Landing AI alternatives: 5 honest takes
Landing AI is a strong product. It set the modern bar for document-extraction accuracy, especially on tables and structured forms, and a lot of teams pay for that bar. The reasons to look elsewhere are not about Landing AI being bad. They are about fit: pricing, deployment posture, transparency, ecosystem.
We considered each of the five below seriously while building Docira. Here is the honest read on each, with the use cases where they win.
Docira
The pitch: routing transparency. Every parsed page comes back with the routing trace — tier, provider, model, complexity score, confidence, latency — embedded in the response. No black box. The same trace is the audit log, so compliance review is one query rather than a separate tool to integrate.
We route across nine upstream providers. When NVIDIA is degraded, traffic shifts to Together. When a page is genuinely simple, it lands on Groq for low cost. The router decision is visible to you and to us in the same record.
When to choose Docira: you need to explain to compliance, your CTO, or a customer why the model made a specific extraction choice on a specific page. Or you want to avoid single-provider lock-in.
AWS Textract
Textract is the default if you already run on AWS. It is in your VPC, your IAM, your billing, your logs. Forms and queries work well, key-value extraction is solid on standard layouts, and the latency is competitive once your traffic warms a region.
The cost gets large quickly. AWS publishes per-page rates of roughly $0.05 for forms and $0.10 for tables — about an order of magnitude more than commodity OCR for trivial pages, where you do not need the table extractor at all. Textract also does not return a routing trace, because there is nothing to route — one model handles everything.
When to choose Textract: you are an AWS shop, your data must not leave the AWS network, and the volume is low enough that the per-page rate is not the binding constraint.
Unstructured
Unstructured ships an open-source library plus a hosted API. The open-source story is the strongest in this group. You can run their parsers in your own infrastructure, no per-page fees, no data-leaving-the-VPC concerns.
The hosted product is priced low — cents per page on the standard tier — but accuracy on complex tables and scanned documents trails the VLM-based options. Unstructured leans on traditional OCR with light VLM augmentation; that is a different accuracy bracket from a VLM-first router.
When to choose Unstructured: you want to self-host, your accuracy bar is moderate, and your workload is high-volume on relatively clean documents.
Google Document AI
Google's Document AI sits in the same product family as Textract: deep cloud integration, mature pipelines for specific document types (invoices, receipts, IDs), and a per-page price around $0.10 for the form parser based on Google's public pricing page.
Google's specialised parsers (e.g., the invoice processor) are calibrated against extensive training data for their target document type. If your workload is exactly invoices and exactly the format Google trained on, the accuracy is hard to beat. If your workload is mixed or non-standard, the per-document parser model becomes a friction.
When to choose Document AI: you are on GCP, your documents fit one of the supported parser types, and you want managed accuracy without tuning prompts.
OCR.space
OCR.space is the cheap-and-simple option. It runs traditional OCR, returns plain text, and costs about $0.001 per page on the paid tier. There is no table reconstruction, no key-value extraction, no schema-guided output.
For workflows that just need text out of a PDF and nothing more — legal-discovery indexing, search-engine ingestion of plain prose — OCR.space is the right tool. The simpler the document, the more it makes sense.
When to choose OCR.space: you have plain text in your PDFs and you need it in a string. That is the whole job.
What we'd choose, by workload
Mixed-document workloads with table extraction and audit requirements: Docira. The routing-trace story compounds across both technical fit and compliance review.
Locked-in AWS or GCP environments with low-to-moderate volume: Textract or Document AI. The integration cost of moving data out outweighs the per-page savings.
Self-hosted, high-volume, moderate-accuracy: Unstructured. The open-source story is genuinely strong.
Plain-text-only, high volume: OCR.space. Do not over-engineer this.
The first question to answer is not “which vendor.” It is “what does my workload actually need.” The honest answer often points to a different choice than the one the vendor pitch suggests.