launch weekPro / Team is $0 / month $49 — full engine, no card charged.7 days left · ends Aug 20claim launch offer →
[ACE_FLEET // AI_COMPUTE_EFFICIENCY_ENGINE]

Cut Your AI API Bill
in 15 Minutes.

Connect ACE to your existing OpenAI or Anthropic client.
No code rewrite. No credit card.

select_your_stack →
ace_efficiency_engine · projectedlive
→ Every prompt cached, routed, and pruned before it burns a token.
prompt_tokens
−58%
cache_hit_ratio
0.71
avg_latency
18ms
cost_per_1k_calls
$0.34
Try ACE Free →

See exactly how much you can save · Free forever for developers

Already using OpenAI / Anthropic? Change one line:
python
base_url = "https://engine.acefleet.dev/v1"
SOC2 Type IIZero Data RetentionLocal ONNX EmbeddingsSelf-Hosted VPCK8s Operator Native
§ 05 / interactive cost calculator

Estimate your ROI.

Pick a workload profile, drop in your monthly spend, toggle skills. Cascaded waterfall math — no overlapping percentages, no black-box multipliers.

1 · workload_profile
2 · monthly_ai_spend$100,000
$1k$500k+
3 · active_optimization_skills
projected_outputreal-time
monthly_savings
$58,505
58.5% reduction · annualized $702,063
cache router prune net spend
performance_bonus
⚡ 27%est. P95 latency reduction
⚡ Lock in $58,505/mo savings →Zero code refactoring · drop-in proxy in < 15 min
§ 02 / ecosystem coverage

Plugs into your existing tech stack in under 15 minutes.

No infrastructure rebuilds required. ACE operates across every layer — from Groq and CoreWeave silicon up to Bedrock, vLLM, and CrewAI agents.

HWHardware & Accelerators·NVIDIA H100/H200 · Groq LPU · Cerebras WSE-3 · AMD Instinct
ENGInference Engines & Serving·vLLM · SGLang · TensorRT-LLM · TGI
CLDClouds & Orchestration·AWS Bedrock · Azure · GCP · CoreWeave · Kubernetes · Ray
AGTAgent Frameworks·CrewAI · LangChain · LlamaIndex · DSPy
HWSilicon & Accelerators·NVIDIA H100/H200 · Groq LPU · Cerebras WSE-3 · AMD Instinct
NETInterconnect·NVLink · InfiniBand · RoCEv2
CLDNeoclouds & Hyperscalers·CoreWeave · Nebius · Lambda · AWS · GCP · Azure
ORCOrchestration·Kubernetes · Ray · Karpenter
SRVInference Engines·vLLM · SGLang · TensorRT-LLM · TGI
OSSCustom Open-Source Models·Llama · Qwen · DeepSeek · Mistral
ENTEnterprise Platforms·Databricks · Snowflake · Vertex · Bedrock
AGTFrontier APIs & Agents·OpenAI · Anthropic · CrewAI · LangGraph · LlamaIndex · DSPy
HWHardware & Accelerators·NVIDIA H100/H200 · Groq LPU · Cerebras WSE-3 · AMD Instinct
ENGInference Engines & Serving·vLLM · SGLang · TensorRT-LLM · TGI
CLDClouds & Orchestration·AWS Bedrock · Azure · GCP · CoreWeave · Kubernetes · Ray
AGTAgent Frameworks·CrewAI · LangChain · LlamaIndex · DSPy
HWSilicon & Accelerators·NVIDIA H100/H200 · Groq LPU · Cerebras WSE-3 · AMD Instinct
NETInterconnect·NVLink · InfiniBand · RoCEv2
CLDNeoclouds & Hyperscalers·CoreWeave · Nebius · Lambda · AWS · GCP · Azure
ORCOrchestration·Kubernetes · Ray · Karpenter
SRVInference Engines·vLLM · SGLang · TensorRT-LLM · TGI
OSSCustom Open-Source Models·Llama · Qwen · DeepSeek · Mistral
ENTEnterprise Platforms·Databricks · Snowflake · Vertex · Bedrock
AGTFrontier APIs & Agents·OpenAI · Anthropic · CrewAI · LangGraph · LlamaIndex · DSPy
§ 03 / at a glance

One engine. Three deployment surfaces.

Business outcome on the left. Technical mechanics on the right. Same control plane underneath.

~/ace/deployment_matrix.tsv● reference
API & Agentic Apps
50%–70% lower API bills; protects SaaS gross margins.
Semantic caching · intent routing · entropy pruning (LLMLingua) · agent state summarization.
Custom Open-Source
3× token throughput per dollar on self-hosted models.
RadixAttention shared KV caching · Multi-LoRA S-LoRA · speculative decoding · model distillation.
Dedicated GPU Fleets
90%+ GPU VRAM utilization; prevents premature hardware purchases.
Prefill–Decode disaggregation · FP8/FP4 quantization · K8s MPS bin-packing · ASIC kernel offload.
3 rows · 0.02sexpand each deployment ↓
§ 06 / integration

Enterprise-grade. Developer-fast.

see full onboarding doc →

15-Minute Setup. Zero Refactor.

Point your existing OpenAI or Anthropic client at the ACE proxy URL. Semantic caching, intent routing, context pruning, and agent guardrails activate automatically — your SDK calls, streaming, and tool-use stay identical.

  • · OpenAI · Anthropic · Azure OpenAI SDKs supported
  • · Streaming, tool-calling, and vision preserved
  • · Zero-config semantic caching via Redis / Qdrant
  • · Prometheus + OTLP telemetry out of the box
● app.py
# OpenAI Python SDK — swap base_url, and use your ACE key
from openai import OpenAI

client = OpenAI(
    api_key="ace_dev_...",                      # not your OpenAI key
    base_url="https://engine.acefleet.dev/v1",  # 👈 ACE intercepts
)

resp = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": heavy_prompt}],
)
§ 04 / core showcaseAI Scale Without the Compute Inflation.

Optimization matched to your architecture.

Three deployment surfaces, one control plane. Pick your architecture — ACE composes the right primitives underneath.

10 skills ·
target_persona50–70%

API & Agentic Applications

AI app builders, agent developers, and early-stage startups consuming third-party frontier APIs.

👔business_value·leadership
problem

Multi-step agents repeat prompt histories constantly, causing exponential token inflation and gross margin collapse.

ace_result

Cut monthly API spend 50%–70% without touching core application logic or degrading user experience.

🛠️technical_engine·CTO / VP ML

Multi-step agents replay prompt histories on every hop — token inflation shrinks SaaS gross margins from 80% down to 30%.

prompt_tokens
−58%
cache_hit_ratio
0.71
avg_latency
18ms
cost_per_1k_calls
$0.34
feature_01

Semantic Prompt Caching (sub-20ms)

mechanism

Vector embedding proxy (Redis / Qdrant) running cosine similarity match at ≥0.93 threshold on incoming prompt embeddings.

impact

Intercepts repetitive user queries and returns cached responses instantly at zero token cost.

feature_02

Dynamic Intent-Based LLM Routing

mechanism

Lightweight 0.5B classification router / regex intent parser intercepts outbound API calls before dispatch.

impact

Routes multi-step reasoning to GPT-4o / Claude 3.5 Sonnet; offloads JSON extraction, formatting, and classification to Llama 3.1 8B / GPT-4o-mini.

feature_03

Entropy-Based Prompt Pruning (LLMLingua-2)

mechanism

Contextual compression algorithm calculates token perplexity and strips low-information, redundant, or repetitive context prior to provider dispatch.

impact

2×–5× input token reduction while preserving generation quality.

feature_04

Agentic KV Summarizer & Loop Guardrails

mechanism

Sliding-window state compressor auto-condenses multi-turn agent trajectories (Reasoning → Action → Observation) into dense summaries.

impact

Stops exponential context-length scaling during long agent execution loops.

§ 08 / observabilityThe receipt, not the promise.

Every saved token, on a live dashboard you own.

Ship ACE and the gateway renders a real-time cost dashboard out of the box — cache hits, router downgrades, compaction ratios, and reclaimed spend, all fed by the same request stream that serves your traffic. No BI stack to stand up.

ace / dashboard / overview
tokens_saved
184.2M
+23.4%· last 7 days
reclaimed_spend
$27,481
+31.7%· vs baseline
cache_hit_ratio
62.8%
+4.1pp· rolling 24h
latency_delta
−38ms
p50 faster· vs upstream
reclaimed_spend · usd / hourlast 18h
peak
$1,842 / hr
avg
$1,140 / hr
since deploy
$184,720
request_disposition
  • semantic_cache47%
  • llm_router31%
  • prompt_compaction16%
  • passthrough6%
cost_attribution · by_provider
anthropicnet $9,240 · saved $7,180
openainet $6,120 · saved $5,240
google_vertexnet $3,480 · saved $2,310
deepseeknet $1,260 · saved $940
cost_attribution · by_model_endpoint
gpt-5.6-solnet $6,120 · saved $5,240
fable-5net $5,180 · saved $4,320
gemini-3.6-flashnet $2,940 · saved $1,980
deepseek-v4-pronet $1,040 · saved $780
router_intelligence · why_it_routed● live
  • optimized_for_cost58%
    simple prompt → cheaper model
    n = 12,481
  • optimized_for_latency27%
    primary lagging → faster peer
    n = 5,812
  • fallback / auto_retry15%
    upstream 5xx → sibling provider
    n = 3,204
traffic_switch · gpt-5.6-sol ⇢ fable-5last 24m
openai 503 detected · auto-switched · 0ms downtime
gpt-5.6-sol (primary)fable-5 (failover)
fail_open_guarantee● active

ACE is designed to be invisible. If any internal skill exceeds its latency budget, or the gateway itself encounters an error, ACE instantly degrades to a direct pass-through — your request is relayed straight to the downstream provider, pipeline bypassed, upstream body untouched.

x-ace-served-by: direct_passthroughtrigger: skill > 50ms · panic · storage unreachableproduction traffic: never drops
request_lifecycle · most_recenthover a row
requesttypemodel_routed_tocachecostsaved
Hey, could you please refactor the auth module across all of the 12 files to use the new session APIcodingfable-5 → deepseek-v4-proMISS$0.3040 $0.0101$0.2939
Given these three conflicting lab results, can you tell me which hypothesis best explains the data?reasoninggpt-5.6-sol → kimi-k3MISS$0.0700 $0.0366$0.0334
I need you to prove that the sum of the first n odd integers equals n squaredformal_mathgpt-5.6-sol → deepseek-r1MISS$0.0975 $0.0073$0.0902
Quick question — what is the difference between gross margin and contribution margin?knowledge_qasemantic_cacheHIT$0.0235 $0.0000$0.0235
Review this 340-page vendor agreement very carefully and flag every auto-renewal clauselong_docopus-4.8 → gemini-3.6-flashMISS$0.6800 $0.2040$0.4760
Navigate to the billing portal for me and download last month's invoicecomputer_usefable-5 → sonnet-5MISS$0.0840 $0.0252$0.0588
Please just classify this support ticket by urgency and product areaclassifygemini-3.6-flash → deepseek-v4-proMISS$0.0006 $0.0002$0.0004
Hi, I wanted to ask — how do I rotate an expired API key without downtime?support_faqsemantic_cacheHIT$0.0280 $0.0000$0.0280
Sorry to bother, but what is the parental leave policy for US-based employees?policy_ragsemantic_cacheHIT$0.0370 $0.0000$0.0370
hover a request to trace its lifecycle · struck text is what compaction removed, struck cost is what the caller's model would have billed
open control plane →how it's built →snapshot · representative traffic
§ 05 / STAGED ROLLOUT ENGINE · SHADOW TRUST LADDER

Staged Rollout Controls for Mature Engineering Systems

Deploying AI compute optimizations into high-availability production environments demands rigorous safety. ACE provides a granular 3-tier staged trust ladder: off, shadow (shadow evaluation), and prod.

● ENGINEERING_RIGOR· ZERO_BEHAVIORAL_DRIFT

Run Verification in Shadow Mode Before Enforcing in Prod.

Mature platforms cannot risk model output mutation or unexpected latency spikes. ACE’s shadow mode evaluates skills against 100% of live production traffic, recording counterfactual accuracy metrics, latency impact, and cache candidate scores without altering a single byte of downstream output.

OFF MODE
1 SKILLS
Bypassed
SHADOW MODE
2 SKILLS
Shadow Evaluation
PROD MODE
3 SKILLS
Enforced Live
LIVE SCREENSHOT
ACE Live Skill Staged Rollout Toggles (off, shadow, prod)
✓ Verified: Granular per-skill toggle controls with zero-downtime microsecond fallback.
LIVE SKILL CONTROL MATRIX// Interactive staged rollout toggles
off shadow prod
Semantic Prompt Cachingcost saving

Vector similarity match at ≥0.93 threshold to replay responses at $0.

[MODE: PROD] Serves cached responses instantly at 0 token cost.
Dynamic Intent Routercost saving

Classifies query intent and routes to cheapest baseline model clearing quality floor.

[MODE: SHADOW] Shadow scoring live models; records counterfactual cost savings without altering routing.
Entropy Context Compactionperformance

LLMLingua-2 context pruning stripping low-information tokens before API dispatch.

[MODE: PROD] Compresses prompt context 2×–5× before provider dispatch.
Injection & Jailbreak Guardsecurity

Pre-execution scanner refusing prompt injection and jailbreak payloads.

[MODE: SHADOW] Scores attack vectors and logs security telemetry without blocking candidate prompts.
PII Redaction & NERsecurity

Dual-stage regex & BERT ONNX classifier scrubbing sensitive data before provider call.

[MODE: PROD] Redacts PII tokens live across requests and log traces.
Adaptive Circuit Breakerresilience

Automatic outlier ejection and failure shedding under upstream provider degradation.

[MODE: OFF] Skill is bypassed entirely for incoming requests.
6 core skills configured · real-time mode switchingmanage key settings in dashboard →
§ 09 / try it liveOne prompt, the full engine trace.

LIVE DEMO.

The left pane streams the engine's decision log; the right pane returns the model response with token and cost accounting.

ace / console / try
free trial · 10 of 10 sends left
Engine decision log
[req-a41f9c2d80b3] [in]: "implement fizzbuzz in typescript with tests"
[req-a41f9c2d80b3] [pii]: nothing to redact · ner_regex + ner_model scanned
[req-a41f9c2d80b3] [router]: query_category=code · complexity=medium → gpt-5-mini
[req-a41f9c2d80b3] [guard]: clean · pattern rules + classifier · classifier in shadow mode
[req-a41f9c2d80b3] [cache]: MISS · best-similarity=0.0000 (threshold 0.9200) · scope=session
[req-a41f9c2d80b3] [compact]: -0 tok · below the 40-token floor, sent unchanged
[req-a41f9c2d80b3] [serve]: gpt-5-mini @ azure-v1 · tok 10/42 (16 reasoning) · $0.000087
Responsegpt-5-mini
function fizzbuzz(n: number): string { if (n % 15 === 0) return 'FizzBuzz'; if (n % 3 === 0) return 'Fizz'; if (n % 5 === 0) return 'Buzz'; return String(n); }
tok in
10
tok out
42
cost
$0.000087
reasoning 16 tok
Router selectedgpt-5-mini
Unrealized savings$0.000870 / request
Cheaper than claude-opus-4-5$0.013914 / request
BillingBYOK_PASS_THROUGH

log lines and header values captured verbatim from the ACE production engine · model catalog 07312026

§ 09 / watch the demo

See ACE route, cache, and save in real time.

A two-minute walkthrough of the production engine: one prompt, the full decision trace, and the cost accounting that shows up in your dashboard.

youtube · 0IOG8zqCd7w2 min walkthrough
try it first-hand

Stop Paying the Token Tax.
Reclaim Your Gross Margins Today.

Zero Token Waste. Maximum Fleet Efficiency.

Sign in to your developer console and generate your ACE_DEV_KEY on demand. No waitlist, no call, no credit card.

Generate Developer Key →Read the Docs
§ 09 / field notes

From the fleet. Engineering notes.

all posts →