TAPPAS
Edge AI platforms software
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- Ease of management
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What is TAPPAS
TAPPAS is an edge AI application and pipeline framework used to build, run, and optimize video analytics and other inference workloads on edge devices. It targets developers and solution teams deploying computer vision pipelines that combine pre-processing, model inference, and post-processing with hardware acceleration. The product emphasizes reusable pipeline components and integration with edge AI runtimes and accelerators to move from model to deployable application.
Pipeline-oriented edge AI runtime
TAPPAS structures edge AI workloads as modular pipelines, which helps teams compose pre-processing, inference, and post-processing stages consistently. This approach aligns well with common edge video analytics patterns such as multi-stream ingest, batching, and per-frame metadata handling. It can reduce custom glue code compared with assembling components from scratch.
Integration with edge accelerators
TAPPAS is designed to work with edge inference acceleration stacks rather than only CPU execution. This is useful for deployments that need predictable latency and throughput on constrained devices. It also supports practical deployment patterns where the application must coordinate multiple accelerated stages (decode, inference, tracking, overlay, etc.).
Reusable components for vision apps
TAPPAS provides building blocks that can be reused across applications, such as common computer vision pipeline elements and utilities. This can speed up development for teams building multiple similar edge analytics solutions. It also supports packaging an end-to-end application rather than only producing a trained model artifact.
Narrower scope than full IoT
TAPPAS focuses on edge AI application pipelines and does not function as a full device management and IoT operations suite. Capabilities such as fleet provisioning, OTA updates, device health monitoring, and policy-based rollout typically require additional platforms. Organizations may need to integrate separate tooling for production fleet operations.
Hardware and stack coupling
Practical use often depends on specific edge AI runtimes, drivers, and accelerator toolchains, which can increase environment complexity. Portability across heterogeneous hardware may require additional engineering and testing. This can be a constraint for teams standardizing on multiple device types.
Learning curve for pipeline model
Teams unfamiliar with pipeline-based multimedia/vision frameworks may face a ramp-up in concepts, debugging, and performance tuning. Troubleshooting real-time pipelines (synchronization, buffering, latency) can be more complex than single-model inference scripts. Production readiness may require profiling and careful configuration per target device.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Non-commercial / Academic | $0 (free) | Official site states: "tappAS is FREE for non-commercial use." Desktop Java GUI for RNA-Seq isoform-level analysis; downloads provided. |
| Commercial / Commercial license | Not specified on official site | No commercial pricing or paid plans listed on the official tappAS site; contact information provided for questions (site contact/maintainers). |
Seller details
Hailo Technologies Ltd.
Tel Aviv, Israel
2017
Private
https://hailo.ai/
https://x.com/hailo_ai
https://www.linkedin.com/company/hailo-ai/
