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Why Go Local AI

Choose models against your own workloads and quality requirements. Keep your existing tools, and reassess the setup as your needs change.

Other optionsWhat they doWhat Go Local AI adds
AI gatewaysStatistical or rules-based routing, failover, governance and load balancing.A classifier that understands your work and intelligently reduces costs within your quality requirements and data boundaries. Your gateway still handles the requests.
AI FinOps & observabilityCost attribution, performance monitoring and optimisation recommendations.Tests on your real workloads provide actionable information on model and infrastructure choices, with ongoing routing and reassessment to reduce costs in production.
One-off consultancyBespoke assessment and implementation.Software that continues assessing and optimising after deployment, without having to pay for repeated audits.
Cloud AI & model providersAccess to their models and APIs.Comparison across cloud and private models to find a suitable option for each workload. Keep your current provider where it fits.

Know what your
AI uses.

A provider’s average per-query figure cannot tell you the impact of your own workload.

For on-premise and private-cloud deployments, we use available energy telemetry to estimate operational carbon. Hosting with us uses verified renewable power.

How we report environmental impact

Use what you need.

Licence one engine or the full platform. Run it on your infrastructure, in private cloud or with managed hosting.

Find your starting point