Platform
Unified gateway for every enterprise LLM workload
Reference architecture
ForgeCrux Enterprise AI Gateway
Unified governance, security, and multi-model integration for agentic AI — from client ingress through orchestration to enterprise systems.
ForgeCrux Enterprise AI Gateway Architecture
Traffic ingress, policy, orchestration, and observability in one control plane
Client applications
Fully managed (SaaS)
SaaS control + cache at the edge
Hybrid
SaaS control plane, on-prem gateway — SaaS ↔ on-prem
Self-hosted
On-prem / private cloud Kubernetes
ForgeCrux Enterprise AI Gateway Core
Traffic ingress layer
Reasoning engine
Multi-agent coordination
Tool use & execution
Contextual memory
Transformation & optimization
- Input/output normalization
- Prompt optimization
- Context synchronization
Security & data privacy
- Data masking & PII protection
- RBAC (model level)
- DLP, secrets, encryption
Agent plan & orchestration
- Reasoning coordination
- Tool use & execution
- Contextual memory
Governance & compliance
- Performance monitoring
- Audit & versioning
- Model monitoring & cost allocation
External agents & tool registry
Reused and expanding
Agent orchestration context bridge
Contextually bridging models
Gateway integrations & observability
Data & model sources
Commercial & open-source LLMs
Azure, AWS, GCP, OpenAI, Anthropic, self-hosted
Agent & tool registry
Enterprise databases
Vector, SQL
Legacy systems
Observability plane
Traces, tokens, cost
Unified governance, robust security, optimized multi-model integration for agentic AI
Version 2.0.1
One API, every model
Applications keep a single ForgeCrux endpoint while you swap, ensemble, or fail over providers without code changes.
Cost, latency, and quality control
Route each prompt to the best model, cache repeated work, and enforce token and dollar budgets per team.
Enterprise-grade AI security
Guardrails, PII detection, prompt security, and immutable audit logs on every request and response.
Key Capabilities
Complete AI Gateway capabilities
Everything required to publish, secure, mediate, observe, and operate ai gateway workloads on ForgeCrux.
Multi-model access and routing
Connect once. Route every completion, embedding, and multimodal call intelligently.
- Unified OpenAI-compatible and native provider APIs
- Chat, completions, embeddings, images, audio, and vision
- Providers: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Mistral, Cohere
- Self-hosted: vLLM, TGI, Ollama, NVIDIA NIM, custom OpenAI-compatible endpoints
- Cost-, latency-, quality-, region-, and health-based routing
- Weighted load balancing and sticky sessions for conversations
- Automatic fallback chains and circuit breakers per model
- Canary and shadow traffic to evaluate new models
- Context window matching and automatic model upgrade/downgrade
- Streaming SSE/WebSocket with cancellation and timeout control
Prompts, tokens, and cost
Treat prompts and spend as first-class production assets.
- Prompt catalog with versions, labels, and rollback
- Prompt templates, variables, and localization
- A/B and multivariate prompt experiments
- Interactive playground with policy preview
- Token counting, reservation, and streaming usage
- Spend caps by organization, team, app, user, and model
- Chargeback and showback reports
- Semantic cache and exact-match cache for repeated prompts
- Context compression, summarization, and truncation policies
- Batch inference, async jobs, and priority queues
Guardrails, safety, and governance
Block unsafe, non-compliant, or out-of-policy AI traffic in real time.
- Prompt-injection and jailbreak detection
- PII, PHI, PCI, and secrets detection on input and output
- Toxicity, hate, self-harm, and sexual-content filters
- Allowed/denied topic and competitor policies
- Groundedness and hallucination checks against retrieved context
- Output schema validation and structured JSON enforcement
- Data residency, region pin, and provider allow lists
- RBAC/ABAC on models, prompts, and playgrounds
- SSO, OIDC, API keys, and workload identity
- Immutable audit logs of prompts, completions, and policy hits
RAG, tools, and application patterns
Production patterns for retrieval, function calling, and multimodal apps.
- Function calling and parallel tool calls with schema validation
- Retrieval connectors: vector DBs, search, files, and knowledge bases
- Chunking, embedding, and rerank pipelines behind the gateway
- Citation and source-attribution policies
- Multimodal routing for images, documents, and audio
- Conversation memory with TTL, isolation, and redaction
- Rate limits, concurrency limits, and fair-share scheduling
- Content filters aligned to industry and internal policies
Evaluation, quality, and observability
Know whether models are accurate, fast, and worth the cost.
- Online evals: relevance, faithfulness, toxicity, and user feedback
- Offline eval datasets, golden sets, and regression gates in CI
- Model quality scorecards and drift detection
- Latency percentiles, TTFB, tokens/sec, and error budgets
- Token, cost, and cache-hit dashboards
- Full traces: prompt, retrieved docs, tools, and completion
- OpenTelemetry export to Grafana, Datadog, and Prometheus
- Alerts on cost spikes, quality drops, and policy violations
Platform, SDKs, and runtime
Drop into existing apps and deploy wherever the enterprise runs.
- Python, TypeScript, Java, Go SDKs and REST APIs
- Drop-in OpenAI client base URL with ForgeCrux auth
- Kubernetes operator, Helm, Terraform, and GitOps
- VPC, on-prem, air-gapped, and multi-cloud data planes
- High availability, horizontal scale, and multi-region failover
- Config as code for models, routes, prompts, and policies
- Environments: development, test, staging, production
- Zero-downtime policy and model catalog updates
How teams run AI Gateway on ForgeCrux
Connect models
Register cloud and self-hosted providers, set credentials in the vault, and publish a single OpenAI-compatible endpoint.
Define routes and budgets
Map tasks to model pools with fallback, spend caps, and latency SLOs per team and application.
Attach guardrails
Turn on PII, injection, topic, and output-schema policies before production traffic is allowed.
Version prompts
Move prompts into the registry, run A/B tests, and promote versions through environments.
Evaluate continuously
Score quality online and in CI so model or prompt changes cannot regress silently.
Observe and optimize
Use traces, token analytics, and cache hit rates to cut cost and latency without changing apps.
Related Products
MCP Gateway
ForgeCrux MCP Gateway is the governed fabric for Model Context Protocol: server registry, tool discovery, OAuth, RBAC, credentials, virtual servers, traffic control, and full audit of every tool call.
Agent Gateway
ForgeCrux Agent Gateway gives every agent an identity, permissions, routing, memory controls, guardrails, tracing, evaluation, cost limits, and lifecycle—so multi-agent systems can reach models, APIs, MCP tools, and data without unmanaged autonomy.
Observability
ForgeCrux One Observability is the telemetry plane for APIs, LLMs, MCP tools, and agents. Platform, SRE, security, and FinOps teams ingest logs, metrics, and traces from every gateway hop, then act from one dashboard—maps, alerts, incident tools, and audit replay included.
Ready to get started with AI Gateway?
Talk to our team about deploying AI Gateway in your enterprise environment.