Latentforce delivers production-grade inference optimization for AI teams — cut latency by up to 70%, reduce compute costs, and deploy with confidence.
We help engineering teams move from prototype to production by building the inference layer that makes AI models fast enough, affordable enough, and reliable enough for enterprise workloads.
Production AI is hard. Model serving, batching, quantization, autoscaling — we handle it all so your team ships faster.
Dynamic batching, KV-cache management, and quantization pipelines that reduce time-to-first-token by up to 70% without model quality loss.
Deploy and manage LLMs, vision models, and embedding models behind a single unified API with automatic routing and load balancing.
GPU-aware horizontal and vertical scaling that responds to traffic spikes in seconds — no cold starts, no wasted idle compute.
Real-time dashboards for latency percentiles, token throughput, cost per request, and model drift detection across all deployments.
SOC 2 Type II compliance, end-to-end encryption, private VPC deployment, role-based access control, and full audit logging.
OpenAI-compatible REST API with Python and TypeScript SDKs, webhook support, and one-click model migration from any cloud provider.
Go from model to production endpoint in four simple steps.
Import from HuggingFace, AWS, GCP, or upload your fine-tuned checkpoint directly via the Latentforce dashboard or CLI.
Our engine runs quantization, kernel fusion, and batching configuration automatically to match your latency and cost targets.
Push a serving endpoint with one command. TLS, auth, and rate limiting are pre-configured. Blue/green and canary rollouts included.
Real-time metrics stream into your observability stack. Autoscaler adjusts GPU allocation based on live request volume and SLA targets.
"We cut our inference costs by 55% and eliminated cold start delays entirely. Latentforce handles the hard parts so we focus on the model."Head of AI Platform · Series B fintech startup
"The auto-scaling engine is genuinely impressive. We went from 100 requests/sec to 50,000 overnight during a product launch — zero service disruptions."Principal ML Engineer · Enterprise healthcare AI company
Most AI teams discover that a model that performs brilliantly in a notebook fails in production because of the infrastructure gap — not the model itself.
Backed by
$4.8M Seed Round · March 2025
Get started with the Starter plan at $199/month, or talk to our team about a custom Enterprise deployment.