The inference engine that should have been Rust from the start. Serve any model, on any hardware, with governance that vLLM cannot touch.
The inference engine that should have been Rust from the start. Serve any model, on any hardware, with governance that vLLM cannot touch.
Single 50MB binary. Zero Python. Zero GIL. Lock-free ring buffers. Schedules inference in under 100 microseconds. Drop-in replacement for vLLM with OpenAI-compatible APIs.
NVIDIA, AMD, Intel, Apple Silicon, Google TPU, AWS Trainium, Cerebras WSE, Tenstorrent, SambaNova, Groq LPU, and FPGA — all from one codebase. No vendor lock-in. No hardware religion.
Per-tenant isolation with cryptographic cache salts. Nanosecond-precision audit trails via causal audit. Authentication-enforced model and tool access. Every prediction is traceable, governable, and billable through XRP metering.
6x KV cache compression at zero accuracy loss. Semantic caching that matches prompts by meaning via hyperdimensional computing. Voronoi-geometric memory layout optimized for GPU access patterns. Speculative decoding with multi-draft routing.
100,000+ concurrent users. Agent-aware scheduling that understands multi-agent DAGs. Disaggregated prefill and decode. Cross-node KV cache transfer via RDMA and CXL. Graceful degradation under load — never drop a request.
Distributed training across 10,000+ GPUs. Document intelligence with per-entity confidence scoring. Active learning that retrains from human correction. Neural architecture search. Multi-modal fusion. Federated learning with differential privacy. Visual computing with synthetic data generation.
Bidirectional integration with 14+ Babadook products. knowledge graph knowledge-grounded RAG. Robotics at sub-200ms planning. causal audit trails. XRP billing. connector mesh. Not just an inference server — the AI node in a governed product mesh.
Prometheus is a Rust-native model serving engine built to replace vLLM in production. Zero Python in the hot path. Zero GIL contention. Zero memory-unsafe CVEs. It serves Transformer, SSM, hybrid, vision-language, and embedding models across 26 hardware targets — from a single RTX 4090 to a 10,000-GPU cluster — with OpenAI-compatible APIs, structured output, tool calling, speculative decoding, continuous batching, and per-tenant isolation that actually works.
Under the hood, Prometheus runs lock-free SPSC ring buffers instead of ZMQ IPC, schedules requests in under 100 microseconds, compresses KV cache by 6x without accuracy loss, and matches prompts by meaning — not just exact tokens — using hyperdimensional computing. It deploys as a single 50MB binary and connects to every major accelerator: NVIDIA, AMD, Intel, Apple Silicon, Google TPU, AWS Trainium, Cerebras, Tenstorrent, SambaNova, Groq, and FPGA.
Beyond inference, Prometheus runs the full ML lifecycle: distributed training across 10,000+ GPUs, document intelligence with confidence-scored extraction, active learning that retrains from human feedback, automated architecture search, multi-modal fusion, edge deployment, and autonomous agent orchestration. It is bidirectionally integrated with every Babadook product — knowledge graph grounds every prediction in a verified knowledge graph, causal audit records every inference with nanosecond-precision audit trails, and Authentication enforces tenant isolation at the cache-salt level. This is inference that can be governed, audited, and billed.
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.
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.
Compresses the KV cache by 6x at 3-bit precision with zero accuracy degradation on LongBench, RULER, and Needle-in-a-Haystack. Runs within 2.7x of the Shannon lower bound — mathematically provable, data-oblivious, no calibration needed. Every token of context you add costs less RAM than the competition, which means longer contexts, larger batches, and fewer GPUs.
NVIDIA H100, AMD MI300X, Google TPU v5p, AWS Trainium, Cerebras CS-3, Tenstorrent Blackhole, SambaNova SN40L, Groq 3 LPU in AFD co-processing mode, AMD/Xilinx FPGA for sub-5μs deterministic inference, Qualcomm Snapdragon at the edge, Apple Silicon on developer workstations, and every x86 CPU with AVX-512 and AMX tile operations.
This is not a compatibility matrix — it is a hardware abstraction layer built on six compute paradigms: BatchParallel, WeightStreaming, ExplicitDataflow, SpatialDataflow, CompositionOfExperts, and ReconfigurableCompute. The same inference engine that streams weights through a Cerebras WSE at 600 GB/s also maps a spatial dataflow onto a SambaNova RDU at compile time, and decomposes attention and FFN across GPU and Groq LPU in a single decode step.
Every backend ships with a constraints table. If TPU static shapes or Neuron's missing all-to-all primitive would break your deployment, you know before you provision. We do not sell hardware fantasies — we ship verified paths.
In production AI, the question is never 'can the model generate text?' It is: which model served which answer, to which tenant, under which policy, using which adapter, with which retrieval context, within which quota, and where is the audit trail?
Per-tenant isolation at the cache-salt level. Authentication-managed with cryptographic KV cache salts prevents cross-tenant information leakage — including timing side-channel attacks that other inference engines do not even model.
Nanosecond-precision audit. causal audit records every inference event with causal ordering. Every prediction, every model switch, every adapter selection, every tool dispatch — cryptographically verifiable, compliance-ready from day one.
Billing that traces to the token. XRP per-request token accounting with per-tenant quota enforcement and 429 gating. If you sell AI, you bill it. Prometheus makes that possible without building a metering layer yourself.
knowledge graph grounds every retrieval in a verified knowledge graph with entity resolution, disambiguation, temporal reasoning, and provenance tracking. When a healthcare model references a drug interaction, it does not retrieve a similar-looking vector — it traverses a graph node with FDA approval status, contraindication data, and temporal validity. When a financial model cites a regulation, it references the specific regulatory paragraph, not a LLM-hallucinated summary.
This is not naive vector search with a confidence score slapped on top. It is graph-grounded retrieval with disambiguation, ontology constraints, and full chain-of-custody for every fact the model uses. The model does not own truth — it references governed truth, and every reference is traceable.
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.
3D parallelism across 10,000+ GPUs. ZeRO-3 with 90%+ scaling efficiency. Fault-tolerant with sub-5-minute node failure recovery. Elastic scaling — add or remove GPUs during training without restarting. Federated learning with differential privacy for multi-party training without data sharing.
OCR, layout analysis, NER, and relationship extraction with per-entity confidence scores — not black-box extraction. Low-confidence entities route to review queues. Human corrections feed back into training. This is document understanding that gets smarter every time someone uses it.
Uncertainty, diversity, and hybrid sampling strategies. Human corrections captured with full provenance. Incremental retraining with catastrophic forgetting prevention. A/B testing with Thompson Sampling bandit optimization and automatic rollback on degradation.
Same binary runs on H100 clusters, Jetson edge devices, and Snapdragon mobile. Model compression down to 10MB. Sub-50ms inference. Offline buffering that syncs when connectivity returns. VLA robots at sub-200ms planning. safety-bounded physical action with four truth regimes for evidence provenance.
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.
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.
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.
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.
Most production incidents are preventable. Prometheus catches them before they cost you revenue. Events flow from detection through auto-mitigation to escalation — only a tiny fraction reach revenue impact.
The earlier you catch an incident, the cheaper it is to fix. Auto-mitigation handles the majority. Your team handles the rest. Revenue impact is the exception, not the rule.
Prometheus serves 20 industries — each with different compliance, scale, and latency requirements. Finance and healthcare lead adoption. Aerospace and manufacturing follow. Every industry gets the same engine, the same inference quality, and the same governance.
No industry is a second-class citizen. No industry gets a stripped-down build. One binary serves all.
Mean time to detect vs mean time to resolve. Lower is better — Prometheus compresses both. Industry average MTTD is 45 minutes. Prometheus detects in under 3. Industry average MTTR is 120 minutes. Prometheus resolves in under 8.
The difference between 45 minutes and 3 minutes is the difference between a brief glitch and a customer-facing outage.
Specific operational pain this product is designed to resolve.
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.
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.
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.
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.
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 running in production. And when a paper claims state-of-the-art, nobody can reproduce it because the serving infrastructure does not match the training infrastructure.
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.
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 running in production. 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.
Prometheus is a drop-in vLLM replacement with enterprise governance, 26 hardware targets, and novel compute primitives that no other inference engine ships.
Rust-native serving engine. Continuous batching with lock-free ring buffers. OpenAI-compatible API. Structured output. Tool calling. Speculative decoding. Multi-LoRA. PagedAttention with Copy-on-Write. SSM state pool. Pushdown automaton jump-forward decoding. All Transformer, SSM, and hybrid model families.
NVIDIA CUDA native. AMD ROCm native. Intel AMX CPU. Apple Silicon MPS. Google TPU XLA bridge. AWS Trainium Neuron bridge. ONNX Runtime universal bridge. Constraints tables per backend. Hardware-aware scheduling. Verified paths, not marketing claims.
TurboQuant 6x KV compression at zero accuracy loss. HDC semantic cache with hypervector meaning-matching at 250M comparisons/sec/core. Voronoi geometric memory layout for GPU cache locality. Multi-draft speculative routing. Self-speculation with draft-model bypass.
Disaggregated prefill and decode across independent GPU pools. KV cache transfer via RDMA and CXL. Agent-aware scheduling with DAG topology tracking. Workflow-aware KV eviction. 100K+ concurrent users. Graceful degradation at five levels. Raft consensus control plane. No Kubernetes dependency.
Authentication with cryptographic cache salts. Nanosecond-precision event tracking. XRP per-token billing. Knowledge-grounded RAG. Supply-chain weight verification. Sandboxed Python bridge. SSRF-resistant tool dispatch. Air-gapped deployment. Encrypted models at rest.
Distributed training at 10,000+ GPU scale. Document intelligence with confidence-scored extraction. Active learning with human feedback loops. Neural architecture search. Multi-modal fusion. Federated learning with differential privacy. Edge deployment with sub-50ms inference and offline buffering.
A Rust-native inference stack from socket to salt