lift is live · free plan, paid from $400/mo · last verified 8 Nov 2026 · viability monitor
lift
Datalab's document intelligence API turns PDFs, scans, slides, and spreadsheets into structured markdown, JSON, or HTML at up to 1B+ pages
The public OmniParseBench results are what set Datalab apart: 16,288 pass/fail tests with every verdict visible, and an accurate mode scoring 94.1 ahead of Reducto, LlamaParse, Mistral OCR, Extend, and Azure. That transparency is worth something when a wrong extracted field costs more than a page fee. Skip it if you want to chat with a PDF or hand a drag-and-drop tool to non-technical staff.
Verdict generatedVerified 5d ago · liveness 65/100 · cite: rightaichoice.com/tools/lift
- AI research labs parsing web crawls, scientific papers, and 1000+ page books into training corpora
- Financial services and insurance teams extracting structured fields from non-standard documents
- Healthcare and clinical trial teams that cannot let documents leave the network (VPC or air-gapped)
- Developers composing convert, extract, and eval pipelines and promoting versioned pipelines to production
- Anyone who wants conversational Q&A over a document - a chat-based LLM is the right tool
- Non-technical teams needing a no-code, drag-and-drop document tool
- Low-volume users: the Free tier's $20 monthly allowance runs out fast on real documents and caps at 25 requests/min
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Skip Datalab if you want to ask a document questions in plain English rather than define an extraction schema and wire up an API call.
Word-level bounding boxes with a confidence score are a +$3 per 1,000 pages add-on, so the routing-to-human-review workflow Datalab recommends costs extra on top of the base Convert rate.
Free ($0/month, $20 included usage on a work email, $10 on a personal one) is an evaluation tier, and the 25 requests/min cap keeps it there. Team at $400/month billed monthly is a real commitment for a small team but reasonable for a production document pipeline; the 400 requests/min ceiling is the upgrade you are buying. Enterprise is where VPC, air-gapped, SSO and volume discounts live, and Datalab offers up to $5,000 in migration credits plus 33% off for qualifying startups under three
In short
lift — Datalab's document intelligence API turns PDFs, scans, slides, and spreadsheets into structured markdown, JSON, or HTML at up to 1B+ pages. Best for AI research labs parsing web crawls, scientific papers, and 1000+ page books into training corpora, Financial services and insurance teams extracting structured fields from non-standard documents, Healthcare and clinical trial teams that cannot let documents leave the network (VPC or air-gapped). Free to start; paid plans from $400/mo.
What is lift?
lift — Datalab's document intelligence API turns PDFs, scans, slides, and spreadsheets into structured markdown, JSON, or HTML at up to 1B+ pages. Best for AI research labs parsing web crawls, scientific papers, and 1000+ page books into training corpora, Financial services and insurance teams extracting structured fields from non-standard documents, Healthcare and clinical trial teams that cannot let documents leave the network (VPC or air-gapped). Free to start; paid plans from $400/mo.
Datalab is a research lab that trains its own OCR and document parsing models, then sells them as composable processors behind one hosted API and SDK. Hand a processor a file and structured markdown, HTML, or JSON comes back, with bounding boxes and per-field confidence where you ask for them. This is aimed at developers and data teams, plus the labs, banks, insurers, and clinical groups that need parsed documents at volume.
Key features
- Convert processor: PDFs, spreadsheets, and slides to markdown, HTML, or JSON in fast, balanced, or accurate mode
- Chandra 2.6 OCR, live in every Datalab mode, 2.5x faster than Chandra OCR 2.4 at the same price
- Surya 2.5 open OCR: 1.7x faster than Surya 2, 85.9 on OmniParseBench
- Indra OCR 1.1: CPU model reading handwriting, 80+ languages, and pages at any angle
- Lite mode: text, tables, headings, reading order, and layout boxes at $0.50 per 1,000 pages
- Extract processor: schema-defined fields to JSON with per-field citations and confidence scores
- Segment processor: find boundaries between documents merged into one file
- Eval processor: score output quality against Datalab criteria or your own rubrics
- Form field detection: field type, box, and label at 87% recall, median 1.1s per page
- Word-level bounding boxes tuned for handwritten and scanned pages
- Chart understanding, infographic parsing, and structured figure descriptions
- Track changes, form fill, spreadsheet tables, and document creation processors
- Document agent for multi-step document workflows
- Pipeline playground: compose processors, version a pipeline, promote it to production
- Managed batch: burst to 1B+ pages a day on Datalab's GPU workers, pay per page
Behind the verdict
Datalab earns a spot when parsing accuracy is a line item you can defend to someone above you. The October releases matter: Chandra 2.6 is now live in every mode at the same price and is 2.5x faster than Chandra OCR 2.4, Surya 2.5 reads 1.7x as many pages per second as Surya 2, and Lite mode returns text, tables, headings, reading order, and layout boxes for $0.50 per 1,000 pages. On OmniParseBench the accurate mode scores 94.1, 95.6 on tables and 94.9 on multi-column layouts. Where it bites: this is an engineering product. You wire up an SDK, compose processors into a pipeline, and version it before it goes to production. The Free tier's $20 monthly allowance and 25 requests/min cap run out on real document sets, and SSO only shows up on Enterprise. If all you need is one-off extraction from a handful of invoices, the setup cost trumps the page fees. The closest alternative depends on your bottleneck. LlamaParse and Reducto are the names Datalab benchmarks against and both are competent; what you get here is auditable test results and your choice of deployment. Managed cloud runs in us-east-1 with SOC 2 Type II and 99.99% uptime, but you can also drop the models into your own VPC on AWS, GCP, or Azure, or run fully air-gapped with the weights. That flexibility is why regulated buyers pick it over cheaper parsers. Try the playground before you commit.
Models under the hood
as of 2026-11-08
Limitations
- Pricing is per-processor: every plan runs the full hosted API at the same rate card, and you pay for the processors each request runs, so cost scales with workflow complexity (e.g.
- Lite parsing at $0.50 per 1,000 pages versus higher-cost Extract tiers).
- The Free plan ($0/month) includes $20/$10 monthly usage, a 25 requests/min rate limit, 25 concurrency, and 1 custom-processor creation per month; Team is $400/month with 400 requests/min and 400 concurrency.
- EU data residency costs 1.25x usage on every tier, and VPC, fully air-gapped deployment, SSO, and MSA/SLA are Enterprise-only; SOC 2 Type II is covered on all tiers.
as of 2026-10-20
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Is lift still active?
lift is live · free plan, paid from $400/mo · last verified 8 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is lift? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in lift
Checked todayAcross the latest 7 updates: 2 feature updates, 1 launch, 1 pricing change and 3 changelog entries.
Chandra 2.7 improves table and language handling
Balanced mode's OmniParseBench table score rises from 91.6 to 94.2; accurate mode reads non-English pages better at the same speed and price.
Fast mode drops to $1 per 1,000 pages and runs Surya 2.6
Fast mode now runs Surya 2.6, costs $1 per 1,000 pages with data sharing (down from $3), scores 91.6 on OmniParseBench and is faster.
Extraction citations now point at the exact words
Each extracted value links to the words or table cell it came from, and computed totals cite where their inputs came from.
Evals names the right mode for your documents
Evals runs Datalab's modes on your documents, marks every word, table cell and checkbox where they differ, and names the mode to use.
Form field detection: fewer missed fields, cleaner labels
Updated form field detection model finds more fields per page, returns cleaner labels and no longer repeats fields. Live in the API and playground.
API submits and result checks twice as fast
Median document submission time drops from 670 ms to 314 ms; result checks are twice as fast and SDK 0.9.1 picks up finished results sooner.
Surya 2.5: 1.7x faster, best open OCR under 1B
Surya 2.5 reads 1.7x as many pages per second as Surya 2 and scores 85.9 on OmniParseBench, described as the best of any open model under 1B parameters.
What do users say about lift?
What people actually say about lift — is it worth it?
Our community sentiment research written Aug 5, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about lift — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 8 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified lift 17 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed features, our verdict, who should skip it, pricing tiers, description, limitations, pricing page
- — changed our verdict, summary, pricing tiers, who should skip it, description, who it suits, features
- — changed models, limitations, pricing page
- — changed our verdict, who should skip it, pricing tiers, summary, features, description, who it suits
- — changed pricing page
- — changed models, pricing tiers, who it suits, features, summary, hidden costs, our verdict, use cases, limitations, switching in, who should skip it, switching out, setup time, description, pricing fit
Showing the 6 most recent of 17 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does lift cost in 2026?
lift: Free to start; paid plans from $400/mo. Pricing last verified 8 Nov 2026.
Plans compared
Every published lift tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Free | Free | — | A developer or small team evaluating the API against your own document pile before committing to production.Adds: Starting free entry point: full hosted API and SDK at the same per-processor rate card, $20/month included usage ($10 on a personal email), 25 requests/min, 25 concurrency, 1 custom-processor creation per month. |
| Team | $400/mo | monthly | A team running a production document pipeline that needs more throughput and a BAA/DPA.Adds: Adds $400/month included usage, 400 requests/min and 400 concurrency (16x Free), BAA/DPA, admin MFA enforcement, 4 custom-processor creations per month, and email + Slack support. |
| Enterprise | Custom | quote | Regulated or very high-volume buyers who need the models inside their own network with contractual terms.Adds: Adds VPC deployment or fully air-gapped on-prem, SSO, volume-based discounts, custom rate limits and concurrency, custom EU data residency terms, and a dedicated MSA/SLA. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where lift's pricing actually pencils out — and where peers do it cheaper.
Free ($0/month, $20 included usage on a work email, $10 on a personal one) is an evaluation tier, and the 25 requests/min cap keeps it there. Team at $400/month billed monthly is a real commitment for a small team but reasonable for a production document pipeline; the 400 requests/min ceiling is the upgrade you are buying. Enterprise is where VPC, air-gapped, SSO and volume discounts live, and Datalab offers up to $5,000 in migration credits plus 33% off for qualifying startups under three
Has lift's pricing changed?
No plan price for lift has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick lift?
Pick lift if you are AI research labs parsing web crawls, scientific papers, and 1000+ page books into training corpora, Financial services and insurance teams extracting structured fields from non-standard documents, Healthcare and clinical trial teams that cannot let documents leave the network (VPC or air-gapped), Developers composing convert, extract, and eval pipelines and promoting versioned pipelines to production.
Look elsewhere if you are Anyone who wants conversational Q&A over a document - a chat-based LLM is the right tool, Non-technical teams needing a no-code, drag-and-drop document tool, Low-volume users: the Free tier's $20 monthly allowance runs out fast on real documents and caps at 25 requests/min, Teams that need SSO on the standard paid tier - it's Enterprise only.
Skip it if Skip Datalab if you want to ask a document questions in plain English rather than define an extraction schema and wire up an API call.
GeneratedReal-world workflow fit
Concrete scenarios for the personas lift actually fits — and what changes day-one when you adopt it.
You are assembling a training corpus from web crawls, arXiv papers, and 1000+ page books, and need clean markdown with structure preserved at high volume.
Outcome: Convert on the fast tier handles the bulk at $4 per 1,000 pages, the accurate tier catches the hard scans, marker and surya open source models let you sanity-check locally, and managed batch scales to 100M+ pages a day on an allocated worker pool.
Claims arrive as scans and PDFs with wildly variable layouts, and you need consistent structured fields with an audit trail for each one.
Outcome: Extract on the balanced or accurate tier returns per-field citations so reviewers can see where each value came from, and word-level confidence scores from the add-on route uncertain pages to human review instead of guessing.
Patient forms and trial documents cannot leave the corporate network, but you still need structured output for downstream systems.
Outcome: Datalab runs inside your VPC on AWS, GCP, or Azure — or fully air-gapped on-prem — with a BAA/DPA, admin MFA enforcement on Team, and the same processors as the hosted API.
Use cases
- Extract line items and totals from invoices with per-field citations on the balanced or accurate extraction tier.
- Parse receipt data for expense reporting and accounting workflows.
- Convert contract fields into structured database entries with citations per field.
- Digitize surgical or medical forms for health records inside a VPC or air-gapped deployment.
- Process bank statements or pay stubs for loan applications.
- Classify and extract data from insurance claim documents.
- Build training-grade corpora from web crawls, scientific papers, multilingual sources, and 1000+ page books.
- Convert math and science documents into braille-ready markdown and LaTeX — Davis County School District cut QC time roughly 90% using Chandra.
Setup time & first value
How long it actually takes to get something useful out of lift — broken out by persona, not the marketing-page minute.
Managed cloud is the fastest path: sign up, get an API key, ship in minutes, with $20 in included usage to test against your own documents. VPC deployment takes longer because it is an Enterprise conversation with custom BAA/DPA terms. Air-gapped on-prem is the longest, gated on getting model weights into an isolated network.
Switching to or from lift
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a general-purpose chat LLM: define an extraction schema per document type and compare field-level accuracy on your own corpus before cutting over.
- →From Tesseract or PP-OCR: Indra OCR is positioned at roughly 15x their CPU text-model speed, and Datalab offers up to $5,000 in migration credits.
- →From a legacy OCR vendor: apply for migration credits and pilot Convert fast at $4 per 1,000 pages on a sample of your real scans.
- ↗To a chat-based LLM assistant: export your parsed markdown and index it directly — the structured output travels cleanly.
- ↗To a no-code document tool: expect to re-map your extraction schemas by hand, since the API-first schema definitions do not export as a visual template.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “lift”, and we withheld 6: 6 could not be judged, because “lift” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about lift.
Official links
What are the best lift alternatives?
The closest alternatives to lift we list are Airparser, Unstract and Mistral OCR. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Airparser | Freemium | Pricing not published | — |
| Unstract | Freemium | Pricing not published | — |
| Mistral OCR | Paid | Pricing not published | — |
Alternatives to lift
View all
Airparser
Airparser turns emails, PDFs and scans into structured JSON with AI document parsing — no templates to maintain
Unstract
Unstract turns PDFs, scans and forms into structured JSON with agentic, no-template document extraction and auditable Flow workflows.
Mistral OCR
Mistral OCR 3 converts PDFs, scans, and handwriting into structured markdown or JSON at $2 per 1,000 pages.
Tools that pair well with lift
Common stack mates teams adopt alongside lift, with the specific reason each pairing earns its keep.
Airparser
Airparser turns emails, PDFs and scans into structured JSON with AI document parsing — no templates to maintain
Unstract
Unstract turns PDFs, scans and forms into structured JSON with agentic, no-template document extraction and auditable Flow workflows.
Mistral OCR
Mistral OCR 3 converts PDFs, scans, and handwriting into structured markdown or JSON at $2 per 1,000 pages.
lift vs its closest rival
lift's closest head-to-head is with Temporal Ai.
Our lift vs Temporal Ai verdict written Oct 13, 2026 quotes $12.55, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Head-to-head comparisons
Lift vs Temporal Ai
Verdict being re-reviewed against current pricing and data.
Lift vs Audioeye
These aren't competitors — they solve different problems for different buyers, so don't shortlist them against each other. Pick Datalab (lift) if you're engineering a document pipeline: convert PDFs/scans/slides into markdown, JSON, or JATS XML with word-level bounding boxes, chaining its processors at flat per-1k-page rates. Pick AudioEye if you need ongoing ADA/WCAG A/AA coverage on a live website — automated fixes plus certified-expert custom fixes, VPAT/ACR documentation, and legal claim protection. The only overlap is conceptual: both touch accessibility/parsing, but Datalab's tagged accessible PDF processor is one API primitive inside a bigger document tooling platform, while AudioEye is a compliance service for websites.
GeneratedLift vs Screenplayiq
These aren't competitors — they don't belong on the same shortlist. ScreenplayIQ is coverage software for a screenplay: it produces a logline, comps, character emotional journey charts, and proofread fixes for writers, producers, and agents. Lift (Datalab) turns PDFs, scans, and slides into structured markdown, JSON, or HTML for AI labs, financial services, and clinical teams. If you have a draft to assess, pick ScreenplayIQ. If you have documents to parse, pick lift. Buying either to do the other's job is a waste.
GeneratedLabel Studio vs Lift
If your job is extracting structured fields (names, totals, dates) from high volumes of invoices, receipts, or contracts with high accuracy and minimal setup, Lift's pre-built templates and confidence scoring are purpose-built. If you need to label images, transcribe audio, evaluate LLM outputs, or annotate video for custom AI training, Label Studio's open-source flexibility and broad data type support are unmatched. Choose Lift for operational document automation; choose Label Studio for experimental AI data work.
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