Cloud AI: How Mayura Compares

What Is Edge-First AI, and How Does It Differ From Cloud?

Cloud AI runs your requests on remote servers operated by vendors like AWS, Azure, and Google. You send data out, the vendor runs the model, and you pay for what you consume. Edge-first AI runs open models on hardware you own, inside your network. Those models carry the continuous pipeline work: enriching, classifying, and summarizing the items that arrive, and answering search over them. They are a smaller class than the closed frontier, and the cloud stays optional for the tasks that call for it.

Mayura AI is Continuous Intelligence infrastructure that watches the RSS and news feeds you follow, the inboxes you point it at, the web pages that matter to you, and the PDFs they link to, around the clock on hardware you own. It builds durable understanding from what it reads and delivers what it finds without waiting for someone to type a prompt. That is edge AI and sovereign AI, and it is infrastructure you run.

Local models carry the routine, continuous work. When a task demands frontier model quality, Mayura routes that one request to a cloud API, under budget controls you configure. You decide the boundaries, and the deployment is yours alone, with no other organization on it.

How Should You Evaluate Cloud AI and Edge-First AI?

Weigh the two approaches on the terms you will still be living with once the volume is real and the data is sensitive.

Dimension	Cloud AI	Edge-First (Mayura)
Cost model	Metered per token or per call by default. Committed-capacity SKUs exist: Azure provisioned throughput holds capacity and bills per PTU-hour whether or not requests are being made, and it is sized for sustained production volume.	Fixed license plus hardware for local work. Cloud routing for frontier tasks runs under budget controls you configure.
Data location	Vendor's servers by default, and in your VPC still means their hardware and their terms. On-premises and air-gapped options exist upmarket, including Gemini on Google Distributed Cloud.	Sensitive data stays on hardware you own. Cloud routing stays available under budget controls for tasks that need a frontier model.
Processing model	Request-response. Batch and scheduled jobs exist, and each one still runs when something invokes it.	Continuous and always-on. Runs 24/7 locally and routes to the cloud for complex reasoning when the task warrants it.
Scalability	Burst capacity on demand. Pay more for more.	Local hardware carries the baseline load. Cloud overflow absorbs bursts without permanent infrastructure spend.
Auditability	Varies by provider. Retention windows, log formats, and replay are the vendor's product decisions.	Every insight traces back to its source, and system state rebuilds from the stored history.
Setup time	Minutes to a first API call. A production pipeline is a project, and that work is yours.	The pipeline runs from the first day, against the sources you configure during setup.

How Mayura Compares

Mayura takes an edge-first, cloud-optional approach. A Continuous Intelligence engine runs on your hardware and processes data streams around the clock, routing to cloud APIs when a task warrants it.

Flat Economics for continuous operation. Metered pricing is the default across cloud AI, and the committed-capacity SKUs are priced for sustained, high-volume use. Where a flat tier exists, it is a subscription, and published usage limits do the rationing. A vendor whose revenue tracks consumption has little reason to build a mid-market flat-cost always-on product. On hardware you own, running it harder does not cost more, bounded by the throughput of the machine you own, a capacity you chose and can expand.

Local models by default. Mayura runs small open models directly on your hardware for the routine, continuous work. Routine tasks make no API call, so no inference provider receives that data.

Cloud routing for frontier tasks. Some tasks genuinely need a frontier model. Mayura routes those to cloud APIs under budget controls you configure, so most of the work finishes locally at a fixed cost, and the cloud routing you turn on is metered by that provider.

Auditability you can exercise. Every piece of data ingested, every enrichment applied, and every insight generated is recorded. Trace any output back to the source that produced it, and rebuild system state from the stored history.

When Edge-First Fits

Edge-first AI makes sense when your organization:

• Processes high volumes continuously. Hundreds or thousands of documents, emails, or data points daily. Metered cloud costs rise with every item processed. Local processing bills the same either way, bounded by the throughput of the machine you own.
• Handles sensitive or regulated data. When legal, compliance, or regulatory requirements prevent data from leaving your network, edge-first AI keeps sensitive processing on hardware you own and routes non-sensitive tasks to the cloud when you choose. Where your checklist also names a certification, the private LLM comparison covers how that question lands.
• Needs budget predictability. When usage-based billing makes the AI line item move with volume, an edge-first architecture with budget-capped cloud routing puts that cost in front of your CFO on day one.
• Is tired of maintaining DIY scripts. If your team already validated local AI with open-source model runners and cron jobs but needs production infrastructure, Mayura replaces the maintenance burden. Local and open-source AI is the comparison written for that stack.

If that describes the work in front of you, request an Opportunity Audit and we will map which of your data streams belong on hardware you own first.

When Cloud-Only Fits

• Low, bursty volume, where dedicated hardware does not pay for itself.
• Experimenting with AI use cases, where cloud's low entry cost lets you validate ideas before committing to infrastructure.
• No data sensitivity constraints and comfort with vendor terms of service.

Which Approach Should You Choose?

Start with the shape of the work. Occasional, bursty, low-sensitivity tasks belong in the cloud. Continuous, high-volume work on data you cannot hand to a third party is where both the economics and the compliance position change, and that is the work Mayura is built for: open models running on hardware you own, a local cost that does not move with volume, bounded by the throughput of the machine you own, and every insight traceable back to the source that produced it.

Compare the Options

• Sovereign AI and Mayura: How Mayura's continuous, on-premises processing compares with sovereign model providers such as Mistral, Cohere, and Together AI.
• Private LLM and AI Ownership: How a private or self-hosted LLM deployment compares with a continuous system running on your own hardware.

Learn More

• Solutions: See which problem Mayura solves for your organization
• Platform: Explore the technical architecture behind Mayura's edge-first approach
• Talk to us about an Opportunity Audit: a scoped look at your data streams and what continuous processing would cost on hardware you own