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Platform · Deployment

Start with SaaS. Scale
to your own cloud.

Launch in days on low-cost SaaS plans. When compliance, data control, or infrastructure ownership requires it, the same platform — same policies, same roles, same audit history — deploys into your own cloud account. Adoption never has to restart to grow up.

Book a strategy call Explore SaaS plans

Why a path, not a choice

Most companies are forced to pick: move fast, or own your data. The honest answer is a sequence.

Start

SaaS is how adoption actually starts: no infrastructure project, no procurement epic, a first team live in days. For most companies most of the time, a well-run SaaS deployment with strong contractual and technical controls is the right answer — and the fastest way to stop the ungoverned usage already happening.

The line

But some situations genuinely demand more: regulated data that must stay in-country, sector rules that require infrastructure ownership, security postures (public sector, healthcare, defense-adjacent) where "our cloud" is non-negotiable. The failure mode is having to switch platforms — and re-teach every team — when you cross that line.

The answer

Leapforce is built so the line is a deployment detail, not a migration. The control plane you configure on SaaS — roles, policies, registries, budgets — is the same one that ships into your cloud. Employees notice nothing; auditors notice the residency change; nobody rebuilds anything.

Deployment models

Four ways to run the same platform.

Leapforce SaaS

Fully managed, multi-tenant with strict isolation. The default starting point.

  • Live in days, no infrastructure
  • Regional hosting options for residency
  • Always the newest release
  • Encryption in transit and at rest

Dedicated SaaS instance

Single-tenant, isolated per client — still hosted and updated by us.

  • Your own isolated data plane
  • No shared tenancy with other customers
  • Regional placement per client
  • Same release train, managed by us

Enterprise cloud (yours)

The platform deployed into your AWS, Azure, or GCP account.

  • Data never leaves your tenancy
  • Adopts your security posture and controls
  • Local models on your own GPUs
  • Managed updates, optional and reviewed

Managed enterprise plan

Either deployment, plus hands-on operation and agent service.

  • Priority support, direct line
  • Managed agent setup and tuning
  • Custom connectors built on request
  • Ongoing cost and policy optimization

Multi-region and per-entity instances are supported for groups with subsidiaries or strict data segregation between business units.

Decision guide

Which deployment fits which situation.

Your situation Fit Why
SMB or first teams adopting AI SaaS Speed and cost matter most; governance arrives without an infrastructure project. EU entity with residency requirements SaaS, regional Regional hosting plus masking and local-model routing usually satisfies counsel. Regulated data that must not leave Your cloud Platform and local models run inside your tenancy; the record stays home too. Public sector / strict segregation Your cloud, per-entity Separate instances per entity or region keep tenants and jurisdictions apart. No internal AI ops team Managed plan We run the platform and stand up your first coworkers with you, on either deployment.

The move, when it comes

Moving from SaaS to your cloud is a project measured in weeks, not a re-platforming.

Step 1 · Provision

The platform deploys into your cloud account with infrastructure-as-code, inside your network boundaries and under your monitoring.

Step 2 · Carry over

Roles, policies, connector registry, agents, and audit history export from SaaS and import into your instance — configuration is portable by design.

Step 3 · Repoint

The gateway URL swaps behind DNS; tools and agents follow automatically. Employees keep working — most never learn the migration happened.

Step 4 · Verify

Residency, retention, and access controls are re-verified in place, and the evidence pack updates to reflect the new topology for your auditors.

Questions we hear

Deployment — frequently asked.

The major public clouds — AWS, Microsoft Azure, and Google Cloud — with infrastructure-as-code delivery so your platform team can review exactly what runs. Constrained or sovereign environments are scoped case by case on the managed plan.

Your choice. Your team can operate it with our runbooks, or the managed plan includes operation by us inside your tenancy — updates proposed, reviewed by you, then applied. Either way the infrastructure account is yours.

SaaS updates roll continuously with change notes. In-cloud deployments update on your cadence: releases are versioned, changelogged, and applied in maintenance windows you control — pinning a version for a review cycle is normal.

On GPU capacity inside your deployment — your cloud tenancy or connected on-prem hardware. Policy pins sensitive data classes to those models, so the privacy guarantee is placement, not promises.

Yes — per-entity or per-region instances with separate data planes are supported for groups that need hard segregation, with group-level reporting where governance allows it.

Configuration, registries, and audit records export in documented formats on request, and contractual deletion timelines apply to what remains. In your-cloud deployments the data was in your account all along.

SaaS plans are priced to make starting easy for small businesses; enterprise cloud and the managed plan are scoped to your footprint on a strategy call. Model usage remains transparent and attributable either way.

The platform is built with EU AI Act, ISO/IEC 42001, NIST AI RMF, and SOC 2 expectations in mind, and evidence exports map to those frameworks. Formal certification status is disclosed transparently on request as the program progresses — we'd rather be precise than impressive.