Boundless
Boundless provides tailored inference engineering for AI-native teams, combining model selection, serving-system design, and ongoing production operations to optimize speed, quality, and cost. The company offers both a self-serve model API with per-token pricing and a forward-deployed engineering service that tunes hardware, batching, caching, and routing to match specific traffic patterns. Its API is compatible with OpenAI and Anthropic SDKs, and its models are available through the Vercel AI Gateway.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
AI-native companies often struggle with high inference costs and poor performance because they deploy models without optimizing the serving stack for their specific traffic patterns. Off-the-shelf deployments frequently overspend on underutilized hardware, miss latency targets for interactive products, and fail to exploit caching or batching opportunities that could dramatically lower per-token expenses. This makes scaling ambitious AI products economically difficult.
Solution
Boundless provides embedded inference engineering services that integrate directly into a client's stack, selecting and fine-tuning frontier AI models to match specific traffic patterns, latency targets, and context shapes. The company profiles a customer's traffic, evaluates viable open models against quality criteria, and then designs a serving system around the workload, tuning precision, parallelism, batching, caching, routing, and hardware selection. Forward-deployed inference engineers work alongside client teams through evaluation, benchmarking, deployment, and ongoing production operations, including capacity management, monitoring, and incident response. Boundless also offers a self-serve platform with model APIs and a pricing calculator that lets customers estimate their monthly bill based on model choice, output token throughput, and expected cache-hit rates.
Target Audience
Primary customers are AI-native startups and product teams that need scalable, cost-efficient inference for chat, copilots, live agents, and batch workloads, plus engineering organizations lacking in-house expertise in serving-system optimization.
Features
- Serving-system design tailored to workload-specific targets for tokens per second, time-to-first-token, per-token latency, or batch deadlines
- Model selection and evaluation using representative test sets and repeatable quality thresholds
- Hardware and runtime configuration including quantization/precision, tensor and pipeline parallelism, batch size, concurrency, scheduler policy, cache allocation, and routing
- Forward-deployed engineering team that continues operating the serving environment post-launch with canary deployments and incident response
- Public model API with list pricing (e.g., DeepSeek V4.1 Flash at $0.20 per 1M input tokens, $1.00 per 1M output tokens, $0.01 per 1M cached input tokens) and a cost-estimation calculator comparing 0% vs. scenario-based cache-hit rates
- Integration with Vercel AI Gateway, making Laya models available at no cost through that platform
- API compatible with OpenAI and Anthropic SDKs, enabling switching by changing only the base URL, API key, and model name
- Prepaid credit billing with per-API-key budget controls to prevent surprise bills