Inside the Skytells Console — One Control Plane for AI, Infrastructure, and Production Operations

Inside the Skytells Console — One Control Plane for AI, Infrastructure, and Production Operations

April 13, 2026 — When we started building the Skytells Console, we didn't set out to build a dashboard. We set out to solve a problem that every engineering team working with AI hits at some point: the tooling sprawl.

You run inference through one provider's API. You deploy applications on a separate platform. You manage cloud instances through yet another interface. Your databases live somewhere else. Observability is a different product entirely. Before long, you have six tabs open, four sets of credentials, three billing relationships, and no single place that tells you the state of your production systems.

The Skytells Console is our answer to that fragmentation. It's a unified control plane where engineering teams manage AI inference, application deployments, GPU and cloud infrastructure, enterprise network design, database operations, and production monitoring — in one place, under one account, with one set of access controls.

The Cost of Fragmented Tooling

This isn't an abstract problem. It has measurable consequences.

When your inference API, deployment platform, and monitoring system are separate products, you're dealing with separate permission models, separate billing, separate audit logs, and separate incident response workflows. If something goes wrong — a spike in inference latency, a deployment that introduced a regression, a database connection pool that's exhausted — your team has to correlate data across three or four different systems to understand what happened.

That correlation step is where time gets wasted. Not in the fix itself, but in the investigation. Every context switch between tools adds friction. Every separate login adds a security surface. Every separate billing report makes cost attribution harder.

The console exists to collapse those boundaries. One login. One permissions system. One billing dashboard. One place to investigate an incident from inference through deployment to infrastructure.

50+ AI Models, One Inference Endpoint

The console gives teams access to over 50 AI models spanning text generation, image generation, audio, and video — from providers including OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Cohere, Groq, xAI, Perplexity, Black Forest Labs, Amazon Bedrock, Azure, Cerebras, and Skytells' own foundation models like TrueFusion.

What makes this different from just having a lot of integrations: every model is accessible through a single, unified API endpoint.

Your application code doesn't change when you switch from one provider to another. The endpoint is the same. The authentication is the same. The request format is the same. This matters for several practical reasons:

Fallback and load balancing. The unified endpoint supports automatic failover. If your primary model provider experiences degraded performance, requests route to a secondary provider without code changes or redeployment. This is the kind of resilience that would take weeks to build if you were integrating each provider separately.

Provider independence. Your application logic isn't coupled to a single vendor's SDK. If you start with one provider and later find another offers better latency or pricing for your workload, the switch is a configuration change — not a code rewrite.

Consistent logging. Every inference call — regardless of provider — is logged with the same structure: full input and output, token counts, latency measurement, and cost breakdown. You don't need to normalize data across three different logging formats to understand your AI spend.

Built-In Playgrounds

The console includes interactive playgrounds for text, image, audio, and video models. These aren't toys. They're a working environment where engineers can test prompts, compare model outputs side by side, tune temperature and sampling parameters, and export working code when they're ready to integrate.

This shortens the gap between experimentation and production. A prompt that performs well in the playground can be moved to your application code in minutes — not after a separate prototyping phase with a different toolset.

Bring Your Own Keys

Not every organization wants to route all inference through Skytells' accounts. The console supports a bring-your-own-key model: store your existing provider API keys with AES-256-GCM encryption, and use your own contracts with the providers directly. Zero markup. Your negotiated rates, your quotas, your agreements — just managed through a single interface.

Application Deployment from Git

The second major surface of the console is application deployment. Connect your GitHub repository, and the console handles the rest: build pipeline, managed infrastructure, environment variables, custom domains, and SSL.

This is Git-based deployment done seriously:

• Push to deploy. Commits to your configured branch trigger automated builds and deployments. The build pipeline detects your framework, installs dependencies, runs the build, and deploys to managed infrastructure.
• Environment variable management. Configure environment variables at the project or application level. Variables are encrypted at rest and injected at build and runtime. Sensitive values never appear in build logs.
• Custom domains and SSL. Add custom domains with automatic SSL provisioning. DNS validation, certificate issuance, and renewal are handled automatically.
• Instant rollbacks. Every deployment produces an immutable artifact. Rolling back to a previous deployment is a single action — no rebuild required.

The practical value here is that teams don't need a separate CI/CD platform, a separate hosting provider, and a separate domain management tool. The build, deploy, and run lifecycle lives inside the same console where you manage inference and infrastructure.

Global GPU and Cloud Infrastructure

The console is the management interface for Skytells' global infrastructure: over 9,300 GPUs — including 2,100 NVIDIA H100s and 4,400 A100s — deployed across 24 Tier 3 data centers in 30+ locations worldwide.

Engineering teams use the console to provision and manage compute resources across regions:

From the console, you can:

• Provision GPU instances — Select an H100, A100, or A40 plan, choose a region, configure networking (VPC, firewall groups, IPv6), and deploy. The instance is available within minutes.
• Manage lifecycle — Start, halt, reboot, and destroy instances. Each action has proper confirmation guards in the UI.
• Monitor resource utilization — See CPU, memory, GPU utilization, and network metrics in real time.
• Scale on demand — Spin up additional instances for training runs, tear them down when complete. GPU infrastructure treated as ephemeral, not permanent.

For teams working on machine learning, this means the training cluster for your next model and the inference fleet serving your production API are managed through the same interface — with the same access controls, the same billing, the same audit trail.

Edge Computing

Not every workload belongs in a centralized data center. The console provides access to Skytells' edge network, which pushes compute closer to end users for latency-sensitive applications.

Edge deployments are managed through the same console interface. Configure an application for edge distribution, select target regions, and the platform handles replication, routing, and cache invalidation. This is particularly relevant for:

• Real-time inference applications where every millisecond of latency affects user experience
• Content delivery for media-heavy AI applications with global user bases
• Data residency requirements where certain workloads must execute within specific geographic boundaries

The edge layer integrates with the same monitoring and logging infrastructure as centralized deployments, so there's no visibility gap when you move workloads to the edge.

Enterprise Network Design

For larger organizations, the console includes network design capabilities that go beyond individual instance management:

VPC (Virtual Private Cloud) configuration. Create isolated network environments for your workloads. Resources within a VPC communicate over private network paths — no traffic traverses the public internet.

Firewall groups. Define inbound and outbound rules that apply to instances and services. Firewall configurations are managed as named groups, so the same security posture can be applied consistently across multiple resources.

Project-scoped access keys. Each project can have its own set of access keys (prefixed sk_proj_), restricting all operations — API calls, CLI commands, deployments — to a single project boundary. A compromised key cannot access resources in other projects.

Role-based access control. Team members are assigned roles with specific permissions. Billing access, deployment access, and infrastructure access are separable. An engineer who deploys applications doesn't necessarily have access to billing data or infrastructure teardown actions.

Audit logging. Every action taken through the console — every deployment, every instance provisioned, every environment variable changed — is logged with the actor, timestamp, and action details. For regulated industries (healthcare, finance, government), this audit trail is not optional — it's a compliance requirement.

These aren't features bolted on after the fact. The console was architected around multi-tenant, project-scoped isolation from the ground up. This is how enterprise teams operate safely at scale.

Production Monitoring and Observability

Deploying is half the job. The other half is understanding what your systems are doing in production. The console integrates real-time monitoring across every layer:

Inference Monitoring

Every API call to the unified inference endpoint is tracked with:

• Full request and response payloads
• Token counts (input and output)
• End-to-end latency measurement
• Cost per prediction, broken down by model and provider
• Error rates and status codes

This data is searchable, filterable, and exportable. Teams use it for cost optimization (identifying which models offer the best cost-to-quality ratio for specific tasks), debugging (replaying exact inputs that produced unexpected outputs), and capacity planning (understanding usage patterns over time).

Application Monitoring

For deployed applications, the console provides live dashboards showing:

• CPU and memory utilization
• Error rates and exception tracking
• Response time distributions
• Security threat detection

AI-Powered Diagnostics

The monitoring layer doesn't just collect data — it analyzes it. Anomaly detection surfaces unusual patterns in your metrics: a sudden increase in error rate, an unexpected change in latency distribution, a deviation from normal resource consumption. These alerts reach you before your users notice a problem.

This is where the value of a unified console becomes most tangible. When an anomaly is detected in your inference layer, you can immediately check whether a recent deployment correlates with the change, inspect the infrastructure metrics for the region handling those requests, and review the specific predictions that are showing unusual behavior — all without leaving the console.

Cost Visibility and Control

Cost management in AI workloads is uniquely challenging. Inference costs vary by model, by token count, by provider. Infrastructure costs vary by region, by GPU type, by utilization. When these are spread across separate billing systems, getting a clear picture of total cost of ownership is a quarterly accounting exercise.

The console provides:

• Per-prediction cost tracking. Every inference call shows its cost, broken down by model and provider. Roll up to daily, weekly, or monthly views.
• Infrastructure cost attribution. GPU instance costs attributed to the team, project, or workload that consumed them.
• Project-level billing. Each project has its own cost center. Teams can see exactly what their project costs — inference, infrastructure, storage, bandwidth — without cross-contamination from other projects.
• Bring-your-own-key cost pass-through. When using your own provider keys, inference costs flow through your existing provider agreements. The console provides visibility without adding markup.

For organizations running AI at scale, this level of cost granularity is the difference between "AI is expensive" and "we know exactly where our AI budget goes and which workloads justify their cost."

CLI and API Integration

The console is not the only way to interact with Skytells. Every capability exposed in the console is also available through the Skytells API and the Skytells CLI.

The relationship between these interfaces is intentional:

• Console for visual management, exploration, and ad-hoc operations
• CLI for terminal-native workflows, scripting, and CI/CD integration
• API for programmatic access and custom automation

All three use the same authentication system, the same project-scoped access controls, and the same underlying API surface. What you can do in the console, you can automate through the CLI or API.

Who This Is For

The console is designed for teams that are past the experimentation phase and are operating AI in production — or preparing to.

If you're a startup shipping an AI-powered product each week and need inference, deployment, and monitoring under one roof, the console does that. If you're an enterprise with compliance requirements, multi-team access controls, and cost attribution needs, the console does that too. If you're a research team that needs GPU instances for training runs and a place to deploy inference endpoints, it's the same console.

The common thread isn't company size or industry. It's operational maturity. The console is for teams that understand that the real challenge in AI isn't building a demo — it's running a production system reliably, economically, and securely over time.

Getting Started

The console is available at console.skytells.ai. Creating an account is free, and you can start running inference in under two minutes. No credit card required.

For documentation and integration guides, visit Skytells Learn. The API reference covers every endpoint available through the platform. The CLI is installable via npm.

If you're evaluating the platform for enterprise use, our team is available through the contact page to discuss your requirements, security posture, and integration needs.

About Skytells

Skytells has been building AI systems, foundation models, and production infrastructure since 2012. The platform powers production systems for over 15,000 organizations across technology, healthcare, finance, and government. With offices in Venice, California and Singapore, Skytells provides enterprise AI solutions, managed GPU infrastructure, edge computing, and the tools that engineering teams need to ship AI products with confidence. Learn more at skytells.ai.