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Choose the Raspberry Pi AI Camera when you need a camera with supported on-sensor inference. Choose AI HAT+ when a Raspberry Pi 5 project needs a separate vision accelerator, selecting the 13 TOPS or 26 TOPS version for the workload. Treat the AI Kit as legacy hardware: it is discontinued, and Raspberry Pi directs new buyers to AI HAT+. If your definition of “AI” includes running local large language models (LLMs) or vision-language models (VLMs), look at AI HAT+ 2 instead.
Those products are not interchangeable versions of the same device. They put inference hardware in different places, work with different Raspberry Pi computers, and target different kinds of models. The most reliable buying decision starts with your workload and host computer, not with the largest TOPS number.
Quick answer: which Raspberry Pi AI product should you buy?
| Product | Best fit | Host requirement | Published price or status |
|---|---|---|---|
| Raspberry Pi AI Camera | Camera-first projects using supported vision models with inference on the camera module | Raspberry Pi computer with a compatible camera connector; official setup covers Raspberry Pi 4 and Raspberry Pi 5, with adjustments for other listed models | $70 US; production stated through at least January 2028 |
| Raspberry Pi AI HAT+ 13 TOPS | Pi 5 vision projects needing a separate, moderate-capacity accelerator | Raspberry Pi 5 | From $70 for the Hailo-8L version; production stated through at least January 2030 |
| Raspberry Pi AI HAT+ 26 TOPS | Pi 5 projects that need the higher AI HAT+ inference tier or more headroom for vision workloads | Raspberry Pi 5 | From $70 product family listing; the 26 TOPS Hailo-8 version is the higher-capacity model; production stated through at least January 2030 |
| Raspberry Pi AI Kit | Existing owners or buyers who find a verified used kit and understand its legacy status | Raspberry Pi 5 | Discontinued; no current official product price |
| Raspberry Pi AI HAT+ 2 | Local generative AI, including LLM and VLM workloads, alongside vision | Raspberry Pi 5 | $200; production stated through at least January 2036 |
Prices above are manufacturer list prices or product-page statements, not regional retail guarantees. Stock, taxes, shipping, compatible cables and software support can change by country and over time.
What is different about the AI Camera, AI HAT+ and AI Kit?
AI Camera: inference is part of the image sensor
The AI Camera combines a camera and an AI accelerator in one module built around Sony’s IMX500 sensor. Raspberry Pi describes the architecture this way: “The AI Camera is built around a Sony IMX500 image sensor with an integrated AI accelerator.” In practical terms, the camera module accepts an input tensor, runs a supported model on its own accelerator, and returns inference outputs to the Raspberry Pi. The Pi still runs the camera application, handles results and performs any additional processing your project requires.
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This arrangement makes the AI Camera attractive when the camera is the central component. You do not add a separate HAT simply to obtain the supported on-sensor inference capability. You do, however, need to deploy a model that the IMX500 workflow supports, complete the required firmware and software setup, and connect the camera to a compatible Raspberry Pi camera connector.
The module has a 12.3-megapixel sensor and a published 1080p30 video mode. Those are camera specifications, not a promise that every neural-network model will run at 30 inference frames per second. Actual throughput depends on the model, image pipeline and application work performed by the host.
AI HAT+: a separate Hailo accelerator for Raspberry Pi 5
AI HAT+ is an expansion board for Raspberry Pi 5. It adds a Hailo neural-processing unit (NPU) that supported Raspberry Pi camera frameworks can use for vision inference after the required software and models are installed. The product family is available with a Hailo-8L rated at 13 TOPS or a Hailo-8 rated at 26 TOPS.
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Because the accelerator is separate from the camera, an AI HAT+ project normally has three pieces: the Raspberry Pi 5 host, the HAT+ board and a compatible camera or another input source. This separation is useful when you want to change cameras, process more than one stream in an application designed for it, or keep the camera module independent from the accelerator. It also means more hardware to mount, power and configure than an AI Camera.
AI Kit: the earlier 13 TOPS bundle
The AI Kit bundled an M.2 HAT+ with a Hailo-8L module rated at 13 TOPS for Raspberry Pi 5. Raspberry Pi documents the kit as functionally equivalent to the 13 TOPS AI HAT+ variant, but the kit is no longer in production. The manufacturer recommends AI HAT+ for new customers.
If you already own an AI Kit, its discontinuation does not make the hardware unusable. If you are buying today, a used listing needs extra verification: confirm that the Hailo module and M.2 HAT+ are both present, check the board condition and connector hardware, and make sure current Raspberry Pi software still supports the model you intend to run. There is no current official AI Kit price to use as a reliable value benchmark.
AI HAT+ 2: a different proposition for generative AI
AI HAT+ 2 is the newer adjacent product in this family. It uses a Hailo-10H board, provides 40 TOPS at INT4 and includes 8 GB of onboard RAM. Raspberry Pi positions it for local LLM and VLM workloads as well as vision.
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That specification does not make AI HAT+ 2 a universally faster AI Camera or an automatic upgrade for ordinary object detection. Raspberry Pi states that its computer-vision performance is comparable to AI HAT+ 26 TOPS. The reason to pay for the $200 board is primarily its generative-AI support and local memory, not an assumption that the 40 TOPS figure translates directly into a higher camera frame rate.
Match the hardware to your workload
Choose AI Camera for a self-contained camera project
AI Camera is the clearest choice when your requirements look like these:
- The camera is the main product feature: a detector, counter, classifier or similar embedded-vision device.
- You want inference integrated into the sensor rather than a separate accelerator board.
- You may use a Raspberry Pi computer other than Raspberry Pi 5, provided it has a compatible camera connector and the chosen model can be deployed to IMX500.
- Your model fits the AI Camera’s supported conversion and deployment workflow.
It is particularly sensible for a compact installation where reducing board count, cable runs and mounting points matters. It is less suitable when your project depends on arbitrary neural-network models, local language models or a general-purpose accelerator whose workload will change frequently. Supported-model constraints should be checked before you design the application around the camera.
Choose AI HAT+ 13 TOPS for a moderate Pi 5 vision workload
The 13 TOPS Hailo-8L version is the appropriate starting tier when a Raspberry Pi 5 needs supported vision inference and the application does not require the higher published AI HAT+ capacity. It is also the closest current equivalent to the discontinued AI Kit’s accelerator specification.
“Moderate” is a workload description, not an FPS guarantee. A small detector at one resolution and a multi-camera pipeline at another can place very different demands on the same NPU. Validate the models, input sizes, stream count and latency target you actually need rather than treating 13 TOPS as a universal performance threshold.
Choose AI HAT+ 26 TOPS for additional vision headroom
The 26 TOPS Hailo-8 version is the better fit when your Pi 5 application needs the higher listed AI HAT+ tier, has more demanding models, or needs additional capacity for the vision pipeline. It is a manufacturer capability distinction, not an independently measured promise of twice the application frame rate of the 13 TOPS version.
Consider the 26 TOPS board when replacing a 13 TOPS design would be difficult after deployment, but keep the model runtime and software support in the decision. A model that cannot be compiled or integrated for the Hailo software stack gains nothing from unused theoretical throughput.
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Choose AI HAT+ 2 when local LLM or VLM support is a requirement
AI HAT+ 2 is the relevant option when the Pi 5 must run generative models locally, such as an LLM that interprets text or a VLM that combines image and language input. Its 8 GB onboard RAM changes the system design compared with a vision-only accelerator.
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Do not select it merely because “40 TOPS” is larger than “26 TOPS.” The 40 TOPS number is specified at INT4, and TOPS figures from different architectures and precisions are not directly comparable. For a conventional camera detector, AI HAT+ 26 TOPS may be the more appropriate product; for local generative workloads, AI HAT+ 2 is the family member designed for that job.
Compatibility and required hardware
AI Camera requirements
- A Raspberry Pi computer with a compatible camera connector. The official getting-started material covers Raspberry Pi 4 and Raspberry Pi 5, with minor changes for other listed models.
- A suitable camera cable for the specific Raspberry Pi model and connector arrangement.
- Current Raspberry Pi software and the AI Camera firmware/model deployment components.
- A neural-network model that can be deployed to the IMX500 workflow.
The camera does not require an AI HAT to provide its integrated inference. The host Pi remains responsible for the application, display or network service and any processing that is not performed on the sensor.
AI HAT+ and AI Kit requirements
- Raspberry Pi 5; AI HAT+ is not a Raspberry Pi 4 accelerator board.
- The HAT or M.2 hardware installed correctly on the Pi 5, with the required mechanical hardware and board-to-board connection.
- A camera or other input device if the application is doing vision.
- Raspberry Pi software, Hailo runtime support and models prepared for the supported pipeline.
- A power supply and enclosure arrangement appropriate for the Pi 5, HAT and any connected peripherals.
The AI Kit follows the same Pi 5 requirement. Its discontinued status affects availability and support expectations, not the basic fact that it uses the 13 TOPS Hailo-8L configuration.
AI HAT+ 2 requirements
- Raspberry Pi 5 and the current software stack documented for AI HAT+ 2.
- Enough system storage and memory for the host application, model files and operating system in addition to the HAT’s 8 GB onboard RAM.
- Models and runtimes that support the board’s intended LLM, VLM or vision workflows.
Onboard RAM is not the same as adding general-purpose Raspberry Pi system memory. The Pi still runs the operating system and application, while the HAT supplies its own accelerator memory for supported workloads.
What 13 TOPS, 26 TOPS and 40 TOPS actually tell you
TOPS means trillions of operations per second under a stated arithmetic format and hardware design. It is useful for identifying the manufacturer’s capacity tiers, but it is not an application benchmark. The figures in this product family are not a controlled, independent comparison of camera frame rate, end-to-end latency, energy use or model accuracy.
| Published figure | Product and qualification | How to use it in a buying decision |
|---|---|---|
| 13 TOPS | AI HAT+ with Hailo-8L; also the AI Kit’s Hailo-8L module | Moderate Pi 5 vision-acceleration tier; do not convert it directly to FPS |
| 26 TOPS | AI HAT+ with Hailo-8 | Higher AI HAT+ vision tier and more nominal capacity; still not an FPS guarantee |
| 40 TOPS INT4 | AI HAT+ 2 with Hailo-10H | Relevant to its generative-AI design; INT4 and architecture make direct comparison with other figures inappropriate |
For a serious deployment, evaluate the exact model, image dimensions, quantization, number of streams, preprocessing, postprocessing and host-side work. If a vendor or project maintainer publishes a benchmark for that exact combination, use those conditions. In the absence of such a test, describe the TOPS number as a specification rather than a measured result.
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Prices, production horizons and value
The AI Camera is listed at $70 US. AI HAT+ is listed from $70, with the 13 TOPS and 26 TOPS versions occupying different capability tiers. AI HAT+ 2 is listed at $200. These are manufacturer statements accessed in 2026; local retail prices, tax, shipping and stock are separate questions.
| Product | Published price information | Stated production horizon | Value warning |
|---|---|---|---|
| AI Camera | $70 US | At least January 2028 | Budget separately for a compatible cable and verify that your model is deployable to IMX500 |
| AI HAT+ | From $70 | At least January 2030 | Requires Raspberry Pi 5, and the camera is a separate component for vision projects |
| AI Kit | Discontinued; no current official price | Not stated as a current product horizon | Used-market condition, completeness and software support need individual verification |
| AI HAT+ 2 | $200 | At least January 2036 | Pay for local LLM/VLM capability and onboard RAM, not simply a larger TOPS label |
A low listed board price does not necessarily mean a lower complete-project cost. The AI Camera includes the camera function, while an AI HAT+ project may require a separate camera, cable and mounting solution. Conversely, a Pi 5 owner who already has a camera may find the modular HAT approach easier to reuse across projects.
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Installation planning: a safe sequence for each option
AI Camera setup sequence
- Confirm the host connector. Identify the camera connector on your Raspberry Pi model and obtain the cable specified for that connector. Do not force a cable designed for a different connector format.
- Install a current Raspberry Pi operating system. Update the system before adding the camera software so firmware and camera-framework versions are in a supported state.
- Mount the camera with power removed. Seat the ribbon cable fully, observe the contacts and locking mechanism, and secure the module so vibration cannot loosen the connection.
- Install the AI Camera software and firmware components. Use the current Raspberry Pi getting-started procedure for your Pi model; the exact package and firmware requirements can change.
- Deploy a compatible IMX500 model. A normal CPU or GPU model file is not automatically an IMX500 deployment. Follow the supported conversion and packaging path for the model you selected.
- Run a camera preview and an inference example. First prove that the camera stream works, then verify that inference results arrive at the host. This separates cable and camera problems from model-deployment problems.
- Measure your application conditions. Record resolution, stream rate, model and host-side processing before deciding that the system meets a latency or throughput target.
AI HAT+ setup sequence
- Start with a Raspberry Pi 5. AI HAT+ is not intended for Raspberry Pi 4, so do not purchase it for an older host expecting a software-only workaround.
- Install the HAT with the Pi powered off. Use the supplied mechanical hardware and ensure the board-to-board connection is fully seated.
- Prepare the operating system and Hailo software. Install the current supported runtime, libraries and model packages for the AI HAT+ variant you bought.
- Connect and test the camera separately. A vision failure can come from the camera, cable, application or NPU stack. Establish a working camera stream before adding inference.
- Choose a supported model and pipeline. Confirm that the model is available for the Hailo workflow, then configure the camera framework or application to use the NPU.
- Test one stream first. Measure end-to-end latency and host load before adding additional streams, larger images or multiple models.
- Check thermal and power behavior in the enclosure. Sustained inference can expose cooling or power limitations that are invisible during a short demonstration.
AI Kit setup or used purchase
- Verify the complete bundle. Confirm that the listing includes both the M.2 HAT+ and Hailo-8L module, not only an empty adapter board.
- Inspect connectors and fasteners. Damage, missing standoffs or bent contacts can cause an apparent software failure.
- Confirm current software support. Install the present Raspberry Pi and Hailo components before committing to a production design.
- Use the 13 TOPS AI HAT+ guidance as the closest functional reference. The kit’s documented capability is equivalent to that tier, but used condition and seller history are additional risks.
AI HAT+ 2 setup sequence
- Confirm that the project really needs local generative AI. If the application is only object detection, compare the cost and requirements of AI HAT+ vision hardware first.
- Install the current AI HAT+ 2 software stack on Raspberry Pi 5. Use the board’s supported runtime and model instructions rather than assuming that every AI HAT+ package is interchangeable.
- Allocate models with the board’s memory and precision in mind. The 40 TOPS figure is specified at INT4; model format and quantization are part of compatibility.
- Test the LLM or VLM independently. Verify prompt or image-response behavior before combining it with a live camera pipeline.
- Profile the complete application. Include camera capture, preprocessing, model inference, response generation and network or display output in the latency measurement.
Common buying mistakes and how to avoid them
Buying AI HAT+ for a non-Pi-5 host
AI HAT+ is a Raspberry Pi 5 accessory. If your computer is a Raspberry Pi 4 or another compatible model, the AI Camera is the relevant product in this comparison, subject to connector and IMX500 model support. There is no AI HAT+ software setting that turns an incompatible host into a supported one.
Assuming the AI Camera needs a HAT
The AI Camera’s inference processor is integrated into the IMX500 sensor. A separate AI HAT is not required for that supported on-sensor inference path. You may still add other hardware for unrelated acceleration or I/O, but it is not a prerequisite for the camera’s AI function.
Choosing by TOPS alone
13, 26 and 40 TOPS describe different products, architectures and, in the AI HAT+ 2 case, INT4 arithmetic. They do not provide a common FPS scale. Select the board that supports your model and host first, then use capacity as a way to choose between tiers.
Expecting every model to run everywhere
The AI Camera requires an IMX500-compatible deployment workflow. AI HAT+ requires models and software supported by the Hailo runtime. AI HAT+ 2 has its own generative-AI support path. Check model conversion, operators, quantization and runtime versions before purchasing hardware for a particular network.
Confusing a camera with an accelerator
The AI Camera is both an imaging module and an inference device. AI HAT+ and AI HAT+ 2 are accelerator boards; they do not replace the need for a camera when the project captures images. This distinction affects the bill of materials, enclosure design and troubleshooting sequence.
Treating a discontinued AI Kit as a normal new product
The AI Kit can make sense for an existing owner or a carefully verified used purchase, but Raspberry Pi recommends AI HAT+ for new customers. Compare a used kit’s total cost and condition with a current AI HAT+ board before accepting the older bundle’s nominal similarity to the 13 TOPS version.
Decision checklist
- Do you need a camera and supported on-sensor inference in one module? Choose AI Camera.
- Is the host a Raspberry Pi 5 and the workload supported vision inference? Choose AI HAT+ 13 TOPS or 26 TOPS according to the required capacity.
- Do you already own the discontinued AI Kit? Keep using it if it meets your model and software needs; do not treat it as the default new purchase.
- Do you need local LLM or VLM execution? Consider AI HAT+ 2 and its 8 GB onboard RAM.
- Will the project use an unconverted or unusual model? Verify deployment support before selecting any of these products.
- Are you comparing speed? Look for measurements using your exact model and pipeline; do not translate TOPS directly into FPS.
- Are you budgeting the whole system? Include the Pi, camera where required, cable, power, cooling, enclosure and storage, not only the accelerator price.
Bottom line
For a camera-led Raspberry Pi project, the AI Camera is the simplest architectural fit because its Sony IMX500 sensor includes the inference accelerator. For Raspberry Pi 5 vision projects that need a separate NPU, AI HAT+ is the current choice: 13 TOPS for the moderate tier and 26 TOPS when the workload needs more nominal capacity. The AI Kit is a discontinued 13 TOPS-era bundle and belongs in a used-hardware discussion, not at the top of a new-buy list. If local generative AI is the requirement, AI HAT+ 2 is the purpose-built option, with 40 TOPS INT4 and 8 GB onboard RAM. None of those figures substitutes for testing the models and pipeline your project will actually run.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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