Integrating Akash Network: Decentralized Computing for Web3

We integrate Akash Network into your infrastructure to host AI inference (LLM, Stable Diffusion), blockchain nodes (Ethereum, Cosmos), DApp backends, and compute tasks on a decentralized cloud. Providers offer GPU/CPU power; clients manage workloads via SDL manifests; payment in AKT. Key advantage: support for standard Docker containers without custom runtime, simplifying migration of existing applications. Typical scenario: an ML team needs to deploy an inference server for Llama 3 on GPU, but AWS or GCP are expensive, and Kubernetes is overkill. Akash allows running the same Docker image on decentralized providers with savings up to 50% and full blockchain control.

How the SDL Manifest Works and Why It's Critical

Stack Definition Language (SDL) is a YAML format for describing deployments. It looks like docker-compose but has differences that can break deployment without preparation. Here's an example manifest for an inference server:

---
version: "2.0"

services:
  inference-api:
    image: your-org/llm-inference:sha256-abc123
    expose:
      - port: 8080
        as: 80
        to:
          - global: true
    env:
      - MODEL_PATH=/models/llama-7b
      - MAX_CONCURRENT=4
    resources:
      cpu:
        units: 4.0
      memory:
        size: 16Gi
      storage:
        - size: 50Gi
          attributes:
            persistent: true
            class: beta3

profiles:
  compute:
    inference-api:
      resources:
        cpu:
          units: 4
        memory:
          size: 16Gi
        gpu:
          units: 1
          attributes:
            vendor:
              nvidia:
                - model: rtx3090
  placement:
    dcloud:
      pricing:
        inference-api:
          denom: uakt
          amount: 1000

deployment:
  inference-api:
    dcloud:
      profile: inference-api
      count: 1

Akash Network Documentation highlights the importance of persistent: true for data surviving restarts. Without it, the container starts fresh when moved to another provider. Use class: beta3 (NVMe) for high IOPS — critical for ML models and databases.

SDL pitfalls:

• persistent storage — mandatory for data that must survive container restarts. Without it, ephemeral storage.
• Storage class — beta3 (NVMe) is significantly faster than beta2 (HDD). IOPS difference is critical for DBs and ML models.
• Image pinning — use digest (sha256) instead of latest tag. Providers cache images, so latest may differ.
• GPU resources — not available on all providers. Specifying exact model (rtx3090, a100) narrows the pool but guarantees compatibility.
• Missing health check — deployment gets no IP, and you'll pay even for a non-working service.
• Wrong port expose — the application will be inaccessible externally.

How to Programmatically Deploy to Akash

For automation from your application, we use the Akash JavaScript SDK or direct REST API calls (Cosmos-based). Below is an example of creating a deployment via SDK:

import { Registry, DirectSecp256k1HdWallet } from "@cosmjs/proto-signing";
import { SigningStargateClient } from "@cosmjs/stargate";
import { MsgCreateDeployment } from "@akashnetwork/akash-api/akash/deployment/v1beta3";

const AKASH_RPC = "https://rpc.akashnet.net:443";
const AKASH_DENOM = "uakt";

async function createDeployment(sdlContent: string, walletMnemonic: string) {
    const wallet = await DirectSecp256k1HdWallet.fromMnemonic(walletMnemonic, {
        prefix: "akash",
    });

    const [account] = await wallet.getAccounts();
    const client = await SigningStargateClient.connectWithSigner(AKASH_RPC, wallet, {
        registry: new Registry(/* akash proto types */),
    });

    const dseq = Math.floor(Date.now() / 1000);

    const msg = {
        typeUrl: "/akash.deployment.v1beta3.MsgCreateDeployment",
        value: MsgCreateDeployment.fromPartial({
            id: {
                owner: account.address,
                dseq: BigInt(dseq),
            },
            groups: parseSDLGroups(sdlContent),
            deposit: { denom: AKASH_DENOM, amount: "5000000" },
        }),
    };

    const result = await client.signAndBroadcast(
        account.address,
        [msg],
        { amount: [{ denom: AKASH_DENOM, amount: "20000" }], gas: "800000" }
    );

    return { dseq, txHash: result.transactionHash };
}

After deployment creation, an auction starts: providers place bids, the client selects the best and creates a lease. This is an asynchronous process — you need to subscribe to blockchain events (WebSocket or polling).

async function watchBidsAndCreateLease(dseq: number, ownerAddress: string) {
    const bids = await pollBids(dseq, ownerAddress, { timeoutMs: 120000 });
    if (bids.length === 0) throw new Error("No bids received");
    const bestBid = bids.sort((a, b) =>
        Number(a.bid.price.amount) - Number(b.bid.price.amount)
    )[0];
    await createLease(bestBid.bid.bidId, wallet);
    await sendManifestToProvider(bestBid.bid.bidId.provider, dseq, sdlContent);
}

How to Manage the Deployment Lifecycle

After lease creation, the deployment is managed via the Provider Service API — HTTP endpoints of the provider. The endpoint is obtained from on-chain provider data.

async function getDeploymentStatus(providerAddress: string, dseq: number, owner: string) {
    const providerInfo = await queryProviderInfo(providerAddress);
    const providerHost = providerInfo.hostUri;
    const response = await fetch(
        `${providerHost}/lease/${owner}/${dseq}/1/1/status`,
        { headers: { Authorization: `Bearer ${await getProviderToken()}` } }
    );
    return response.json();
}

For production, we integrate monitoring via Prometheus/Grafana, running a sidecar container in the same deployment. We regularly check the deposit balance — when exhausted, the deployment terminates without recovery. To avoid this, we set up automatic refills.

Which Workloads Are Best Suited for Akash?

Pricing and Cost Control

Cost is denominated in uAKT per block (~6 seconds). For pre-deployment estimation, use the Cloudmos API: send the SDL and get the price per block. Typical price for ML inference with RTX 3090 GPU is a few tens of cents per hour, offering significant savings compared to cloud providers. For production, we configure automatic deposit refills — otherwise the deployment shuts down without warning.

Comparison of Manual and Programmatic Deployment

What's Included in the Work

• Analysis of your application and preparation of the SDL manifest
• Development of deployment scripts (CLI or programmatic integration)
• Monitoring and alerting setup
• Integration with EVM contracts (optional)
• Documentation and team training
• Post-launch support

Experience: 5+ years in Web3, 30+ projects in decentralized computing integration. We use Foundry, Cosmos SDK, and TypeScript.

Integration Timelines

• Basic (manual SDL, CLI) — 1 to 3 days
• Programmatic (automated deployment, monitoring) — 1 to 2 weeks
• Full (EVM contract, escrow, lifecycle) — 3 to 5 weeks

We'll evaluate your project within 1–2 business days. Contact us to discuss details. Get a consultation on Akash integration.

Blockchain Infrastructure Deployment: Nodes, RPC, Indexing

Subgraph fell at 3:47 AM. By morning users saw outdated balances, transactions "hung" in the UI, support received 47 tickets in an hour. Cause: the handler in the subgraph failed on a transaction with a non-standard event log — and the entire index stopped. We have encountered such situations dozens of times. Our experience shows: blockchain infrastructure does not forgive gaps in observability. Guaranteeing uptime without multi-layered monitoring and fault-tolerant architecture is impossible. Over 8 years working with Ethereum, Polygon, and Solana, we have developed an approach that allows predictable deployment of infrastructure of any scale — from a single node to a multichain grid with dozens of subgraphs.

RPC Layer Architecture

Every dApp interaction with the blockchain goes through RPC — the JSON-RPC API provided by a node. Three options:

Managed providers — Alchemy, QuickNode, Infura, Ankr. Minimal operational costs, SLA, built-in monitoring. Limits: rate limits (Alchemy Free: 300 RU/sec), vendor lock, potential downtime during provider incidents. For most projects — the right choice at the start.

Self-owned nodes — full control, no rate limits, no third-party dependence. Cost: archive Ethereum node requires 2.5–3TB SSD, a strong server, and DevOps support. Sync from scratch on Ethereum via Geth/Nethermind — 3–7 days. Justified under high load or latency requirements.

Hybrid — self-owned node as primary, managed provider as fallback. Standard for protocols with high TVL. Proper load balancing can reduce costs by 20–30% compared to pure managed setup. Under high monthly request volume, hybrid saves significantly.

How to Set Up an RPC Layer Without a Single Point of Failure?

At least two providers, DNS round-robin with health check every 5 seconds, automatic fallback when latency >500 ms. In practice, this gives 99.99% availability during any provider failure. For protocols with high TVL, we recommend a custom HA-proxy (nginx or Envoy) in front of two managed providers.

Why Is a Hybrid RPC Scheme More Cost-Effective Than Pure Managed?

At high request volumes, managed providers can be very expensive; a hybrid using a self-owned node as primary and a managed fallback cuts costs significantly without losing SLA.

Ethereum Node Clients

Execution clients: Geth (most used), Nethermind (C#, fast sync), Besu (Java, enterprise), Erigon (fastest sync, efficient archive mode ~2TB instead of 3TB).

Consensus clients (post-Merge): Lighthouse (Rust), Prysm (Go), Teku (Java), Nimbus (Nim). Each node after The Merge requires a pair of execution + consensus clients.

For DevOps: eth-docker — Docker Compose configurations for all client combinations. Setting up monitoring via Grafana + Prometheus is mandatory; a standard dashboard is available in each client's repository.

The Graph: Event Indexing

The Graph Protocol — decentralized indexing. A subgraph describes which events from which contracts to index and how to transform them into a GraphQL schema.

Subgraph structure:

• subgraph.yaml — manifest: contract addresses, startBlock, events to handle
• schema.graphql — GraphQL schema of entities
• src/mapping.ts — AssemblyScript event handlers

dataSources:
  - kind: ethereum
    name: UniswapV3Pool
    network: mainnet
    source:
      address: "0x88e6A0c2dDD26FEEb64F039a2c41296FcB3f5640"
      abi: UniswapV3Pool
      startBlock: 12370624
    mapping:
      eventHandlers:
        - event: Swap(indexed address,indexed address,int256,int256,uint160,uint128,int24)
          handler: handleSwap

AssemblyScript handlers — not TypeScript. No nullable types, no closures, no many standard APIs. An error in the handler stops the subgraph indexing on that transaction. Important: add try-catch for operations that can fail (e.g., store.get() for an entity that may not exist).

How to Avoid Subgraph Indexing Stops?

Graph Node logs are monitored in real-time; on hasIndexingErrors = true an alert fires and an automatic node restart (via systemd or Kubernetes). Typical downtime on error — 150–300 seconds to recover. Additionally, for production we set up a watchdog that restarts Graph Node if subgraph lag exceeds 50 blocks.

Choosing Between Hosted Service and Decentralized Network

Graph Hosted Service (free, centralized) is deprecated in favor of Subgraph Studio + Graph Network. For production: deploy on Graph Network with GRT curation signal — the subgraph gets indexers proportional to curation.

Alternatives to The Graph: Ponder (TypeScript, self-hosted, easier to debug), Envio (ultra-fast indexer, supports EVM + non-EVM), Subsquid (TypeScript, own network), Moralis Streams (managed, webhook-based). Our experience shows: for high-load projects with unique logic, Ponder or Envio are more effective — they give full control over the process and do not require GRT tokenomics.

Webhooks and Real-Time Notifications

Alchemy Webhooks and QuickNode Streams allow receiving events in real-time via HTTP webhook or WebSocket. For monitoring addresses, new transactions, mints — this is faster than polling RPC.

Tenderly — platform for monitoring and alerts. You can set up an alert for a specific contract event, balance change, function call with certain parameters. Transaction simulation via Tenderly API is invaluable for debugging.

Monitoring and Observability

Minimum monitoring stack for a protocol:

On-chain: OpenZeppelin Defender Sentinel — watches contract events, triggers webhook or Autotask when conditions are met. Forta Network — community-maintained bots detect anomalies (large withdrawals, flash loans, governance attacks).

Infrastructure: Grafana + Prometheus for nodes, Datadog or Grafana Cloud for managed metrics. Alerts on: node is 10+ blocks behind, RPC latency >500ms, subgraph lag >100 blocks.

Uptime: Better Uptime or PagerDuty on RPC endpoint and subgraph health endpoint (The Graph provides _meta { hasIndexingErrors, block { number } }).

Why Is Monitoring Without Tenderly Insufficient?

Tenderly provides transaction simulation and detailed traces — critical for debugging subgraph and smart contract errors. Forta focuses on network anomalies, not your infrastructure. The combination of Tenderly plus a custom Grafana dashboard covers 90% of incident scenarios.

Multichain Infrastructure

A protocol on 5 chains = 5 separate RPC endpoints, 5 subgraphs, 5 monitoring configs. Manageable but requires deployment automation.

For subgraph multi-network deployment: graph deploy --network mainnet, graph deploy --network arbitrum-one etc. with a unified codebase and network-specific addresses in separate config files.

Chainlink CCIP and LayerZero for cross-chain messaging require monitoring of both chains and transactions on intermediate relayers. A reorg on the source chain after a confirmed mint on the target chain is a classic bridge problem. Solution: wait for finality (on Ethereum ~15 minutes after Merge for economic finality) before confirming on the target chain.

Infrastructure Setup Process

1. Audit current stack — determine chains, request volume, latency and availability requirements.
2. Architecture design — select providers, load balancing, redundancy.
3. Subgraph development — manifest → schema → handlers → testing on local Graph Node → deploy to testnet → mainnet.
4. Monitoring configuration — Tenderly alerts, Grafana dashboard, PagerDuty integration.
5. Documentation and runbook — what to do when: subgraph falls behind, RPC downtime, node desync.
6. Handover to operations — team training, access transfer, first month support.

What's Included

• Deployment of managed or self-hosted Ethereum, Polygon, BNB Chain nodes
• RPC layer setup with primary/fallback and load balancing
• Subgraph development and deployment for your protocol
• Monitoring connection (Tenderly, Grafana, alerts)
• Runbook and operations documentation
• Team training (up to 4 hours online)
• 30-day support after delivery

Timeline

All projects are managed in a GitHub/GitLab repository with CI/CD; configuration code stays with you. Order infrastructure deployment — we'll show how to cut costs by 20–30% without losing reliability. Get a consultation — we'll demonstrate how we deployed infrastructure for a protocol with large TVL on Ethereum and Arbitrum. Contact us.