Lilypad x Akave
Compute Meets Storage: Forging the AI Superstack

Let's talk about the real things. Founder @ Lilypad Compute Network prev-@Protocol Labs | @Filecoin | @Lilypad_Tech PM | Advisor @GodwokenRises | prev-@IBM TechJam Podcast Co-Host | coder, engineer, dog lover 🐕, global citizen 🌏, entrepreneur 👩💻, aspiring francophone 🥐
Lilypad and Akave are thrilled to announce a formal strategic partnership that brings together two foundational primitives of the decentralized AI stack: compute and storage. This collaboration is a bold step toward building a truly modular, community-owned AI infrastructure that rivals centralized platforms.
As Lilypad continues to evolve into the nexus of a decentralized AI cooperative, this alliance with Akave strengthens our ability to support verifiable workflows, secure model outputs, data provenance, and composable AI pipelines.
2. Meet Our Partner: Akave
Akave is a Filecoin Layer 2 decentralized data management network that enables secure, programmable data storage, access, and monetization. Designed to empower the next generation of data-driven applications and marketplaces, Akave delivers cost-effective and performant decentralized storage for AI, Web3, and DePIN ecosystems.
Akave’s architecture supports both public and permissioned data buckets, policy-enforced access, and full data provenance—making it the perfect partner for AI systems that require secure storage and traceable lineage.
3. Shared Vision: Infrastructure for Open AI
Akave and Lilypad are aligned by a clear and ambitious goal: building infrastructure for decentralized intelligence.
Decentralization-first: Both platforms reduce dependency on opaque, centralized cloud providers
Composable AI primitives: Compute and storage as modular services
Data provenance and monetization: Transparent value flows and auditability baked into system design
AI accessibility: Empower developers and creators with verifiable infrastructure at every layer
This partnership is more than integration—it’s the emergence of a decentralized foundation for building, training, and deploying AI applications.
4. Synergistic Strengths
What Lilypad Brings:
Serverless decentralized GPU compute
On-demand model hosting, inference APIs, and agent workflows
Verifiable job execution and API-first architecture
What Akave Brings:
Decentralized object storage with programmable data buckets
Full support for on-chain provenance and usage policies
Optimized infrastructure for large LLM and ML workloads
🔧 Short-Term Use Cases
RAG Integration Pilot: Showcase a reference architecture for retrieval-augmented generation using Akave-stored data and Lilypad inference
Model Caching: Use Akave to store fine-tuned models and intermediate outputs
Job Output Storage: Enable users to preserve inference results and synthetic datasets with cryptographic traceability

🚀 Mid-Term Roadmap
Plugin-like Integration: Build native ingestion and retrieval pipelines between Akave and Lilypad
Co-hosted Agent Workflows: Empower developers to deploy agents that retrieve, compute, and store data using both networks
Synthetic Data Provenance: Enable tracking and monetization of AI-generated data across the full pipeline
Reputation Transparency: Store Lilypad job provider statistics and performance metadata using Akave’s provenance infrastructure
5. Strategic Impact
This collaboration delivers:
A fully modular decentralized AI stack
Real value for users: fast, flexible, censorship-resistant compute and storage
Clear provenance for models and data outputs, a critical need for commercial and regulated AI
Blueprints for the ecosystem: example architectures that others can replicate and build on
Together, Lilypad and Akave move the ecosystem beyond theory to execution, showing what a real decentralized AI infrastructure can look like.
6. Long-Term Ecosystem Vision
This is only the beginning. The long-term vision is a global, modular, permissionless infrastructure layer for AI:
Lilypad powers verifiable computation and AI agent execution
Akave anchors decentralized storage and data integrity
Together, they offer an open foundation to train, fine-tune, and deploy models with full transparency and composability
With upcoming POCs such as Waterlily 2.0 (artist attribution via model fine-tuning) and data-to-agent reference architectures, the potential for future impact is vast.
7. Looking Ahead
This announcement is only the first beat of a new rhythm. The decentralized AI stack is forming.
🌐 Signup for Akave testnet: https://akave.ai/testnet
🧠Join Lilypad ecosystem to deploy, run, and monetize models: https://docs.lilypad.tech
Let’s build decentralized intelligence infrastructure - together.





