About us
We are building the quiet control plane behind local-first AI apps.
Fallbakit exists for teams that want their applications to prefer local inference without turning reliability, fallback, and operational trust into bespoke infrastructure work.
One sentence mission
Keep the local path practical, keep fallback dependable, and keep the integration surface boring for application developers.
One OpenAI-compatible endpoint for both local and fallback execution.
Support for Ollama, oMLX, and vLLM local runtimes without changing the application integration.
Observable route decisions with health-aware failover and cost guardrails.
Principles
What guides the product.
Local-first is the default
Private, low-cost, and latency-sensitive traffic should stay close to your own machines whenever the runner is healthy.
Fallback should feel boring
When local cannot serve, the cloud path should recover cleanly without forcing your applications to learn a second API surface.
Operational trust matters
Teams should be able to understand which backend served a request, why it was chosen, and what it cost.
Team
A small team with a strong infrastructure bias.
Fallbakit Core Team
Product and infrastructure
We build for teams that want local inference to stay practical in production instead of becoming an operational science project.
Routing, tunnel reliability, and developer experience.
Applied platform engineers
Runtime and operations
Our work sits between application teams, local GPU boxes, and cloud fallbacks, so we care about clean interfaces, honest observability, and predictable recovery.
Ollama, oMLX, vLLM, gateway behavior, and safe fallback.
Design and documentation
Public product surface
We keep the product legible for developers: one endpoint, one control plane, and documentation that reflects how the platform is actually operated.
Marketing, docs, onboarding, and support clarity.
Funding
We are actively looking for sponsorship and funding partners.
If you care about local AI infrastructure, developer tooling, or making hybrid inference cheaper and easier to operate, we would love to talk.