Prometheus

Prometheus is the intelligence-amplification layer of the Babadook ecosystem. Its specified operating model connects model training, registry and experiment lineage; document processing and confidence-routed human review; active learning and controlled retraining; and a Rust-native inference engine for Transformer, state-space, hybrid, vision-language, vision-language-action, embedding, and SGMM workloads.

The platform is designed for three deployment motions. It can operate as a standalone OpenAI-compatible inference service, as an integration layer between selected Babadook systems, or as a governed node in the full CRTL-mediated product mesh. Hardware backends, cluster orchestration, semantic caching, KV compression, and ecosystem integrations remain independently gated by their phase-specific verification contracts.

Prometheus is in development. Performance figures on this page are specification thresholds or design targets unless a result is explicitly identified as verified. The model never owns organizational truth or deployment authority: source systems retain their records, ARGUS governs identity and access, CRTL governs runtime placement and capability claims, Timeline-X owns causal event history, and authorized workflows retain the final decision to approve, deploy, bill, or act on an inference.

Prometheus governs a model through five connected lifecycles: build, understand, learn, serve, and improve. A team registers data and experiments, trains or imports a model, validates it against declared acceptance criteria, deploys an approved version, observes requests and outcomes, and routes corrections back through a controlled learning workflow. Model promotion and rollback remain explicit actions; feedback never mutates a production model by itself.

The platform keeps four objects distinct: source evidence, model artifacts, runtime requests, and operator decisions. That separation lets engineers reproduce a result, reviewers challenge an extraction, operators diagnose a serving path, and auditors trace which authorized action changed production state.

Standalone mode serves external clients through compatible inference APIs without requiring the wider product mesh. Integration-layer mode connects named systems for a bounded workflow such as connector-fed retrieval, knowledge-grounded generation, or metered inference. Full-platform mode runs Prometheus as a CRTL-governed node with product discovery, identity, telemetry, deployment, billing, and causal-history contracts.

The specified runtime spans developer CPU or workstation use, accelerator-backed single-node serving, cluster-scale fleets, and edge or disconnected deployments. Capability is hardware- and profile-dependent: an edge instance may continue with a pinned local model and buffered evidence, while a cluster deployment may separate prefill, decode, and routing pools. Offline operation does not waive model provenance, tenant separation, or later evidence synchronization.

Prometheus owns model execution, model-lifecycle evidence, extraction confidence, learning candidates, and inference telemetry. It does not become the source of truth for enterprise records, knowledge entities, identities, invoices, mission state, or deployment policy merely because those systems exchange data with it.

ARGUS supplies identity and authorization decisions. CRTL supplies runtime placement, capability claims, networking, and secret boundaries. Timeline-X receives ordered lifecycle and inference events. XRP receives metering records and owns commercial billing state. Hephaestus owns approved rollout actions. Tool calls, physical actions, and model promotions remain subject to the connected system's authorization and denial rules. A generated answer or rule is evidence for a decision, never the decision itself.

Prometheus is in development and its canonical phase documents are construction contracts. Terms such as supported, compatible, low-latency, isolated, fault-tolerant, or drop-in describe requirements only after the relevant functional, performance, security, interoperability, and failure-mode criteria pass.

Public numbers are therefore identified as targets unless backed by a published verification record. Hardware breadth means specified backend scope, not equal capability on every device. Ecosystem integration means a named typed contract and authority boundary, not a claim that every adjacent product is already deployed with Prometheus.

The Python tax is real, and it is enormous.

vLLM spends 62% of its CPU time on Python overhead — HTTP handling and scheduling — before a single token is generated. At 150+ concurrent users, SGLang's latency spikes 2.4x. Both run ZMQ IPC with serialization overhead that compounds with every request. Both have shipped multiple RCE vulnerabilities from unsafe deserialization paths. And both are NVIDIA-only. If you are deploying models on AMD, TPU, Cerebras, or Trainium, vLLM is not an option — it never will be.

Prometheus is the inference engine that should have been Rust from the start. Lock-free SPSC ring buffers replace ZMQ. A single 50MB binary replaces the 2GB Python stack. Scheduling runs in under 100 microseconds. The same binary serves Llama on an NVIDIA H100, Mamba on an AMD MI300X, and a VLA on a Jetson at the edge — and it starts in under 2 seconds cold.

Throughput without the drama.

12,500 tokens per second on a single H100. Continuous batching that keeps GPU utilization above 95% under sustained load. TTFT under 100ms for Llama-3.1-8B. Structured output adds less than 5% overhead. Speculative decoding with EAGLE-3 tree attention running at 60%+ acceptance rates. Multi-draft routing that automatically selects the best draft model per domain.

The numbers are not aspirational — they are verification thresholds in the construction contract. If it does not hit 12,500 tok/s on a single H100, it does not ship.

Inference is the core. The platform is the whole.

Beyond the engine, Prometheus runs the complete ML lifecycle on the same infrastructure. Each capability shares the same governance, the same audit trail, and the same hardware abstraction layer.

Single-process. Multi-threaded. Lock-free.

One Rust binary. No Python interpreter. No GIL. No ZMQ message passing. No serialization between the scheduler and the GPU worker. The control plane and the compute engine share memory through lock-free SPSC ring buffers. The scheduler prepares batch N+1 on CPU while the GPU executes batch N — overlap execution without IPC overhead.

Continuous batching with iteration-level unified token-budget scheduling. PagedAttention KV cache with block-table indirection, Copy-on-Write, radix tree prefix caching, and content-addressable hash chains. SSM state pool for Mamba-family models with state checkpointing — 200-400x cheaper to fork than Transformer KV cache. Dual-state management for hybrid SSM+Attention models with Marconi-style co-management.

Structured output via pushdown automaton with jump-forward decoding. Precomputed context-independent token masks at under 40 microseconds per token — matching XGrammar. Tool calling with grammar-enforced valid calls and parallel dispatch. Multi-LoRA serving with per-request in-batch adapter switching.

100,000 concurrent users. Million-agent swarms.

The production platform disaggregates prefill and decode across independent GPU pools — compute-bound prefill on high tensor-parallel nodes, memory-bandwidth-bound decode on high-batch nodes — with KV cache transfer via RDMA and CXL shared memory.

The scheduler is agent-aware. It tracks agent DAGs (user→agent→sub-agent), co-locates related agents on shared-prefix nodes, inherits priority from parent agents, and applies workflow-aware KV eviction. Unlike generic request-level schedulers, it understands that an agent swarm is a coordinated organism, not a random collection of independent tokens.

Graceful degradation at five levels — under load, the system compresses, queues, and prioritizes. It never drops a request. Sessions reconnect with zero token loss within TTL. The control plane runs Raft consensus with sub-5-second leader election. Pure Rust. No Kubernetes dependency. No etcd. Air-gapped and edge-ready.

Zero-trust inference from socket to salt.

Authentication with per-tenant rate limits, cache salts, and model-adapter access control. Row-level tenant isolation for evolutionary rules. Encrypted models, genomes, and rule sets at rest. TLS 1.3 everywhere.

Supply-chain verification of model weights. Sandboxed Python bridge with schema validation. SSRF-resistant tool dispatch. Safe model loading with format validation before deserialization. Cache isolation against timing leaks — cryptographic per-tenant salting prevents cache-based side-channel attacks that leak information between tenants sharing physical GPU memory.

vLLM has shipped multiple RCE CVEs from pickle deserialization paths (CVE-2025-62164, CVE-2026-22778, CVE-2025-66448). Prometheus has zero Python in the hot path and eliminates every memory-unsafe vector that makes those CVEs possible.

Swap your client, not your architecture.

OpenAI-compatible /v1/chat/completions endpoint. Point OpenAI(base_url=...) at Prometheus and it works. Chat, completions, embeddings, streaming, structured output, tool calling, logprobs, multi-modal — every OpenAI API pattern is supported.

Native gRPC and Protobuf for inter-service communication within the Babadook ecosystem — binary-efficient, schema-versioned, type-safe contracts. WebSocket realtime for bidirectional streaming with multiplexed streams, mid-generation cancellation, and session reconnection.

SDKs in Rust (prometheus-client), Python (prometheus-ai, PyO3-bound), and TypeScript (prometheus-js). Each is a first-class citizen — not an OpenAPI-generated afterthought. CLI for every operation. Minimal zero-dependency web UI served from the binary at /ui.

Complaint: Algorithmic trading models run on inference engines that cannot tell you which model version served which prediction, under which policy, with which risk boundary. Fraud detection retrains on drift without evidence of what changed or why. When the SEC, FCA, or RBI auditor asks for the model lineage behind a trade decision that lost nine figures, your answer cannot be 'the vector store returned something.' Regulatory AI governance demands cryptographic audit trails, per-tenant isolation, and immutable model lineage — and Python-based inference engines were never built for this.

Resolution: causal audit records every inference event at nanosecond precision with causal ordering — model version, adapter selection, retrieval context, tool dispatch, and decision boundary, all cryptographically verifiable. Authentication enforces per-tenant isolation at the cache-salt level, preventing cross-client information leakage including timing side-channel attacks. XRP provides per-request token accounting with quota enforcement. Every prediction is auditable. Every model lineage is traceable. Every tenant is isolated at the hardware cache level. This is AI infrastructure your compliance officer will actually sign off on.

Buyer pressure: The EU AI Act, SEC algorithmic trading rules, and global financial regulators are codifying model governance requirements. Banks and trading firms deploying models without audit trails, tenant isolation, and lineage tracking will face regulatory action — not in five years. Now.

Complaint: Clinical NLP models extract PHI from unstructured physician notes, radiology reports, and pathology findings. Medical imaging models flag potential malignancies. These predictions affect treatment decisions — and they run on inference engines with no evidence chain from source document to model output. When a model misses a diagnosis and the hospital legal team asks 'which version of which model, trained on which data, grounded in which retrieval context, made this prediction,' the answer is a Jira ticket and a prayer. HIPAA requires data isolation that most inference engines do not enforce at the hardware level.

Resolution: Knowledge-grounded RAG links every clinical prediction to a verified knowledge graph with entity resolution, disambiguation, and full provenance — the model references governed truth, not vector similarity. Cryptographic cache salts provide hardware-level tenant isolation so patient data from Hospital A cannot leak into Hospital B's inference context. causal audit records every prediction with causal evidence chains. Document intelligence extracts entities with confidence scoring and routes low-confidence extractions to human review — corrections feed back into active learning retraining. Every prediction has a chain of custody from source document to model output.

Buyer pressure: HIPAA enforcement, FDA software-as-medical-device regulations, and hospital legal departments are converging on a single demand: prove what your model did and why. Healthcare AI without provenance is a liability, not a product.

Complaint: Classified intelligence models must run air-gapped with zero outbound network connectivity. Supply-chain verification of model weights is non-negotiable — a compromised checkpoint is a national security incident. Multi-level security requires hardware-enforced tenant isolation across classification boundaries. Python-based inference engines with pickle deserialization paths and multiple shipped RCE CVEs are fundamentally ineligible for classified workloads. And CUDA-only hardware lock-in means you cannot deploy on the full diversity of defense compute infrastructure.

Resolution: Prometheus deploys as a single 50MB Rust binary with no Python interpreter, no pickle, and no memory-unsafe attack surface. Air-gapped operation with offline model loading, supply-chain weight verification before deserialization, and encrypted models at rest. 26 hardware targets including FPGA for sub-5-microsecond deterministic inference — deploy on whatever the mission requires. TLS 1.3 everywhere. Zero-trust architecture from socket to cache salt. Authentication-enforced multi-level tenant isolation. causal audit trails satisfy evidentiary requirements. This is inference infrastructure built for classified environments.

Buyer pressure: Defense procurement is mandating software supply-chain verification, air-gapped operation, and hardware diversity. Python-based inference engines with RCE vulnerability histories are being explicitly ruled out of RFPs. The bar is not 'fast tokens' — it is 'verifiably secure tokens on verifiably secure infrastructure.'

Complaint: Predictive maintenance models run on factory-floor edge devices with 4GB of RAM and intermittent connectivity. Quality inspection vision models need sub-50-millisecond inference at the production line. Robotic VLA models must plan and execute physical actions in under 200 milliseconds with safety-bounded output. Python-based inference engines with 2GB memory footprints, GIL contention, and Python interpreter overhead cannot meet these constraints. And when the edge device reconnects after an offline period, models must sync and update without re-deploying the entire stack.

Resolution: Prometheus ships as a 50MB binary that runs on Jetson, Snapdragon, and x86 edge devices. Sub-50ms inference for quality inspection. Sub-200ms VLA planning for safety-bounded robotics with four truth regimes for evidence provenance. Offline buffering with automatic sync when connectivity returns. Model compression down to 10MB for edge deployment. Evolutionary compression with NAS for architecture-specific optimization. The same inference engine that runs on a 10,000-GPU cluster runs on a factory-floor edge device — same binary, same API, same governance.

Buyer pressure: Industry 4.0 is not a PowerPoint deck — it is inference at the edge under hard latency, memory, and reliability constraints. Python inference stacks with interpreter overhead and 2GB memory footprints cannot serve this market. The factories are waiting. The robots are waiting. The inference engine needs to be ready.

Complaint: Research teams train models on whatever hardware their grant budget affords — AMD today, TPU tomorrow, Cerebras next quarter. Production teams deploy on whatever the inference engine supports — which is usually NVIDIA-only. The research-to-production gap is not a talent problem; it is a hardware compatibility problem. Experiment tracking is disconnected from deployment lineage. Hyperparameter search results cannot be traced to the model version serving a live workload. And when a paper claims state-of-the-art, nobody can reproduce it because the serving infrastructure does not match the training infrastructure.

Resolution: Prometheus serves models on 26 hardware targets through one InferenceBackend trait — train on AMD MI300X, serve on Cerebras WSE, deploy to NVIDIA H100, all from the same codebase. tensor optimization integration for geodesic hyperparameter optimization with deterministic checkpointing. Neural architecture search with automated deployment to the serving engine. Experiment tracking with full lineage from training config to serving deployment. Reproducible inference with deterministic seeds, hardware-aware scheduling, and versioned model lineage. The inference engine bridges research and production — same binary, same API, same governance.

Buyer pressure: Research reproducibility and hardware flexibility are not academic concerns — they are production requirements. When a trained model cannot be served because the inference engine does not support the training hardware, the research was a cost center, not an asset.

Complaint: Recommendation engines serve 100,000+ concurrent users during Black Friday — and Python-based inference engines spike latency under load, drop requests, and have no graceful degradation strategy beyond 'scale horizontally and pray.' Personalization models train on customer behavior data that must be isolated per tenant — but inference engines share KV cache without tenant-aware isolation. Search ranking, product categorization, and dynamic pricing models all run on the same serving infrastructure with no per-model quota enforcement. And when the CFO asks 'how much did AI cost us this quarter,' the answer is a cloud bill, not per-token attribution.

Resolution: Prometheus handles 100,000+ concurrent users with continuous batching, disaggregated prefill/decode, and five-level graceful degradation — compress, queue, prioritize, but never drop a request. Per-tenant cryptographic cache salts prevent cross-retailer information leakage. XRP per-request token accounting with per-tenant quota enforcement and 429 gating — bill AI by the token, not the cloud hour. Agent-aware scheduling for multi-stage recommendation pipelines. Structured output for JSON product categorization. Tool calling for inventory-lookup agents. The inference engine that retail infrastructure teams have been waiting for.

Buyer pressure: E-commerce margins are razor-thin. AI infrastructure that cannot bill by usage, cannot isolate tenants, and cannot handle peak load without dropping requests is not infrastructure — it is a revenue risk with a Python dependency.