We sit between your application and your GPU clusters—optimizing every token, every batch, every millisecond. You pay only from the savings we generate.
Standard DevOps tools track utilization. We track economics— exposing the cost per token, per request, per routing decision in real time.
Most systems optimize model cost. The real waste lives in GPU economics. Routing based on the wrong signal costs you millions.
A static or averaged estimate based on model size or provider pricing. It ignores where and how the model runs—no awareness of utilization, scheduling inefficiencies, or live system conditions.
How much you actually pay to generate a token on a specific GPU, given its real-time utilization, throughput, and idle time. Fluctuates every millisecond.
A larger model running on a well-utilized GPU with a full batch can be cheaper per token than a smaller model sitting on an underfilled, idle GPU. Most routing systems will choose the "cheap" small model—and lose money every single time.
One line of code changes everything. Your application stays unchanged—KV Cognition does the heavy lifting in the background.
Change one line in your LangChain, OpenAI SDK, or vLLM client. Our proxy is fully API-compatible—no code refactoring required. Works with every major LLM framework out of the box.
Every request hitting the proxy gets its prefix hashed (first ~1,000 tokens). We check our Global Registry: "Which GPU in which cluster currently holds the warm KV-cache for this conversation?"
For each candidate (model × GPU × cluster), we compute the effective cost: GPU cost/token (live) + latency penalty (based on your SLA tier). We pick the globally optimal option—not just the "cheapest model."
If no warm KV-cache exists, the proxy holds the request for ~5ms to group it with other semantically similar requests. This creates high-density batches that push GPU utilization toward theoretical maximum.
After execution, every request is logged with its actual GPU cost, latency, cache hit status, and routing decision. You finally have the metric that's been missing from every DevOps stack: cost per token per request.
Our engine makes decisions that look wrong on the surface—but are mathematically optimal. See the live arbitrage in action:
| Candidate | Model Size | GPU Util. | Batch Fill | GPU Cost/Token | Latency | Effective Cost | Decision |
|---|---|---|---|---|---|---|---|
| AWS Spot · H100 · Cluster B | 70B | 94% | 88% | $0.000038 | 42ms | $0.000045 | ✓ SELECTED |
| GCP Reserved · A100 · Cluster A | 7B | 41% | 23% | $0.000091 | 28ms | $0.000098 | ✗ SKIPPED |
| Azure VPC · H100 · Cluster C | 13B | 67% | 55% | $0.000061 | 35ms | $0.000068 | ✗ SKIPPED |
| On-Prem VPC · H100 · Corp Cluster | 13B | 18% | 12% | $0.000142 | 61ms | $0.000158 | ✗ SKIPPED |
The 70B model on Cluster B had a warm KV-cache for this conversation prefix (hit on hash #8A22F) and was running at 94% batch density. Even though the 7B model on Cluster A had lower raw latency, its 23% batch density meant a 2.4× higher effective GPU cost per token. We picked the larger model—and it was 51% cheaper.
Our advantages compound over time—the more data we process, the smarter the routing, the deeper the savings.
We take a percentage of savings—never a seat license, never a flat fee. If we don't save you money, you owe us nothing. Cloud providers monetize consumption. We monetize waste.
Zero-Risk ModelThe Switzerland of inference. One proxy that routes across AWS, GCP, Azure, and private VPC clusters simultaneously. An enterprise buys one optimization layer—not three.
Multi-Cloud NativeWe track where every KV-cache lives. When a request arrives, we pin it to the GPU that already holds its context—skipping full prompt re-computation and cutting TTFT by up to 40%.
KV-Cache AffinityA 5ms hold window groups semantically similar requests into high-density batches. This pushes GPU utilization to theoretical maximums—turning idle silicon into revenue.
Dynamic BatchingEnterprise Fortune 500s are moving to on-prem H100 clusters. When demand grows 10× and hardware budgets are locked, we deliver the throughput story: get more from the silicon you own.
On-Prem ReadyThe first platform to expose true cost-per-token-per-request as a live metric. Not utilization %. Not raw latency. Actual economic data—the signal that drives real decisions.
New Metric CategoryAs the cost of tokens decreases, total consumption increases so dramatically that aggregate spend goes up—not down. Every price drop is a volume explosion. We don't just survive cheaper tokens—we thrive because volume growth creates more waste to optimize. The bigger the market, the bigger our opportunity.
Traditional load balancers are blind to KV-cache state. We built a Global Mapping Table that knows where every conversation's memory lives—and routes accordingly.
The proxy takes the prompt prefix and generates a deterministic hash of the first ~1,000 tokens—unique to that conversation's context window.
Query the Global Registry: "Which GPU in which cluster currently holds the warm KV-cache for hash #8A22F?"
Request is pinned to that specific GPU. Re-computation of the full prompt context is skipped entirely—the GPU already has it in memory.
If no warm cache exists, the proxy holds the request for ~5ms to find other requests with similar prefixes—creating a high-density batch from scratch.
GPU utilization is not just a DevOps metric—it's missing economic observability. We expose the inference system as a real-time economic engine.
What Prometheus, Grafana, and Datadog give you today—the signals that don't tell the whole story.
The economic signals that drive real optimization decisions—invisible to every existing tool.
The full economic picture—every metric you need to understand the real cost of inference, not just its operational health.
A single control plane that works across every provider, every VPC, every cluster—simultaneously. Route to wherever the marginal cost is lowest at this exact millisecond.
If we don't save you a dollar, you don't owe us a cent. We take a percentage of the savings we generate—nothing more.
Zero upfront cost. Zero flat fees. Zero seat licenses. Our entire revenue comes from the waste we eliminate from your GPU spend. Our incentives are 100% aligned with yours.
We integrate in under 5 minutes, show you the savings within hours, and only charge when you're already profitable. Start with your existing infrastructure.