Platform · Features
The engine of intelligence.
Nine modules engineered to operate as one. Choose what you need today, adopt the rest when you're ready — without rewriting a single integration.
01 · Training fabric
Distributed training that respects your time.
Spin up multi-node training across H100 or A100 clusters with a config file you can read in thirty seconds. We handle the rest — orchestration, fault tolerance, checkpointing, spot instance reclamation.
- Native support for PyTorch, JAX, and DeepSpeed
- Automatic mixed precision and gradient checkpointing
- Resume from checkpoint after preemption — every time
- Per-step cost telemetry to keep finance close to ML
02 · Inference runtime
Serve fast. Serve everywhere.
A single deploy command moves your model from notebook to a multi-region, autoscaling endpoint. Optimized runtimes for transformers, retrieval, and classical ML.
- Sub-50ms p95 latency for most workloads
- Native streaming, batching, and speculative decoding
- Canary & blue-green deploys, gated by live evals
- BYO container or use our optimized base images
03 · Observability & evals
The model is a system. Treat it like one.
Continuous evaluation, drift detection, prompt diffs, token-level traces. Every prediction is replayable. Every regression is preventable.
- Online + offline eval harness with custom metrics
- Cohort & segment slicing — find the failure mode, not the average
- Tracing compatible with OpenTelemetry
- SOC 2 Type II ready, GDPR-friendly retention controls
Engineering specs
Numbers we're honest about.
42 ms
p95 latency · standard endpoint
99.95%
Inference uptime · last 90 days
3.2×
Faster training vs. unoptimized baseline
12
Regions across 3 cloud providers