One place to connect
Put compatible coding clients behind a consistent gateway interface instead of managing a different integration path for every tool.
Model and client integration Private pilot in preparation
Make AI coding operational.
Coding agents move fast. Your infrastructure should keep up. Bring model access, usage evidence, and budget guardrails together behind one gateway.
Built for engineering teams experimenting with AI coding tools.
REQUEST FLOW 01 / 04
Coding clientEditor · agent · CLI connected
HTTPS / compatible API
CodeYield GatewayCONTROL PLANEACCESSVirtual keys
USAGEEvidence states
POLICYBudget holds
Route + observe
Model endpointProvider / local model
Usage ledgerMeasured · unknown
A PRACTICAL FOUNDATIONOpenAI-compatible APIProvider flexibilityUsage transparencyTenant-aware controls
01 / THE PROBLEMMore coding agents.
More coding agents.
Less guesswork.
Teams are adopting AI coding tools faster than they can standardize access, explain usage, or apply meaningful controls.
Usage with context
Track what was reported, what can be verified, and what remains unknown. Good decisions start with honest data—not invented precision.
Evidence-aware meteringGuardrails by design
Organize access by tenant and virtual key, with budget reservation and accounting safeguards being hardened for a hosted pilot.
Access and budget policies02 / THE APPROACHSimple to adopt.
Simple to adopt.
Deliberate by design.
CodeYield is built to sit between a coding client and a model endpoint, with a separate control plane for accountability.
Connect your tool
Use an OpenAI-compatible gateway interface. OpenCode and real-model behavior are being validated.
Choose a model route
Define allowed model aliases and direct requests toward supported inference backends.
Observe and govern
Review usage events, measurement completeness, key access and budget states in one place.
LOCAL-FIRST
Current work is validated with deterministic mock inference. Real-model compatibility and production security must pass separate checks before hosting.
View milestones03 / BUILDING IN THE OPEN, CAREFULLYFrom validated
From validated
prototype to pilot.
A measured release path. We'll favor evidence and security over inflated product claims or flashy feature counts.
✓
Local gateway foundation
API facade, model aliases, tenant-aware usage records and deterministic test coverage.
↻
Real inference and accounting
Provider replay, accurate usage attribution, interrupted streams and fail-closed financial behavior.
03
Production identity and security
OIDC authentication, tenant authorization, session protection, recovery and abuse controls.
04
Hosted private pilot
Reproducible deployment, HTTPS, encrypted backups, restore drills and invited design partners.
04 / GOOD QUESTIONSA few things
A few things
worth knowing.
Can we use CodeYield in production today?
Not yet. CodeYield is being developed and tested for a secure private pilot. Hosted authentication, real-model compatibility and operational recovery are still being verified.
Does CodeYield include AI models or GPUs?
No. CodeYield is the gateway and usage control plane. Model inference runs separately through supported provider endpoints or an approved private model runtime.
Does CodeYield guarantee AI cost savings?
No. We aim to improve visibility and decision-making, but we won't claim savings or precise token accounting without measured evidence.
Who is the private pilot intended for?
Engineering and platform teams evaluating AI coding tools that need clearer access controls, usage evidence and safer operational policies.
NEXT / EARLY ACCESSBuild with AI.
Build with AI.
Stay in control.
We're looking to speak with a small number of engineering teams as CodeYield prepares for private testing.
Start a conversation No automated signup. Just a conversation.