Prism
Prism Inference provides a serverless AI inference platform offering OpenAI- and Anthropic-compatible APIs for open-weight models like DeepSeek-V4.1-Flash and Gemma 4 31B. The platform delivers inference speeds up to 5.8× faster than major cloud providers at up to 50% lower cost, with a strict zero-data-retention policy across all service tiers. Its engine is specifically optimized for coding-agent workloads, such as 20k-token prefills with high cache-hit rates.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Developers and AI teams need fast, affordable inference for open-weight models, but major cloud providers often deliver suboptimal performance at high costs. Additionally, enterprise users face privacy concerns regarding whether their prompts and outputs are stored or used for training by inference providers.
Solution
Prism Inference provides a serverless AI inference platform that combines model APIs, GPU infrastructure, and agent runtimes in a single unified stack. The platform offers OpenAI- and Anthropic-compatible APIs for frontier open-weight models like DeepSeek-V4.1-Flash and Gemma 4 31B, achieving inference speeds up to 5.8× faster than major cloud providers while costing up to 50% less. Prism's engine is optimized for agent workloads—characterized by high prefix-cache hit rates and longer input contexts—through techniques like speculative decoding, tuned quantization, and careful tensor-parallel configuration. The platform enforces a strict zero-data-retention policy: all API inputs and outputs are processed transiently and never written to persistent storage, logs, or backups, and are never used for model training.
Target Audience
Primary customers are AI engineering teams, coding-agent developers, and enterprises that need high-throughput, low-latency inference for open-weight models at scale, with strict data-privacy guarantees.
Features
- OpenAI-compatible and Anthropic-compatible APIs covering models like DeepSeek-V4.1-Flash and Gemma 4 31B
- Serverless per-token pricing with no minimum commitment, plus elastic endpoints, dedicated GPU deployments, and batch processing options
- Optimized serving stack on AMD Instinct hardware with custom kernel tuning, speculative decoding, and quantization (including MXFP4) to maximize tokens per dollar
- Engine tuned for coding-agent traffic patterns such as 20k-token prefills with high cache-hit rates, not just single-stream decode benchmarks
- Zero data retention for all API inputs and outputs across every service tier; metadata kept only for billing and operational purposes
- Pricing at $0.09 per 1M input tokens and $1.20 per 1M output tokens for DeepSeek-V4.1-Flash, with cache-read rates as low as $0.03 per 1M tokens