The hard part of AI isn’t training anymore.
It’s running the thing — reliably, cheaply, and at scale.
Fleek is a developer platform for AI inference and model optimization, built for the phase where experiments turn into systems and latency starts to matter more than launch threads.
Modern ML teams lose time to infrastructure gravity: GPU provisioning, bin-packing, autoscaling heuristics, model variants, and performance cliffs that only appear under real load. Fleek collapses that surface area into a single execution layer where models are deployed as production-grade APIs — without hand-rolled clusters or bespoke optimization pipelines.
At its core, Fleek is about making inference boring:
- Lower tail latency
- Higher utilization
- Fewer surprises
We optimize models for the hardware they actually run on, enforce predictable execution characteristics, and keep throughput stable when traffic stops being polite. Generative models, vision workloads, and custom pipelines all run through the same machinery.
The interface stays simple on purpose. Developers integrate through clean APIs while Fleek handles scheduling, scaling, and execution across optimized compute environments. Pricing follows usage, not capacity reservations, so iteration doesn’t require financial guesswork.
- Latency distributions, not averages
- Operational invariants, not fragile scaling demos
- Cost proportional to work, not idle GPUs
- APIs that survive production, not notebooks
Our goal isn’t to expose infrastructure — it’s to remove it. By constraining the problem space and focusing on performance, determinism, and ergonomics, Fleek lets teams ship AI systems that behave predictably under real-world conditions.
Less ceremony. Fewer knobs. Fewer mistakes.
Learn more at https://www.fleek.sh
