AI Inference Infrastructure
Orchestrate GPU workloads across 22 cloud providers simultaneously — at 65–76% lower cost than AWS SageMaker. Predictive autoscaling, zero-trust security, and hard budget stops. The orchestration layer, already built.
# any python fn → gpu api endpoint def execute(prompt_str: str): model = load("llama-3-70b") for token in model.stream(prompt_str): yield token # → auto sse mesh.deploy(execute, FlopConfig( gpu = "RTX4090", vram_min = 24, strategy = "predictive", max_hr = 0.44, )) # routes across 22 providers # arima autoscaling · auto-failover
The Problem
Running AI inference in production forces a choice between two options — neither of which is viable at scale.
AWS SageMaker · GCP Vertex AI · Azure ML
Vast.ai · RunPod · Spheron · io.net
Building the orchestration layer takes 12–18 months of engineering. Paying the managed-cloud premium destroys unit economics. Neither option is viable at scale for an AI-native product team.
The Solution
Wazza Mesh is a production-hardened, provider-agnostic control plane. Everything you would have spent 12–18 months building, delivered as a single deployable.
Routes every workload to the cheapest GPU available across 22 integrated providers in real time. Automatic failover if a spot instance is interrupted — invisible to end users.
CP-SAT constraint solver · FLOP-based routing
ARIMA-based forecaster provisions GPU capacity before demand spikes arrive — not reactively. Warm pool nodes slide in seamlessly with sub-60-second cold starts.
EnhancedARIMAPredictor · QoS fair-queue
AES-256-GCM + RSA-4096 per-session encryption. Containers self-destruct if tampered with. Runs securely on fully untrusted third-party hardware with fileless payload execution.
Zero-plaintext model · Dead man's switch
Configurable spend caps per second / hour / day. Circuit breakers destroy instances the moment cost bounds are breached. No runaway billing from autoscaler latency.
BudgetManager · velocity tracking · circuit breaker
Deploy any Python function as a
GPU-backed API endpoint in minutes. Auto-generates
OpenAPI schema. Detects yield and switches
to SSE streaming automatically — no config required.
AST reflection · VenvEnclave · DockerEnclave
Real-time dashboard, Prometheus metrics, OpenTelemetry tracing, ARIMA forecast visualization, and per-function cost tracking — all built in. Nothing to wire up.
AnalyticsEngine · LatencyPredictor · port 8080
Security Architecture
Wazza Mesh assumes the GPU host machine may be adversarial — including root-level access by the cloud provider's staff. Every layer operates from this premise. Proprietary model weights and inference data are never exposed in plaintext on untrusted hardware.
All shells (/bin/bash, curl,
wget, ssh) deleted from the
image at build time. docker exec from the
host returns an error — no executable shell exists to
enter.
All inference code is transmitted over the encrypted
Zenoh mesh and executed via exec() into an
isolated in-memory namespace.
No .py files exist on disk
— docker cp yields an empty
/app directory.
Each container generates its own RSA-4096 keypair in volatile RAM at boot. The private key never leaves the container. The Engine encrypts a nonce with the announced public key — the container must return the decrypted nonce to prove it is not a relay or MITM.
A new ephemeral session key is generated for every inference job. Keys are zero-wiped from RAM immediately upon job completion. Compromising one session key exposes only that session — nothing before or after.
mTLS boot certificates are injected via compressed
environment variables, then overwritten byte-by-byte
with zeros using C-level memory operations before
Python's garbage collector can expose them.
/proc/<pid>/environ yields only null
bytes after boot.
A background thread continuously polls for
TracerPid != 0 (debugger attached). If a
persistent tracer is detected for >500ms: zero-wipe
all cryptographic keys, call os._exit(9).
The attacker learns nothing and cannot retry from the
same infrastructure.
Large AI model weights are chunk-streamed over the encrypted mesh and re-encrypted on disk using AES-256-CTR with HMAC-SHA256 integrity tags. Physical disk access by a host administrator yields only encrypted binary blobs with no usable data.
Cost Economics
The savings come from two compounding factors: the raw GPU spot price differential, and the elimination of the managed-service platform premium.
Pricing data from AWS SageMaker, Vast.ai marketplace, Spheron GPU pricing (March–May 2026). GPU spot rates are volatile — all figures are illustrative and should be validated against live marketplace data before commitment.
Request Access
Wazza Mesh is in private access for engineering-led teams running sustained GPU inference workloads in production. We onboard in cohorts and work closely with each team during setup.
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We review every request and respond within 2 business days.