Founded in 2025 · Product infrastructure for AI applications

Infrastructure for AI products that have to work.

Predictability at Scale builds the operational and commercial infrastructure behind serious AI applications—from model access to usage economics.

LLM gateway
01
Usage & metering
02
Prompt compression
03
2025Company founded
3Production products
2Open initiatives
1Connected operating layer

From first call to sustainable scale.

Each product solves a distinct production problem. Together, they give application teams a clear path from model access to reliable operations and predictable economics.

01

UsageTap Meter Usage infrastructure

Turn variable AI usage into predictable revenue.

Meter every valuable action, apply customer limits before expensive work begins, forecast runout, and synchronize trusted usage with Stripe or the commerce stack you already use.

  • Real-time entitlements, limits & overage policy
  • Customer-facing usage and forecast visibility
  • Validated billing and commerce synchronization
Explore UsageTap Meter
UsageTap Meter customer usage dashboard with spend forecast, alerts, and billing limit
Allowance, spend forecast, alerts, and guardrails in UsageTap Meter.
02

UsageTap Compress Prompt efficiency

Cut tokens. Keep meaning. Prove both.

Selective prompt compression for production generative AI—with tenant-specific models fine-tuned using LoRA, deterministic protection, inspectable changes, and original-input fallback when checks fail.

  • Tenant-specific LoRA fine-tuning for real workloads
  • Protect IDs, code, JSON, citations & policy text
  • Inspect every transformation before rollout
  • Pay only when input tokens are actually removed
Live UsageTap prompt compression playground showing original and compressed prompt panels
The public Compress playground for prompt inspection and evaluation.
03

LLMAsAService AI gateway

Make AI features exceptional in production.

A full operational layer for application developers: one interface across providers, intelligent routing, failover, safety controls, observability, customer memory, and embedded agent interfaces.

  • Multi-provider routing & automatic failover
  • PII, safety, policy & response controls
  • Cost analytics, call logs & customer context
Visit LLMAsAService.io
LLMAsAService customer memories and validation workspace
Customer memory and validation workspace in LLMAsAService.

Compression you can inspect, question, and improve.

Our public compression playground makes the trade-offs visible. Try real prompts, compare modes, inspect dropped text, run benchmarks, and follow the research shaping our tenant-adapted models.

Production thinking, shared in public.

We publish practical tools and research surfaces that help teams build better AI systems—and give the community a clear view into how we approach the hard parts.

OS / 02

Prompt compression lab Built in public

A working playground, evaluation suite, benchmark surface, and research bibliography for safer prompt compression in real application workloads.

Open the compression lab

Built by operators who know the cost of uncertainty.

Predictability at Scale was founded by Chris Hefley and Troy Magennis—experienced software founders and engineering leaders who have spent decades turning complex systems into practical, measurable products.

Read our story

Building an AI product that needs fewer surprises?

Contact Predictability at Scale