history.
Google Cloud’s GKE guidance gives an FP8 E5M2 KV-cache configuration and explicitly warns that its example reduces inference accuracy. Treat that as a configuration-specific warning, not a universal result. Test factual accuracy, long-context retrieval, repetition, refusal behavior and any domain score you use before enabling KV quantization in production.
Use speculative decoding only when acceptance pays for the draft
Speculative decoding adds a small draft process that proposes several tokens. The target model verifies those proposals in a parallel step; accepted tokens reduce the number of sequential target-model decode steps. The method is most promising when the draft model is substantially cheaper and its proposals match the target often enough to offset draft computation and memory traffic.
Measure acceptance rate by position, draft-model latency, target verification time, total accelerator time and quality. A low acceptance rate turns the draft into overhead. A draft model that competes for the same memory bandwidth can also slow ordinary decoding. Operationally, you must deploy, monitor and update an additional model, and ensure your runtime supports the target architecture and sampling features you use.
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Run a baseline and one change at a time unless you are testing a deliberately combined configuration. Hold model version, tokenizer, sampling policy, hardware class and traffic replay constant. Warm the service before steady-state measurements, then report cold-start behavior separately.
| Lever | Potential benefit | What to verify |
|---|---|---|
| Continuous batching | Higher accelerator utilization and throughput | Queueing, p95/p99 TTFT and TBT at target concurrency |
| Prefix caching and context-aware routing | Less repeated prompt computation | Prefix reuse rate, cache memory, eviction and routing skew |
| Chunked prefill | Fewer decode stalls from long prompts | Token cadence, long-prompt TTFT and overhead from small chunks |
| Context-size limits | Lower memory reservation and predictable scheduling | Rejected or summarized requests and task quality near the limit |
| Weight quantization | Lower weight memory and bandwidth | Kernel support, throughput, latency and task quality |
| KV-cache quantization | More active sequences or longer contexts per worker | Accuracy and long-context behavior under memory pressure |
| Speculative decoding | Fewer sequential target-model steps | Acceptance rate, draft cost, memory use and total cost |
| Compilation or optimized runtime | Lower execution overhead | Warmup, compatibility, rebuild time and steady-state gains |
| Prefill/decode disaggregation | Independent sizing for compute- and bandwidth-heavy phases | KV transfer cost, network saturation and operational complexity |
For each candidate, calculate cost per successful request or output token at the same quality threshold. Include idle capacity and autoscaling behavior; a configuration that is efficient at 90% utilization may be uneconomic at your normal 25% utilization. AWS describes a SageMaker workflow that evaluates latency, throughput and price; use that as a model for controlled comparison, not as a guarantee that a particular setting wins on your hardware.
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- Define the SLO and quality gate. Set separate targets for TTFT, TBT or tokens per second, tail latency and task quality. Decide which failures make a request unsuccessful.
- Build representative traffic. Sample prompt and output lengths, shared-prefix frequency, concurrency bursts and geographic or tenant routing patterns. Remove sensitive content while preserving tokenization and shape.
- Instrument the baseline. Emit queue, prefill, first-token, per-token, completion and accelerator-time measurements. Record cache hits, batch composition and memory utilization.
- Classify the bottleneck. High prompt processing time with low decode occupancy points toward prefill; slow token cadence with high memory traffic points toward decode. Queue growth indicates admission or capacity limits rather than a model-kernel problem.
- Apply one serving change. Start with batching, prefix locality, chunked prefill or context limits that address the observed bottleneck. Keep rollback configuration and a fixed replay.
- Retest at several loads. Measure low, target and peak concurrency, including burst arrivals. A gain at one load is not evidence of a gain across your operating range.
- Test precision changes. Benchmark weight quantization first, then KV-cache quantization if memory is the constraint. Run automated quality suites and human review for high-impact tasks.
- Evaluate speculative decoding last. Add it only after ordinary decode is stable, then compare total cost and latency with and without the draft process at the same output distribution.
- Canary and monitor. Roll out gradually, watch tail latency, cache hit rate, quality alerts, OOM events and cost per successful request, and retain an immediate fallback.
Diagnose common regressions
Throughput rises but TTFT gets worse
The batch or concurrency limit is probably too high for an interactive SLO. Inspect queue time and batch wait time, then lower the admission target or reserve capacity for short requests. Separate long-context traffic if it dominates the queue.
TTFT improves but TBT becomes uneven
Prompt work may be starving active decodes. Reduce prefill chunk size, cap prompt tokens admitted per scheduling cycle or allocate a decode-focused pool. Verify that the change helps p95 TBT, not only the first token.
Prefix caching shows little benefit
Compare the actual serialized prefixes, not their intended templates. Small differences in whitespace, tool schemas, timestamps or tenant instructions can prevent reuse. Check whether routing sends repeat traffic to the same worker and whether eviction occurs before the next request.
Quantization lowers cost but answers degrade
Identify which tasks fail and whether the issue is weight or KV precision. Re-test with a less aggressive format, selectively keep sensitive layers or revert for affected routes. Do not compensate by increasing output length; that can hide the quality problem while raising cost.
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Inspect draft latency and acceptance by token position. If proposals are rarely accepted, choose a closer draft model, shorten the proposal window or disable speculation for that workload. A draft that is too large can consume the memory bandwidth the target decoder needs.
Measured savings disappear on the bill
Check autoscaling floors, replica warmup, storage, network transfer and failed requests. Report cost over the same time window as throughput and include idle periods. Savings in accelerator seconds do not automatically equal savings in total service cost.
Choose an architecture by workload
Interactive chat with short prompts
Prioritize low queue time, small or adaptive batches, fast decode kernels and stable TBT. Prefix caching is valuable when a system prompt is shared. Keep a latency reserve so bursts do not push TTFT into the tail.
Retrieval or agent requests with long shared context
Measure prefill separately, normalize prompt construction to maximize reusable prefixes and use context-aware routing. Chunked prefill can protect ongoing streams. Set explicit context limits and consider a separate pool for unusually long requests.
High-concurrency generation
Continuous batching, KV-cache memory efficiency and throughput per accelerator become central. Quantization may allow more active sequences, but quality testing must include the task’s most sensitive cases. Autoscaling policy should react to queue and token demand, not request count alone.
Offline or relaxed-latency jobs
Larger batches and aggressive utilization can reduce cost when users do not need immediate tokens. TTFT matters less than total completion time and dollars per output token, so a configuration rejected for interactive traffic may be appropriate here.
What the published examples do—and do not—prove
Google Cloud reported a 35% TTFT reduction and doubled cache efficiency in a March 27, 2026 article describing an Inference Gateway case study. The article attributes the result to routing and says it changed neither hardware, model nor cluster size. That is evidence that cache-aware routing can matter in a workload with suitable reuse; it is not an expected percentage for every deployment.
Karl Weinmeister, Director of Developer Relations at Google Cloud, summarized the optimization objective this way: “Every dollar that you spend on model inference buys you a position on a graph of latency and throughput.” The useful implication is methodological: seek the efficient frontier for your workload instead of declaring one setting universally best.
Production checklist
- TTFT and TBT are reported separately with p50 and tail percentiles.
- Prompt and output length distributions, concurrency and arrival bursts match production.
- Cost is expressed per successful request or output token and includes non-accelerator charges.
- Batching limits are tuned against queueing and interactive SLOs.
- Shared prefixes are normalized, cached and routed for locality where reuse exists.
- Chunked prefill and context limits have been tested with mixed prompt sizes.
- Weight and KV quantization have passed task-specific quality gates.
- Speculative decoding has a measured acceptance rate and a rollback switch.
- Runtime, hardware and model-version compatibility are documented.
- Canary monitoring covers latency tails, quality, OOMs, cache behavior and cost.
The lowest-cost configuration is the one that completes acceptable work within your latency target. Measure the bottleneck, remove repeated serving work, then trade precision or add architectural complexity only when the measured gain survives realistic load and quality checks.
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