Cumulus
Labs
Production-grade inference.
Routed, evaluated, and fine-tuned.
Built on our own NVIDIA Grace and Blackwell fleet with custom attention kernels.
Founded by alumni from
Change one line.
Keep your code.
Workflows optimized
top down.
Optimize each node. The whole workflow gets faster, cheaper, and more reliable.
Production-grade,
not demo-grade.
Custom kernels on NVIDIA Grace.
Our Ion engine beats stock vLLM and SGLang on the same chip. More requests per second, lower cost per token.
Provider outages routed around.
Continuous health checks across every provider. When one drops, traffic reroutes before users notice.
More than judge consensus.
Synthetic data, heuristic checks, and LLM judges. Every workflow graded against production traffic in shadow.
Production is brittle.
Five vendors. Five blind spots.
Eight subsystems.
One platform.
Designed to work together. Inference at the core, everything else built on top.
OpenAI-compatible HTTP layer. One client works against every provider.
Declared routing rules pick the model, the provider, the infrastructure. Deterministic and traceable.
Exact-match, prefix, and an optional semantic cache. Stacked. Cuts input tokens dramatically.
Input, output, model, latency, cost, quality. Replayable audit log. Real-time dashboards.
More than judge consensus. Auto-generated rubrics, synthetic test data, deterministic heuristic checks, plus LLM judges. Continuous shadow evaluation on production traffic.
Spots candidate workflows from traffic. Trains LoRAs on our fleet. Migrates traffic gradually.
Host open-weight models or your own fine-tunes on Ion. Cheaper than direct cloud GPU rental.
Our inference engine on NVIDIA Grace. Custom attention kernels beat vLLM and SGLang.
Where it lands.
Anonymized examples. Real deployment shapes.
Routing logic in if-else statements.
Three providers across reasoning, summarization, and classification. Failover paths written, never tested. One outage takes the product down for six hours.
Declared routing rules. Every provider health-checked continuously. Failover routed automatically before the user notices.
Frontier-only by default.
Mistakes have legal consequences. Suspects cheaper models could carry most traffic. Can't justify the engineering work to verify which ones.
Shadow evaluation runs against production traffic. Synthetic data plus heuristic checks plus LLM judges surface the safe swaps. Approve one workflow at a time.
Frontier latency too high. Open-weight too slow.
Voice agents need responses under a second. Frontier model latency overshoots. Rented GPUs add cold-start time and unpredictable throughput.
Fine-tuned LoRA trained on production traffic. Served on Ion's custom kernels. Throughput uplift translates directly to lower end-to-end latency.
Four tools across three clouds.
AI tools built across Azure OpenAI, Bedrock, and Vertex AI. Different APIs, different dashboards, different audit logs. Per-tool attribution missing.
One client across all three clouds. Per-tool attribution by default. A single audit log built for compliance review.