Cradle Bio is live · custom pricing · last verified 23 Oct 2026 · viability monitor
Cradle Bio
AI protein engineering platform that designs optimized variant plates and learns from every wet-lab round.
The compounding-rounds model is the actual product here; sequence generation is table stakes. Cradle's case studies come with numbers you can audit — four peptide constraints in one round, 10 developable IgGs in three, +20 °C thermostability on a dehalogenase, and an 8.69 pM binder in a blind challenge. If you can run rounds and feed results back, the second campaign gets cheaper than the first.
Verdict generatedVerified 7d ago · liveness 69/100 · cite: rightaichoice.com/tools/cradle-bio
- Biopharma R&D teams engineering antibodies, peptides, or vaccine antigens across multiple rounds
- Industrial bio companies optimizing enzymes for specific catalytic conversions
- Teams with their own assay data that want compounding gains instead of one-shot generation
- Organizations needing private custom models, SSO, and retained IP with no royalties
- Academic labs without assay throughput or budget headroom for enterprise software
- Teams that cannot supply their own experimental data to train the models
- Groups that want open-weight models they can host and fine-tune themselves
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Skip Cradle if you cannot run and measure wet-lab rounds on a regular cadence — without your own assay results feeding back, the compounding-learning loop never starts and you are paying for a design tool you could approximate more cheaply.
Cradle's value compounds per round, so the real cost of ownership is your wet-lab spend — every Cradle-designed plate is a plate you have to synthesize and assay.
That places it with enterprise protein-design platforms sold to biopharma R&D rather than with self-serve sequence-generation tools, which cost far less per month but give you no round-to-round learning. The right comparison is Cradle's engagement cost against the wet-lab spend of the extra design-build-test rounds you would otherwise run.
In short
Cradle Bio — AI protein engineering platform that designs optimized variant plates and learns from every wet-lab round. Best for Biopharma R&D teams engineering antibodies, peptides, or vaccine antigens across multiple rounds, Industrial bio companies optimizing enzymes for specific catalytic conversions, Teams with their own assay data that want compounding gains instead of one-shot generation. Pricing is on request (custom quotes).
No head-to-head comparison for Cradle Bio yet — the alternatives below are the closest matches we list.
What is Cradle Bio?
Cradle Bio — AI protein engineering platform that designs optimized variant plates and learns from every wet-lab round. Best for Biopharma R&D teams engineering antibodies, peptides, or vaccine antigens across multiple rounds, Industrial bio companies optimizing enzymes for specific catalytic conversions, Teams with their own assay data that want compounding gains instead of one-shot generation. Pricing is on request (custom quotes).
Cradle is an AI protein engineering platform built for R&D teams that already run assays and want each experiment to sharpen the next one. Bring a protein — antibody, enzyme, peptide, or vaccine antigen — plus the properties that matter (potency, specificity, expression, thermostability), and Cradle designs an optimized plate of variants for the next wet-lab round. Upload the results and round two is built on what round one actually measured. The workflow stacks design, optimization, and learning in one place. Start from a target or your own sequence; multi-property co-optimization pushes efficacy, developability, and manufacturability in parallel instead of trading one against another. Point-and-click candidate design puts the ML in a bench scientist's hands, and the vendor states everything in the web application is also exposed through the API for computational teams scripting and scaling their pipelines. Probabilistic plate-level modeling — optimizing the whole batch rather than each sequence in isolation — is the reasoning behind the round design. Cradle publishes auditable evidence. A top-20 pharma peptide program met all four of its constraints (potency, specificity, expression, thermostability) in one Cradle round per program, reported 5× faster than the partner's own estimate. A VHH hit sub-2 nM binding against three snake venom toxins plus a 76.7 °C Tm and 1.59× expression in two rounds. An IgG campaign returned 10 potent, developable candidates in three rounds. And a blind AIntibody Challenge entry bound at 8.69 pM against 29 competing designs. Cradle runs its own wet lab to test models against real data before customer data arrives. Security and IP suit enterprise buyers: ISO 27001 and SOC 2 Type II, Google Cloud infrastructure, data isolation, monitoring, and SSO for Google and Microsoft. Sequences and data train only your organization's models, and you retain IP with no royalties. Named partners include Novonesis, Lundbeck, and Hansa Biopharma. It
Key features
- Guided rounds: ML-guided experiment design embedded in the round workflow
- Zero-shot antibody diversification before any training data exists
- Multi-property co-optimization across potency, specificity, expression, and thermostability
- Generative protein candidate generation for antibodies, enzymes, peptides, and vaccine antigens
- Probabilistic plate-level batch optimization across an entire experiment plate
- Round tracking from candidate generation through experimental results upload
- Reports workspace for experiment results and custom predictor outputs
- Error fingerprints that model assay noise in round design
- Multi-property trade-off visualization across a batch of designs
- Portfolio-style optimization across parallel programs and design strategies
- Custom predictors (beta) for running internally developed models inside Cradle
- API access mirroring the web application for scripting and pipeline integration
- Privacy-first AI: sequences and data train only your organization's models
- ISO 27001 and SOC 2 Type II certified, hosted on Google Cloud with SSO for Google and Microsoft
Behind the verdict
Pick Cradle when protein design is already an experimental loop for you — you have assay capacity, you generate variant data, and you want each plate to raise the odds on the next one. That feedback cycle is where the platform earns its keep, and the case studies back it up with specifics rather than vibes: 10 potent, developable IgGs in three rounds after a top-50 pharma's own screening and optimization had failed. Pass if you can't supply your own wet-lab data. Cradle learns from your measurements; without a steady stream of them, the compounding story has nothing to compound. Groups that want open-weight models they can host and fine-tune themselves are also the wrong audience — Cradle is a managed platform, and the models stay under the hood. The closest alternative depends on what you're buying. For one-shot generative design of antibodies or binders, dedicated generative tools are cheaper and quicker to trial. Cradle's distinction is the closed design-optimize-learn workflow plus assay-validated benchmarks — the vendor runs its own lab, tests models before customer data arrives, and publishes workflows and failures, which is rarer than it should be. In practice, the API matters more than the marketing suggests for computational teams: everything in the web app is exposed there, so you can script round generation and wire it into existing pipelines. Bench scientists get point-and-click candidate design, which helps adoption across a mixed team. Where it bites: this is enterprise-shaped software. Named partners are Novonesis, Lundbeck, and Hansa Biopharma — mid-to-large organizations with assay throughput. Academic labs without that throughput or budget headroom should weigh cheaper design tooling first. Also be clear that Cradle won't run the experiments for
Models under the hood
as of 2026-10-23
Limitations
- Cradle's value depends on you bringing your own experimental data — if you cannot run and measure rounds, the compounding loop has nothing to learn from, and the error-fingerprint modeling of assay noise has nothing to calibrate against.
- Cradle does not disclose the specifics of its underlying ML models, so you cannot inspect or retrain them yourself.
- The platform guides you through structured campaigns rather than handing you a raw model to embed in your own stack, which will frustrate teams wanting open, fully customizable weights.
- Published speedups (5× on the peptide program, 2-12x timelines) are vendor-reported and depend on assay quality and how far your program has already progressed.
as of 2026-10-18
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Is Cradle Bio still active?
Cradle Bio is live · custom pricing · last verified 23 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Cradle Bio? 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: October 2026
How we score →What's new in Cradle Bio
Checked todayAcross the latest 4 updates: 2 community discussions and 2 news mentions.
Does a Fab antibody need a specialist model?
Cradle compared two approaches to engineering Fab antibodies, whose small multi-chain structure complicates protein engineering, and reports the results.
A tight antibody that also passed developability: Our performance in the AIntibody Challenge
Cradle reports its results in the AIntibody Challenge, citing an antibody that bound tightly and also passed developability criteria.
Hansa Biopharma Partners with Cradle to Advance AI-Driven Protein Design and Engineering
Hansa Biopharma and Cradle announced a partnership to apply AI-driven protein design and engineering.
Optimizing across the entire batch is one secret to better proteins
Cradle describes batch-level rather than per-variant optimization as an approach for improving protein design outcomes.
What do users say about Cradle Bio?
What people actually say about Cradle Bio — is it worth it?
We scanned public community sources for Cradle Bio on Oct 11, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to Cradle Bio. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Cradle Bio— 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 23 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Cradle Bio 21 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 pricing page, description, features, summary, our verdict
- — changed hidden costs, who should skip it, use cases, summary, setup time, limitations, features, our verdict, switching in, switching out, pricing fit, description
- — changed who should skip it, description, our verdict, who it suits, features, summary, SSL certificate
- — changed pricing page
- — changed our verdict, who it suits, description, features, who should skip it, summary
- — changed pricing page
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Cradle Bio cost in 2026?
Cradle Bio: Pricing is on request (custom quotes). Pricing last verified 23 Oct 2026.
Where the pricing makes sense
The company stage and team size where Cradle Bio's pricing actually pencils out — and where peers do it cheaper.
That places it with enterprise protein-design platforms sold to biopharma R&D rather than with self-serve sequence-generation tools, which cost far less per month but give you no round-to-round learning. The right comparison is Cradle's engagement cost against the wet-lab spend of the extra design-build-test rounds you would otherwise run.
Has Cradle Bio's pricing changed?
No plan price for Cradle Bio has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Cradle Bio?
Pick Cradle Bio if you are Biopharma R&D teams engineering antibodies, peptides, or vaccine antigens across multiple rounds, Industrial bio companies optimizing enzymes for specific catalytic conversions, Teams with their own assay data that want compounding gains instead of one-shot generation, Organizations needing private custom models, SSO, and retained IP with no royalties.
Look elsewhere if you are Academic labs without assay throughput or budget headroom for enterprise software, Teams that cannot supply their own experimental data to train the models, Groups that want open-weight models they can host and fine-tune themselves, Buyers expecting a CRO to run the experiments on their behalf.
Skip it if Skip Cradle if you cannot run and measure wet-lab rounds on a regular cadence — without your own assay results feeding back, the compounding-learning loop never starts and you are paying for a design tool you could approximate more cheaply.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Cradle Bio actually fits — and what changes day-one when you adopt it.
You have a lead antibody that binds well but fails on expression and shows developability flags. You upload the sequence, mark potency, expression, and stability as the properties that matter, and Cradle returns a designed plate for the next round.
Outcome: You run the plate, upload results, and the following round is conditioned on what actually measured — including the assay noise Cradle fingerprints — instead of re-ranking the same variants by hand.
You have three parallel campaigns at different stages. Through the API you push sequences in, pull designed plates out, and wire Cradle into your existing design pipeline while using portfolio-style optimization to allocate effort across the programs.
Outcome: Your scripts drive the same design engine the bench scientists use in the web app, so the computational and experimental sides work off one set of rounds rather than two diverging spreadsheets.
Three programs have burned many rounds without meeting four simultaneous constraints. You run one guided Cradle round per program with all four limits set as co-optimization objectives.
Outcome: Cradle's published case study reports all limits met in one round per program, at 5× faster than the partner's own estimate — which is the shape of result to validate on your own targets before scaling the engagement.
Use cases
- Mature antibody candidates for binding affinity and developability, including ADCs and multispecifics.
- Optimize enzyme catalytic conversions that hold up under hostile industrial conditions.
- Design vaccine antigens that keep their shape outside the lab, including thermostable variants.
- Engineer therapeutic peptides to meet potency, specificity, expression, and thermostability at once.
- Rescue a stalled late-stage program by exploring several design strategies in parallel.
- Optimize an entire plate or batch rather than ranking individual variants.
- Diversify an antibody campaign from scratch with zero-shot generation before you have data.
- Script and scale protein design workflows through the API from a computational team's own stack.
Setup time & first value
How long it actually takes to get something useful out of Cradle Bio — broken out by persona, not the marketing-page minute.
Bench scientists typically reach a first designed plate in a single onboarding session — the workflow is point-and-click and starts from a target or an existing sequence. Computational teams take longer only because they are wiring the API into an existing pipeline. Real time-to-value is set by your assay turnaround, not by the software: the compounding benefit appears at round two, once your
Switching to or from Cradle Bio
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual variant picking: upload your existing campaign data and let Cradle design the next plate as a guided round.
- →From one-shot generative models: bring the sequences you already generated plus their assay results, and Cradle conditions the next round on the measured outcomes.
- →From spreadsheets and per-variant ranking: move to batch-level probabilistic optimization across the whole plate.
- →From an internal ML model: wrap it as a custom predictor (beta) so your own model runs inside Cradle alongside the round workflow.
- ↗To open-weight protein models: expect to rebuild campaigns from raw sequences, since Cradle's models are not disclosed or exportable for retraining.
- ↗To a CRO-led campaign: you would hand over design and assay together, losing the in-house round-to-round learning loop.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Cradle Bio”, and we withheld 6: 6 did not mention Cradle Bio. We are showing none, because we could not prove any of them are about Cradle Bio.
Official links
What are the best Cradle Bio alternatives?
The closest alternatives to Cradle Bio we list are SciSpace BioMed Agent, Nimbus Therapeutics and Adaptyv. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| SciSpace BioMed Agent | Freemium | Pricing not published | — |
| Nimbus Therapeutics | Contact Sales | Custom pricing | — |
| Adaptyv | Paid | Pricing not published | — |
Alternatives to Cradle Bio
View all
SciSpace BioMed Agent
SciSpace BioMed Agent runs multi-omics, variant prioritization, and drug repurposing analysis from plain-language biomedical prompts.
Nimbus Therapeutics
Nimbus Therapeutics designs highly selective small-molecule drugs for oncology, immunology, and metabolic disease.
Tools that pair well with Cradle Bio
Common stack mates teams adopt alongside Cradle Bio, with the specific reason each pairing earns its keep.
SciSpace BioMed Agent
SciSpace BioMed Agent runs multi-omics, variant prioritization, and drug repurposing analysis from plain-language biomedical prompts.
Nimbus Therapeutics
Nimbus Therapeutics designs highly selective small-molecule drugs for oncology, immunology, and metabolic disease.
Adaptyv
Automated cloud lab for protein validation — binding, expression and thermostability assays with data back in as little as 3 days
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