# CloudSigma AI Mission Control > Public, read-only product and developer information. ## Pages - [Enterprise AI Control Plane](https://taas.cloudsigma.com/): Bring model access, usage visibility and policy-based routing together with CloudSigma AI Mission Control. ### The Enterprise AI Control Plane. Manage who can use AI, monitor spending and choose from supported models through one platform — without forcing every team into the same provider or deployment model. #### AI Governance Organise access across organisations, departments and projects. Apply model and region policies at the credential boundary. - Role- and project-aware access - Model, supplier and region controls - Enterprise SSO — discuss identity-provider rollout with our team #### AI FinOps Connect token activity to accountable teams and nominated billing relationships, with usable cost and usage evidence. - Usage and cost visibility - Department, project and key attribution - Hard-budget policies — confirm enforcement scope and rollout during your briefing #### AI Sovereignty Choose supported geographic lanes and keep deployment options open as governance requirements evolve. - EU and supported-country routing - Credential-level location restrictions - Customer-owned GPU integration through assisted onboarding #### AI Optimisation Use explicit model selection or opt in to chat routing informed by model fit, cost and available operational telemetry. - Policy-aware route selection - Supplier health and availability signals - Inspectable choices rather than self-learning authority #### Universal AI Access Connect applications to supported models through documented compatible, native and specialist endpoints. - OpenAI- and Anthropic-compatible lanes - Specialist media and embedding endpoints - Selected direct-provider BYOK lanes with documented limits #### AI Trust is the foundation Identity, policy, observability and evidence sit beneath every pillar. Controls are scoped to the route, region and integration you select. - Least-privilege API keys - Auditable request and usage records - Clear supported, assisted and planned boundaries - [AI control plane platform](https://taas.cloudsigma.com/platform): Explore governance, FinOps, sovereignty, optimisation and universal model access in one enterprise AI control plane. ### One control plane. Five connected disciplines. AI adoption becomes manageable when access, economics, location and routing are governed together. Mission Control gives platform, finance and application teams a shared operating layer. #### Govern AI access Create organisation, department and project boundaries, then issue keys with deliberate model, supplier and region permissions. - Separate workloads by accountable owner - Central administrators with delegated operation - Discuss enterprise SSO rollout and identity-provider requirements #### Make AI economics visible Relate usage to models, keys and teams, with pricing and nominated billing available for operational review. - Current usage and cost breakdowns - Project and department reporting - Hard-budget requirements assessed against each enforcement path #### Control location and infrastructure Route through supported EU or country lanes and enforce allowed regions on credentials. Infrastructure boundaries depend on the selected model lane. - Explicit regional policy - Fail closed when an allowed route is unavailable - Assisted onboarding for customer-owned GPU capacity #### Optimise without surrendering policy Applications can name a model directly or opt in to chat autorouting. Eligibility remains bounded by configured policy and available route telemetry. - Model-fit and cost inputs - Availability-aware routing - Explicit selection policies and inspectable routing metadata #### Use one governed access layer Adopt compatible APIs where they fit and native specialist endpoints where modalities require them. - Documented endpoint coverage - Central key lifecycle - Selected direct-provider BYOK lanes; custody and fallback scope are confirmed during evaluation - [Architecture](https://taas.cloudsigma.com/architecture): See the request lifecycle, policy checkpoints and assisted customer-GPU path behind CloudSigma AI Mission Control. ### Policy first, then route. Mission Control evaluates identity and key policy before selecting an eligible model lane. Available models and locations depend on the selected service lane. #### 1. Authenticate and scope A request arrives through a documented endpoint. The API key establishes its organisation, project and allowed model, supplier and region scope. - Credential validation - Organisation and project context - Endpoint-specific input validation #### 2. Apply policy and select Direct model requests retain the caller's choice. Opt-in chat autorouting considers eligible models using configured constraints and available fit, cost and health signals. - Policy eligibility before routing - Regional and supplier restrictions retained - No routing outside configured lanes #### 3. Execute and observe The request is sent to an eligible hosted, direct-provider or integrated infrastructure lane, then metered for operational and billing records. - Current: supported managed and direct-provider lanes - Assisted: customer-owned GPU onboarding and integration - Response, usage and route evidence returned to the platform - [Partners](https://taas.cloudsigma.com/partners): Deliver governed AI access with reseller controls, branding and assisted infrastructure integration opportunities. ### Build governed AI services on a shared control plane. Cloud and service providers can package model access for their markets while retaining operational oversight and a clear relationship with each customer organisation. #### Operate a branded service Use reseller branding and organisation management to present a coherent customer experience. - Branding for supported reseller surfaces - Organisation and model controls - Portfolio and financial reporting #### Shape the catalogue Define which supported models and lanes are appropriate for your customers rather than exposing every upstream option by default. - Reseller model controls - Customer and organisation boundaries - Published pricing and usage context #### Bring infrastructure Explore customer- or partner-owned GPU capacity through an assisted onboarding process with explicit integration and operational responsibilities. - Manual technical assessment - Routing and observability integration - Assisted setup with agreed operational ownership - [Resources](https://taas.cloudsigma.com/resources): Practical guides for evaluating enterprise AI governance, economics, sovereignty and integration. ### Plan an AI control plane around real workloads. Use these public resources to frame requirements, inspect supported interfaces and prepare a focused technical evaluation. #### Evaluation checklist Start with workload, data, identity and spending boundaries before comparing model output. - List applications and accountable owners - Define allowed providers and locations - Separate current controls from rollout requirements #### Developer paths Review compatible APIs, the guide and agent integration notes before choosing a migration path. - API documentation - Quick-start guide - Agent and discovery guidance #### Trust review Map your required controls to available platform evidence and identify where contractual or assisted review is needed. - Security practices - Control scope - Certification and evidence status - [Book an AI briefing](https://taas.cloudsigma.com/contact): Book a focused CloudSigma AI Mission Control briefing for your workload, controls and deployment requirements. ### Put your AI estate on the map. In a focused briefing, we will connect your workloads, model choices, spending controls and location requirements to supported and assisted Mission Control paths. #### What we will cover Bring a current workload or an evaluation plan. We will keep the conversation specific and identify follow-up evidence where needed. - Workloads and supported model options - Access, spending and hard-budget requirements - Location, customer-owned GPU and integration scope - Evaluation and rollout next steps - [Agents and MCP](https://taas.cloudsigma.com/agents): Discover public AI Mission Control content, models and pricing through read-only MCP and HTTP interfaces. ### Public discovery. Clear boundaries. Use standard API clients for inference and read-only public discovery resources for product and catalogue context. Customer data, credentials and inference are not exposed through discovery tools. #### Choose the inference interface Use a documented compatible or specialist endpoint based on the workload modality. - Explicit model IDs for deterministic selection - Opt-in chat autorouting where appropriate - Project-scoped credentials and restrictions #### Discover public capabilities Agent-facing discovery can retrieve approved product pages, public model fields and pricing without crossing into customer operations. - Streamable HTTP MCP: https://taas.cloudsigma.com/mcp - Public discovery OpenAPI: /api/site/openapi.json - Six read-only tools; no inference or customer-data access - Briefing handoff without lead submission or booking #### Keep tools bounded Treat model calls and operational actions as separate capabilities with their own authentication and approval model. - No credentials in prompts - Validate tool inputs and outputs - Log accountable workload context - [Developers](https://taas.cloudsigma.com/developers): Integrate supported models through compatible and specialist APIs with governed keys, routing and usage evidence. ### One governed entry point for diverse AI workloads. Start with familiar API shapes, then use Mission Control policies to constrain models, suppliers and regions without embedding those decisions throughout application code. #### Start with the API Create a scoped key, select a supported endpoint and make a minimal request using the published model catalogue. - OpenAI-compatible chat and related interfaces - Anthropic-compatible paths where documented - Native specialist endpoints for supported modalities #### Express routing intent Name a model directly or opt in to supported chat autorouting. Credential controls remain authoritative over eligible choices. - Model and supplier allowlists - Region restrictions - Request metadata for accountable workloads #### Operate with evidence Use response metadata, request logs and usage reporting to understand what ran and what it cost. - Usage attribution - Route and status visibility - Graceful handling when no compliant route is available - [Security](https://taas.cloudsigma.com/security): Understand the security boundaries used to protect access to CloudSigma AI Mission Control. ### Security begins with scoped access. Mission Control layers credential controls, tenant boundaries and operational evidence around model access. Exact responsibilities vary by hosted, direct-provider and customer-infrastructure lane. #### Identity and access Keys are scoped to accountable platform entities and can be constrained by model, supplier and region. - Role-aware administration - Credential lifecycle controls - Enterprise SSO requirements reviewed during rollout planning #### Data and routing boundaries Region policies govern eligible request lanes; they should be assessed alongside upstream inference, logging, storage and fallback behaviour. - Supported EU and country routing - Fail-closed eligibility when configured - Lane-specific data-flow review #### Operational assurance Logging and usage records support review without publishing sensitive implementation or vulnerability details. - Auditable administrative and request context - Security disclosures handled privately - Contractual documents remain the authoritative legal terms - [AI controls](https://taas.cloudsigma.com/controls): Map AI access, spending and routing requirements to current and assisted Mission Control controls. ### Turn policy into enforceable boundaries. Controls are most useful when their enforcement point and limitations are clear. Mission Control applies current key and routing controls while qualifying capabilities that require rollout assessment. #### Current platform controls Use roles, organisations, departments, projects and API-key restrictions to establish accountable access boundaries. - Model, supplier and region restrictions - Project and payer context - Usage, pricing and reporting views #### Commercial capability areas Plan enterprise identity, BYOK and hard-budget policies with our team, confirming supported paths and rollout requirements during evaluation. - Enterprise SSO — identity-provider rollout discussed with our team - Selected direct-provider BYOK lanes - Hard-budget policies — enforcement scope and rollout confirmed during briefing #### Assisted infrastructure control Customer-owned GPUs can be integrated through a manual assisted service rather than a self-service enrolment flow. - Architecture and connectivity assessment - Model-serving and route integration - Agreed operational ownership - [Certifications and assurance](https://taas.cloudsigma.com/certifications): Review CloudSigma AI Mission Control assurance evidence without unsupported blanket compliance claims. ### Evidence over badges. A control plane touches several responsibility domains. We map relevant company, platform, infrastructure and upstream evidence to the exact service lane being evaluated. #### Scope matters A certification held by one organisation or facility does not automatically certify every model, provider or customer workload. - Identify the legal and technical service boundary - Separate platform, datacentre and model-provider scope - Confirm current evidence during due diligence #### Review path Use public security and legal materials for initial review, then request applicable evidence under the appropriate disclosure process. - Security and subprocessors information - Data processing and service terms - Private evidence review where applicable #### No blanket claim Mission Control does not present a universal compliance badge. Customers remain responsible for assessing their use case and configuration. - Workload-specific assessment - Region and provider selection - Documented shared responsibilities - [Model pricing](https://taas.cloudsigma.com/pricing): View current public model pricing and understand usage-based CloudSigma AI Mission Control economics. ### Pay for the model usage you select. The live pricing view remains the source for current public rates. Use workload attribution and policy controls to understand spend; enterprise commercial terms are discussed directly. #### Live public rates Published model and schedule data is rendered from the public catalogue with current supported rate units. - Model-specific units - Current schedule context where available - Outcomes depend on workload and selected model - [Public model catalogue](https://taas.cloudsigma.com/models-public): Explore the current public model catalogue available through CloudSigma AI Mission Control. ### Choose from the supported catalogue. The live catalogue remains the authority for supported model IDs, modalities, locations and public availability. If the catalogue is temporarily unavailable, contact our team to discuss your requirements. #### One catalogue Applications, pricing pages and public discovery should consume the same allowlisted source. - Current supported model IDs - Public modality and location fields - Explicit availability state - [API documentation](https://taas.cloudsigma.com/api-docs): Reference CloudSigma AI Mission Control compatible and specialist model API endpoints. ### Integrate against documented interfaces. Existing API documentation remains the detailed source for request schemas, authentication and compatibility. Check endpoint-specific behaviour rather than assuming every upstream feature is identical. #### Compatibility with boundaries Compatible interfaces reduce migration effort while Mission Control adds its own policy and routing semantics. - Use published request schemas - Handle platform error responses - Review specialist endpoint differences - [Developer guide](https://taas.cloudsigma.com/guide): Get started with governed model access through CloudSigma AI Mission Control. ### From scoped key to first request. The existing developer guide remains the practical walkthrough for credentials, model selection and requests. #### A controlled start Begin with a narrowly scoped evaluation key and a model from the current public catalogue. - Choose an accountable project - Set required model and region limits - Verify usage and response metadata ## Agent discovery - [CloudSigma AI Mission Control agent guidance](https://taas.cloudsigma.com/agents) - [Public discovery OpenAPI](https://taas.cloudsigma.com/api/site/openapi.json) - Remote MCP (Streamable HTTP): https://taas.cloudsigma.com/mcp - Briefing handoff: https://calendly.com/d/3x2-m4j-zqv