SpeziLLM is live · free · last verified 25 Oct 2026 · viability monitor
SpeziLLM
Swift package for on-device and hosted LLM inference inside Stanford's Spezi health-app framework
For a Swift shop already inside Spezi, SpeziLLM is a practical way to keep sensitive health data on-device and fall back to a hosted model behind one call site. The module split means you are not married to a single provider. Just budget for reading source: version 0.13.8 is pre-1.0 and the docs won't walk you through an app.
Verdict generatedVerified 5d ago · liveness 56/100 · cite: rightaichoice.com/tools/spezillm
- iOS health developers who need local LLM inference for sensitive data with a hosted fallback
- Swift teams already standardized on the Spezi framework
- Health informatics researchers prototyping privacy-preserving LLM features in Swift
- Apple-platform apps that want to switch between local and hosted models without rewriting the call site
- Android, web, or cross-platform products needing LLM support
- Teams wanting low-code or no-code LLM integration
- Projects that require a frozen, stable API before v1.0 (current version 0.13.8)
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Skip SpeziLLM if your product also ships on Android or the web, if you need a no-code integration, or if you require a frozen stable API today — it is at 0.13.8 and Swift/Apple-platform only.
The package is MIT-licensed with no fees of its own, but every hosted call through SpeziLLMOpenAI, SpeziLLMAnthropic, or SpeziLLMGemini is billed to you directly by that provider.
The package itself costs nothing under an MIT license, with no license fees or per-call charges — the real spend is whatever you pay OpenAI, Anthropic, or Gemini for hosted calls, plus the engineering time to work against a 0.13.8 API. That makes it cheaper than a commercial mobile-LLM SDK with seat licensing, but more expensive in staff hours than a managed no-code integration.
In short
SpeziLLM — Swift package for on-device and hosted LLM inference inside Stanford's Spezi health-app framework. Best for iOS health developers who need local LLM inference for sensitive data with a hosted fallback, Swift teams already standardized on the Spezi framework, Health informatics researchers prototyping privacy-preserving LLM features in Swift. Free to use.
What is SpeziLLM?
SpeziLLM — Swift package for on-device and hosted LLM inference inside Stanford's Spezi health-app framework. Best for iOS health developers who need local LLM inference for sensitive data with a hosted fallback, Swift teams already standardized on the Spezi framework, Health informatics researchers prototyping privacy-preserving LLM features in Swift. Free to use.
SpeziLLM is an MIT-licensed Swift package from Stanford's Spezi ecosystem that gives Apple-platform apps a single interface for running language models either on-device or against a hosted provider. Version 0.13.8, documented on 4 April 2026, splits functionality into discrete modules: SpeziLLMLocal for on-device execution, SpeziLLMLocalDownload for fetching and managing model artifacts, and SpeziLLMOpenAI, SpeziLLMAnthropic, and SpeziLLMGemini for hosted inference. A SpeziLLMFog module covers fog/edge scenarios. The package supports streaming token responses, token-aware context management, and backend selection configured in code, so a call site can move between local and cloud without a rewrite. Where it earns its keep is health software: surrounding Spezi modules provide secure data handling and FHIR-compatible modeling, which matters when a language model is touching patient-adjacent data. It is also marked Ready for Swift 6. The trade-offs are structural — Apple platforms and Swift only, an assumed Spezi context, a pre-1.0 API at 0.13.8, and sparse tutorials. Teams wanting broad provider coverage across languages or fully managed inference should compare LangChain and plain CoreML instead.
Key features
- Unified Swift interface for local and hosted LLM inference
- On-device model execution via SpeziLLMLocal
- On-device model download and artifact management via SpeziLLMLocalDownload
- Hosted inference via SpeziLLMOpenAI
- Hosted inference via SpeziLLMAnthropic
- Hosted inference via SpeziLLMGemini
- Fog/edge inference via SpeziLLMFog
- Streaming token responses
- Token-aware context management
- Selectable model backends configured in code
- Integration with the Spezi health-app framework
- Secure data handling through surrounding Spezi modules
- FHIR-compatible data modeling via Spezi modules
- MIT-licensed open source
- Ready for Swift 6
Behind the verdict
We'd reach for SpeziLLM in a specific situation: an iOS health app on the Spezi framework that needs an LLM to handle data it would rather not ship to a third party. The on-device modules do that work, and the hosted modules give you an escape hatch when a local model isn't good enough. That combination is the reason to care. The modular layout is the most useful design decision here. SpeziLLMLocalDownload handles the chores of getting model artifacts onto a device, and the provider-specific modules keep each API surface separate instead of one bloated abstraction. In practice, moving a call from local to OpenAI or Anthropic to Gemini is a configuration change, not a rewrite, and streaming plus token-aware context management are already wired in. Where it bites: the tutorials are thin and the API sits at 0.13.8. Plan on reading the source and pinning a version. Much of what SpeziLLM does well is inherited from the surrounding Spezi ecosystem — secure data handling and FHIR modeling come from sibling modules, so if you are not using Spezi, you are paying for context you won't use. Pick this when you are an Apple-platform team inside Spezi with a privacy requirement and a fallback-to-cloud plan. Pass when you need one provider-agnostic SDK across iOS, Android, and web (LangChain covers more ground), when you cannot tolerate a pre-1.0 API, or when a plain CoreML wrapper with a hosted call would do the job smaller.
Models under the hood
as of 2026-10-30
Limitations
- SpeziLLM is documented at version 0.13.8 with module-level DocC entries (SpeziLLMLocal, SpeziLLMLocalDownload, SpeziLLMOpenAI, SpeziLLMFog, SpeziLLMAnthropic, SpeziLLMGemini) rather than step-by-step guides, so expect to work from source and symbol docs.
- It is built on the Spezi framework and requires Swift (Ready for Swift 6), so adopting it typically means adopting that ecosystem.
- Response quality, latency, and hardware fit are determined entirely by whichever model backend you plug in, not by the package.
as of 2026-10-15
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Is SpeziLLM still active?
SpeziLLM is live · free · last verified 25 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is SpeziLLM? 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 do users say about SpeziLLM?
What people actually say about SpeziLLM — is it worth it?
We scanned public community sources for SpeziLLM on Sep 15, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 3 of the posts we fetched could be positively tied to SpeziLLM. 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 SpeziLLM— 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 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified SpeziLLM 11 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 platforms, limitations
- — changed pricing page, description, summary, pricing tiers, our verdict, who it suits, features
- — changed SSL certificate, features, description, our verdict, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed our verdict, integrations, who it suits, features, description, pricing tiers, summary, who should skip it, SSL certificate
- — changed platforms, pricing page, limitations
- — changed summary, setup time, who should skip it, our verdict, limitations, switching in, hidden costs, switching out, who it suits, use cases, features, description, pricing tiers, pricing fit
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does SpeziLLM cost in 2026?
SpeziLLM: Free to use. Pricing last verified 25 Oct 2026.
Plans compared
Every published SpeziLLM 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 |
|---|---|---|---|
| Open Source | Free | — | Swift teams building iOS or macOS health apps inside the Spezi framework who want local and hosted LLM inference without a license feeAdds: Starting tier: MIT-licensed access to SpeziLLMLocal, SpeziLLMLocalDownload, SpeziLLMOpenAI, SpeziLLMFog, SpeziLLMAnthropic, and SpeziLLMGemini, with hosted provider usage billed separately by OpenAI, Anthropic, or Gemini |
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 SpeziLLM's pricing actually pencils out — and where peers do it cheaper.
The package itself costs nothing under an MIT license, with no license fees or per-call charges — the real spend is whatever you pay OpenAI, Anthropic, or Gemini for hosted calls, plus the engineering time to work against a 0.13.8 API. That makes it cheaper than a commercial mobile-LLM SDK with seat licensing, but more expensive in staff hours than a managed no-code integration.
Has SpeziLLM's pricing changed?
No plan price for SpeziLLM has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick SpeziLLM?
Pick SpeziLLM if you are iOS health developers who need local LLM inference for sensitive data with a hosted fallback, Swift teams already standardized on the Spezi framework, Health informatics researchers prototyping privacy-preserving LLM features in Swift, Apple-platform apps that want to switch between local and hosted models without rewriting the call site.
Look elsewhere if you are Android, web, or cross-platform products needing LLM support, Teams wanting low-code or no-code LLM integration, Projects that require a frozen, stable API before v1.0 (current version 0.13.8), Developers outside the Spezi ecosystem who want a single standalone dependency and thorough tutorials.
Skip it if Skip SpeziLLM if your product also ships on Android or the web, if you need a no-code integration, or if you require a frozen stable API today — it is at 0.13.8 and Swift/Apple-platform only.
GeneratedReal-world workflow fit
Concrete scenarios for the personas SpeziLLM actually fits — and what changes day-one when you adopt it.
You add SpeziLLMLocal to an existing Spezi app, use SpeziLLMLocalDownload to pull a model artifact onto the device, and wire a clinical-reasoning screen to it so prompts never leave the phone.
Outcome: A working offline inference path in your app, with the same call site ready to route to SpeziLLMOpenAI when you later want a hosted fallback.
You build two thin test harnesses against the same interface — one using SpeziLLMLocal, one using SpeziLLMAnthropic — and run the same set of de-identified health texts through both to compare output quality and latency.
Outcome: A measured local-versus-cloud comparison you can put in a paper, without maintaining two separate integration codebases.
You model intake conversation data with the FHIR-compatible Spezi modules and connect the chat flow to SpeziLLMGemini, keeping the inference backend swappable in code.
Outcome: A FHIR-aligned intake chatbot where switching Gemini for a local model later is a configuration change rather than a rewrite.
Use cases
- Run a local model for offline clinical reasoning in an iOS health app
- Generate patient education summaries through the OpenAI backend inside a Spezi app
- Build a FHIR-compatible chatbot for medical history intake
- Benchmark local versus cloud backends for health text classification
- Keep sensitive prompts on-device while escalating harder queries to a hosted model
- Add a fog/edge inference tier via SpeziLLMFog for deployments that cannot use a public cloud endpoint
Setup time & first value
How long it actually takes to get something useful out of SpeziLLM — broken out by persona, not the marketing-page minute.
For an existing Spezi app in Swift, expect a few hours to add a module, configure a backend, and get streaming responses rendering — faster for a hosted module like SpeziLLMOpenAI, slower for SpeziLLMLocal plus SpeziLLMLocalDownload, where you also wait on a model artifact download and tune it to the device. Outside Spezi, add days to weeks to absorb the framework. Expect to read source rather
Switching to or from SpeziLLM
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From raw OpenAI/Anthropic/Gemini Swift SDKs: replace per-provider client code with the corresponding SpeziLLM module and keep one call site across backends.
- →From CoreML-only on-device inference: keep the model on-device via SpeziLLMLocal and gain a hosted fallback through SpeziLLMOpenAI, SpeziLLMAnthropic, or SpeziLLMGemini.
- →From a non-Spezi health app codebase: adopt the surrounding Spezi modules for secure data handling and FHIR-compatible modeling first, then add SpeziLLM.
- ↗To plain CoreML: strip SpeziLLMLocal and call your CoreML model directly if you only ever need on-device inference.
- ↗To a provider's own Swift SDK: move each SpeziLLM provider module back to the vendor SDK if you drop multi-backend swapping.
- ↗To a cross-platform LLM framework such as LangChain: port prompt and context logic to a language runtime your Android or web client can share.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “SpeziLLM”, and we withheld 6: 6 could not be judged, because “SpeziLLM” 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 SpeziLLM.
Official links
What are the best SpeziLLM alternatives?
The closest alternatives to SpeziLLM we list are SwiftOpenAI, Private Gpt and Ruby Llm. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| SwiftOpenAI | Free | Free | — |
| Private Gpt | Free | Free | — |
| Ruby Llm | Free | Free | — |
Alternatives to SpeziLLM
View all
SwiftOpenAI
SwiftOpenAI: an MIT-licensed Swift package for calling the OpenAI API with native async/await and Combine on Apple platforms.
Private Gpt
PrivateGPT is the open-source on-premise RAG framework for local document Q&A — 57k GitHub stars, zero cloud calls.
Tools that pair well with SpeziLLM
Common stack mates teams adopt alongside SpeziLLM, with the specific reason each pairing earns its keep.
SwiftOpenAI
SwiftOpenAI: an MIT-licensed Swift package for calling the OpenAI API with native async/await and Combine on Apple platforms.
Private Gpt
PrivateGPT is the open-source on-premise RAG framework for local document Q&A — 57k GitHub stars, zero cloud calls.
Ruby Llm
RubyLLM is the Ruby-native AI framework for chat, agents, tools, and RAG across 19 providers with one API.
SpeziLLM vs its closest rival
SpeziLLM's closest head-to-head is with Rapidsos — If you need to enhance emergency response with AI-powered dispatch and real-time device data, RapidSOS is the enterprise-grade choice, despite contact-based pricing and US focus. For iOS developers building privacy-centric health apps that use LLMs, SpeziLLM offers a free, open-source integration. These tools serve completely different domains. Verdict generated
Head-to-head comparisons
Spezillm vs Rapidsos
If you need to enhance emergency response with AI-powered dispatch and real-time device data, RapidSOS is the enterprise-grade choice, despite contact-based pricing and US focus. For iOS developers building privacy-centric health apps that use LLMs, SpeziLLM offers a free, open-source integration. These tools serve completely different domains.
GeneratedSpezillm vs Isomorphic Labs
SpeziLLM is the clear choice for Swift developers building privacy-focused health apps with LLM integration, thanks to its free, open-source, and modular design. Isomorphic Labs is a high-cost, partnership-only platform for large pharma tackling drug discovery at scale, leveraging AlphaFold and recent massive funding rounds. Your decision hinges on whether you need a developer toolkit for health apps or a bespoke AI drug discovery partner.
GeneratedSpezillm vs Codametrix
CodaMetrix is the clear choice for large health systems seeking proven ROI in medical coding automation, with 70% reduction in manual effort and deep EHR integration. SpeziLLM is ideal for Swift/iOS developers embedding LLMs into privacy-focused health apps. Choose based on scale and technical environment: enterprise-wide automation vs. developer flexibility.
GeneratedFrequently asked questions
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