Product
One optimization layer, from detection to remediation.
Not three products. One pipeline. Detect what's wrong, decide and fix it with a human in the loop, at the scale of your entire fleet.
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Telemetry
vLLM metrics read continuously; KServe and Ray Serve deployments detected
01
Detect
Your cluster's X-ray.
02
Decide & Fix
Detect. Recommend. Fix, with a human in the loop.
03
Fleet Scale
The same detect-and-fix loop, across every cluster you operate.
Paralleliq runs one loop continuously: it reads the metrics your stack already exposes, turns each problem into a recommendation with its dollar impact, and changes nothing until an operator approves.
- 1Discovers every vLLM, KServe and Ray Serve deployment and the hardware it runs on
- 1Judges model-to-hardware fit, not just GPU utilization
- 1No added load on workloads and no changes to your serving stack
- 2Dollar impact on every finding
- 2Nothing runs without operator approval
- 2Every change is reversible and logged
- 2Findings available through the API
- 3Every cluster, cloud and region in one view
- 3Findings tied to workload, team and cluster
- 3Cost and capacity across the whole fleet
Incident · KV cache pressure on a100-pool-2
needs approval+ recommend rebalance shard 3 → 5
- evict idle replica infer-canary-2 · saves $184/hr
Approve & applySnooze
audit chain · sig 0x9f2…ae1What a finding looks like
How it connects: reads the Prometheus metrics your stack already exposes, with no changes to your serving stack and no added load on workloads. Collection is moving to a standard OpenTelemetry Collector. Other runtimes, SGLang included, are added by mapping their metrics.