Remote experimentation platforms: experiments via service providers

Jonathan Alles

EVOBYTE Digital Biology

By EVOBYTE Your partner for the digital lab

Remote experimentation platforms are changing how lab work gets done. Instead of buying instruments, staffing benches, and waiting for access, teams can log into a digital lab, design a protocol, and have it executed in a highly automated cloud lab or a fully outsourced remote lab run by a specialist service provider. This “lab-as-a-service” model turns wet-lab work into an on‑demand, software-driven experience while keeping data quality high and costs predictable. For small companies and startups, the shift is especially powerful: it places world-class capabilities one click away and removes the need for large upfront investments.

Cloud labs and remote labs sound similar, but there’s a useful distinction. In a cloud lab, you remotely control experiments through a unified software interface and APIs, much like renting compute in the cloud. In a fully outsourced remote lab, you brief a scientific team that runs the work for you on a modern, automated platform. Both approaches sit under the broader lab-as-a-service concept and both can slot into your existing data environment and LIMS to build a truly digital lab.

What “lab-as-a-service” means in practice

Think of lab-as-a-service the same way you think of cloud computing. You bring questions and designs; the provider brings instruments, automation, methods, and compliance. In the cloud lab flavor of the model, you create or select a protocol, ship samples, and monitor execution from your browser. The provider’s robots, sensors, and scheduling systems handle the rest, and your results stream back with full metadata. This approach is not science fiction. Cloud laboratories are heavily automated facilities that let scientists run experiments from anywhere via software, and they standardize execution to reduce variability while logging parameters minute by minute. That combination democratizes access to advanced instruments and can improve reproducibility compared with manual bench work.

In the fully outsourced flavor, you still work remotely, but the service provider operates as an extension of your R&D team. You get assay design help, method development, and expert interpretation in addition to automation and data capture. For many startups, this is the most efficient way to move from idea to decision-ready data because it compresses staffing, training, and equipment setup into a single statement of work.

In both cases, remote experimentation platforms integrate with common informatics tools, export structured files, and support audit trails. That matters if you expect to progress toward regulated development: clean data, consistent execution, and traceable methods make later scale-up and tech transfer smoother.

Remote experimentation platforms: how they work

A typical cloud lab workflow has four stages. First, you design and parameterize your experiment in a web application, selecting instruments, methods, and controls. Second, you ship samples and reagents. Third, the facility executes your work, moving samples through instruments via robotic handlers and validated procedures. Finally, you analyze results inside the platform or pull them into your own environment for further processing. Some providers emphasize breadth of instrumentation and self-service scripting; others emphasize turnkey methods and expert assistance. The right choice depends on your team’s skills, timelines, and risk tolerance.

Two practical examples help make this concrete. If your team needs to run a sequence of analytical chemistry methods—say HPLC, LC‑MS, and NMR—repeatedly while tuning conditions, a cloud lab lets you pilot, adjust, and rerun quickly without rebooking instruments or rewriting SOPs. If your team is exploring a new target and needs a package of biochemical and cell-based assays with mechanistic profiling, an outsourced provider can propose a staged plan, execute it, and return both data and interpretation, all without your team stepping into a physical lab.

Who offers remote experimentation today?

Several credible service providers now operate at scale, and each serves a different slice of the market. Emerald Cloud Lab (ECL) offers a software-controlled facility with over 200 instrument models, including HPLC, GC, mass spectrometers, NMR, PCR and qPCR, ELISA, surface plasmon resonance and biolayer interferometry, western blotting, and flow cytometry. You design and run experiments through a single interface, 24/7, and ECL emphasizes standardized execution and data integrity. For teams that want hands-on control without owning hardware, it is a compelling cloud lab option.

Strateos operates remote-controlled cloud labs and also helps organizations stand up their own automated facilities. Scientists can access and command workflows for drug discovery, cell and gene therapy, and synthetic biology from a web browser. A collaboration with Eli Lilly highlighted the model: a remote, robotic “studio lab” integrated design, synthesis, purification, analysis, and testing into one platform so scientists could run and refine experiments in near real time. For early-stage companies, Strateos positions the cloud lab as a way to reach capabilities normally reserved for larger organizations.

For biologics and small-molecule discovery where deep assay expertise matters, Arctoris pairs an automated laboratory and its Ulysses platform with experienced drug hunters. The service covers biochemistry, cell biology, protein sciences, structural biology, and biophysics, and it offers cascades across target validation, hit finding, and lead optimization. This is a strong example of the fully outsourced remote lab model: you gain precision automation plus a scientific team that designs, executes, and interprets the work with short design‑make‑test‑analyze cycles.

If your challenge is upstream bioprocess development, Culture Biosciences runs cloud‑connected 250 mL and 5 L bioreactors in its own facility and exposes them through a web application called Console. You can design fermentations, monitor live data and imagery, and analyze runs remotely. This lets strain engineering and bioprocess teams iterate quickly without installing parallel bioreactor capacity in-house, and it scales from early optimization to transfer.

These examples are not exhaustive. Biofoundries and specialized CROs also use high levels of automation and data capture, and some now expose APIs or programmatic ordering. But the pattern is clear: whether you want self-service control in a cloud lab or a scientific partner in a remote lab, viable options exist and are maturing fast.

What can you run? Common assays available today

The menu is broad and growing. In analytical chemistry and biophysics, cloud labs offer HPLC, FPLC, GC, LC‑MS, NMR, thermal analysis, plate readers, and label‑free binding assays like SPR and BLI. These methods support purity checks, metabolite profiling, binding kinetics, and formulation work. ECL, for example, lists these instrument classes and more, with methods scriptable and repeatable across runs. That makes it straightforward to build a robust assay, control conditions, and audit parameters later.

In molecular biology and genomics, you can expect PCR and qPCR, nucleic acid extraction, NGS library preparation, and basic cloning workflows. These are popular because they benefit immediately from automation: precision pipetting, strict timing, and reliable thermal control drive better consistency and shorter hands-on time. Cloud lab interfaces make it easy to copy a run, change a variable, and compare outcomes inside the same environment without rewriting everything from scratch.

For drug discovery, outsourced platforms add specialized cascades: biochemical assays with kinetic and mechanistic readouts, binary and ternary complex formation studies, cell-based assays for target engagement, and structural biology for structure‑guided design. Providers like Arctoris emphasize rapid, precise DMTA cycles and can move programs from target validation through lead optimization with consistent data standards. That continuity is valuable when you need to make confident go/no‑go decisions in weeks, not quarters.

In bioprocessing, remote fermentations on parallel, cloud‑connected bioreactors are now routine. Culture Biosciences’ service is a good example: you can parameterize feed strategies, monitor time‑series process data, integrate offline analytics, and compare runs across batches in the same online console. For startups, this replaces months of procurement and setup with a login and a method template, enabling rapid design-of-experiments across strains, media, and process conditions.

Why this matters for small companies and startups

Remote experimentation platforms flatten the playing field. First, they convert capital expense into operating expense. Instead of purchasing a mass spectrometer or a bank of bioreactors, you pay only for the runs you need. Second, they compress timelines. Because providers run 24/7 and schedule work automatically across many instruments, your queue time falls and iteration speeds up. Third, they raise data quality. Automation and standard operating environments reduce variability, while the software layer preserves metadata, environment logs, and instrument settings that often get lost in manual workflows. Fourth, they expand your hiring options. A small team can produce decision‑ready data without staffing every bench skill in-house.

Consider a pre-seed therapeutics startup with two scientists and a tight runway. In month one, they profile a panel of hits using biochemical and cell-based assays run by an outsourced remote lab, getting clean dose‑response data and selectivity readouts. In month two, they move the top series into a cloud lab for orthogonal biophysics and basic ADME. In month three, they use remote bioreactors to test expression conditions and stability for early protein production. All along, data lands in the same store with consistent formats, ready for investors and partners. No leases, no instrument maintenance, and no six‑month wait for lab space.

There are also strategic benefits. Remote platforms make costs transparent, which helps with planning and board reporting. They make methods portable, which helps if you outgrow a provider or need to transfer work to a manufacturing partner. And they encourage better experimental design because scripting and templating force clarity about variables, controls, and readouts before you hit “run.”

Responsibilities and risks

Operating remotely does not remove your responsibility for scientific rigor. You still need to validate methods, define acceptance criteria, and plan controls. Pay attention to sample logistics and chain of custody, especially for unstable materials. Clarify data ownership, retention, and export formats before you start. Ask providers about instrument calibration schedules, quality systems, and how they handle deviations. Run a small pilot first, compare results to your internal gold standards, and only then scale up. Good providers will welcome this and may offer trial credits or structured onboarding to reduce your risk.

Integration is another consideration. Cloud labs and remote labs produce a lot of data, and it is worth an early investment in clean naming conventions, metadata management, and ELN/LIMS sync. Most platforms export in standard formats or provide APIs, which makes it straightforward to automate ingestion and build dashboards for decision meetings. This is how you turn a remote facility into a true extension of your digital lab.

Conclusion: turning your lab into software

Remote experimentation platforms bring the lab to your laptop. By combining the reach of a cloud lab with the expertise of an outsourced remote lab service provider, you can turn ideas into decision‑ready data faster and more affordably than ever before. For resource‑constrained teams, this is a practical way to build a resilient, scalable digital lab without the burden of owning and operating every instrument. Start small, validate, integrate—and then let software and automation do the heavy lifting.