Kelviq vs TurboOCR

Kelviq is the all-in-one revenue infrastructure platform built from the ground up for SaaS and AI companies. As a Merchant of Record, Kelviq takes full liability for global sales tax (VAT, GST), fraud, and regulatory compliance — letting AI startups sell in 100+ countries without ever registering for a foreign tax ID. It supports subscriptions, usage-based billing, feature entitlements, and one-time purchases through a single API. The AI-specific angle is real-time metering: Kelviq can track every token, API call, compute unit, or active user with zero reported latency. This is critical for AI products where costs spike unpredictably and customers need granular visibility into what they're being charged for. Pricing is 2.9% + 40¢ per transaction (up to $5K/month volume) or 3.5% + 40¢ thereafter, with no monthly fees — competitive with Stripe + a separate tax tool. Built by the team behind ParityDeals (a price localization tool with proven market fit), Kelviq launched to #1 on Product Hunt today with 430 upvotes. The founders' experience running a SaaS business internationally gives them genuine insight into the pain points they're solving.

TurboOCR is a C++20 OCR server that uses CUDA and TensorRT to process documents at speeds that make Python-based OCR look like a fax machine. The headline number: 270 images per second on FUNSD form datasets with approximately 11ms single-request latency — roughly 50x faster than PaddleOCR's standard Python implementation. It uses PP-OCRv5 models (the same underlying tech as PaddleOCR) but squeezes them through TensorRT FP16 optimization for GPU inference. The server exposes both HTTP and gRPC interfaces from a single binary and handles PDFs natively with four extraction strategies: pure OCR, native text layer extraction, hybrid verification mode, and a "best of both" fallback chain. PP-DocLayoutV3 handles layout detection across 25 document region classes — useful for structured documents where you need to know that a bounding box is a table cell vs. a header vs. a figure caption. A Prometheus metrics endpoint tracks throughput, latency, and GPU memory in real time. Deployment is Docker-first: TensorRT engine compilation happens automatically on first startup. The catch is it requires Linux with an NVIDIA Turing GPU (RTX 20-series minimum) and driver 595+, so it's not a laptop tool. But for enterprise document automation — invoices, forms, medical records — the throughput-to-cost ratio is hard to beat.