PrimaLabs provides application-specific adaptive inference for frontier open models like DeepSeek, Qwen, and GLM, optimizing throughput, latency, and cost by learning each workload's traffic patterns in real time. The platform delivers up to 3× lower inference costs through cache-aware pricing and dedicated, zero-hop endpoints that can deploy in a customer's VPC or on-premises. A head-to-head benchmark on a customer's own prompts, measuring throughput, latency, and effective cost, is provided in writing before any commitment.
Funding
Funding not disclosed
Founders
Product
Problem
Generic AI inference endpoints are tuned once and then frozen, failing to adapt to the specific traffic patterns of individual workloads. This results in higher latency, lower cache hit rates, and inflated costs, especially for AI-native applications with repetitive or bursty request patterns.
Solution
PrimaLabs provides application-specific adaptive inference for frontier open models, building a dedicated stack that continuously learns and optimizes for a customer's unique workload. The platform watches live traffic, learns from it, and ships improvements only when they measure faster with identical outputs, compounding performance gains over time. This self-adaptive approach, applied to models like DeepSeek V4 Flash, delivers up to 3× lower inference costs and maintains high cache hit rates by keeping a warm, dedicated stack with zero routing hops. PrimaLabs offers deployment in US cloud, VPC, or on-premises environments, and provides a head-to-head benchmark on a customer's own prompts to prove throughput, latency, and cost in writing before any commitment.
Target Audience
PrimaLabs targets AI-native product teams, coding agent developers, and enterprises running high-volume agentic pipelines that require low-latency, cost-effective inference for frontier open models.
Features
- Self-adaptive inference engine that re-tunes batching, scheduling, and cache strategy in real time based on live workload traffic
- Dedicated, zero-hop endpoints with a warm cache, achieving up to 90% cache hit rates versus industry averages
- Cache-aware pricing with effective input rates as low as $0.017 per 1M tokens at a 90% hit rate, and volume-based tier discounts
- Deployment flexibility across US cloud, customer VPC, or on-premises hardware (NVIDIA or AMD GPUs)
- OpenAI-compatible API with US data residency and a known, fixed, auditable data path with no third-party subprocessors
- Pre-commitment benchmark service that measures throughput, latency, and effective cost on the customer's own prompts at no charge