Raspberry Pi 4 & 5 Price Increase: New 2GB Costs

Raspberry Pi increased the official maximum prices of its 2GB Raspberry Pi 4 and Raspberry Pi 5 on October 1, 2026. The generated table above shows the exact old price, new price, dollar increase, and percentage change from the company’s announcement.

The company says sharply rising memory costs made the previous pricing unsustainable. It also says it hopes to reverse the increases when memory prices subside. This is a hardware price change, not an AI API rate change.

What changed today

Only two named board configurations changed in this announcement: the 2GB Pi 4 and 2GB Pi 5. The 1GB variants of both boards remain unchanged, as do the 2GB Compute Module 4 and Compute Module 5.

Raspberry Pi advises buyers to use approved resellers, which follow its published maximum prices. That matters because unauthorized sellers may charge more or misrepresent products. Taxes, shipping, storage, power supplies, cases, cooling, and AI accelerators remain outside the board-only figures shown above.

The exact event data also appears on our maintained Raspberry Pi pricing page, in the canonical hardware JSON, and through the mirrored hardware API path.

What this means for edge AI budgets

The increase compounds across fleets. Use the calculator above to enter the number of boards planned for a classroom, kiosk rollout, sensor network, robotics lab, or edge-AI deployment. It shows the previous total, current total, and added board cost from the dated source data.

For lightweight API-backed gateways, a lower-memory board may still be sufficient because the model runs in the cloud. For local computer vision, speech, or language-model workloads, memory needs depend on the operating system, model, quantization, accelerator, and concurrent services. Buying too little memory can create a false saving if the workload then needs a replacement board.

Compare the full local stack with hosted inference, not just the board price. Current model rates are available on our OpenAI pricing page and Google AI pricing page; enter expected usage in the AI token calculator. Our local AI vs API pricing guide covers the broader break-even factors.

Who benefits—and who loses

The unchanged 1GB boards become relatively more attractive for narrow, headless workloads. Older Pi models may also suit projects that do not need Pi 4 or Pi 5 performance. Raspberry Pi explicitly recommends checking whether an older board can meet the requirement.

Small fleet buyers lose most visibly because the increase is paid upfront and repeats for every board. Schools, makers, integrators, and startups with fixed hardware budgets may need to reduce quantities or move nonessential services to hosted infrastructure.

Cloud providers gain a stronger comparison point, but cloud is not automatically cheaper. Recurring inference, storage, bandwidth, privacy, connectivity, and latency requirements can outweigh the lower initial hardware spend. If the workload outgrows a small edge board, compare RunPod’s current GPU options with the complete board-and-accessory cost.

What to do if you’re buying now

First, measure peak memory use on a representative workload. Include the operating system, containers, camera buffers, databases, inference runtime, and failure headroom rather than sizing from the model file alone.

Second, compare approved-reseller stock across the exact configuration and region. Treat prices above the official maximum as reseller markup, not part of Raspberry Pi’s announced increase.

Third, calculate total deployed cost. Add storage, power, cooling, networking, accelerators, setup labor, spares, and replacements. For cloud alternatives, include token usage, data transfer, storage, uptime requirements, and the operational cost of losing offline execution.

What is confirmed—and what is not

Confirmed: Raspberry Pi announced the two increases, made them effective immediately, kept the listed alternatives unchanged, and attributed the move to higher memory costs.

Not confirmed: the announcement does not say AI demand caused this specific increase. The Hacker News discussion may connect memory inflation with the AI infrastructure boom, but that is commentary, not Raspberry Pi’s stated causal claim. The company also gives no date or threshold for a future reversal.

The board prices remain outside the token-pricing model feed because physical hardware has no model ID, endpoint, or hosted-inference rate. Our Labs coverage note also treats Raspberry Pi as a physical-hardware test gap: a future benchmark would need pinned board, storage, cooling, power, operating system, model artifact, quantization, task set, latency, accuracy, failures, and full-system cost.

Bottom line

The cheapest 2GB Pi 4 and Pi 5 options are now materially more expensive, while selected 1GB boards and 2GB Compute Modules remain unchanged. Edge-AI buyers should avoid paying for unused headroom, but they should not undersize memory for local inference merely to preserve the old budget.

Use the generated fleet calculator, validate memory on the real workload, and compare the all-in local cost with a cloud or hybrid design before purchasing.

Sources: Raspberry Pi’s official October 1 announcement and the Hacker News discussion. This article contains an affiliate link; AI Pricing Guru may earn a commission at no extra cost to you.