Compare
LocalAI, LM Studio, Tabby vs Open WebUI
Every row below is computed for the brief in section 01. Nothing here is a universal score.
- Source
- Registry and rules engines over one brief — nothing on this page is anchored in a fetched document yet
- Evidence
- none on this page — it links to the pages that hold it
01The brief this answers
No context was supplied, so this is a generic reading. Every figure below would move for a real brief.
The same tool can score differently for a different brief.
- Objective
- Local LLM
- Also
- Private LLM · Document Q&A
- Jurisdictions
- none stated
- Industry
- unknown
- Employees
- unknown
- Data
- confidential yes · privileged unknown · personal likely · sensitive unknown
- Local processing
- not required
- Cloud allowed
- unknown
- Users
- unknown
- Capability
- unknown
- Preference
- no preference
- Data risk
- high
interpreted by: rules (deterministic mode) · confidence 40%
unresolved: organisation.industry, requirements.dataResidency, requirements.existingStack, budget, jurisdictions.primary, organisation.employees, data sensitivity, deploymentPreference, technicalCapability
Not accounted for
Nothing in this comparison depends on your industry, any residency requirement, the systems you already run, your budget, the jurisdiction you operate in, your headcount, how sensitive the material is, a hosting preference or the technical capability you have in-house — none of it was supplied. Where a figure would move for one of those, it is stated as an assumption rather than hidden inside the number.
02Comparison
| Dimension | LocalAIToolSTRONG ALTERNATIVEfor self-hosted under this brief | LM StudioToolSTRONG ALTERNATIVEfor self-hosted under this brief | TabbyToolSTRONG ALTERNATIVEfor self-hosted under this brief | Open WebUIToolSTRONG ALTERNATIVEfor self-hosted under this brief |
|---|---|---|---|---|
| Deployment modelRegistry entry: how this is delivered, and which solution class it is scored as. | Self-hosted The registry records LocalAI as available on self-hosted or private cloud; it is scored here as self-hosted. ASSESSMENT | Self-hosted The registry records LM Studio as available only on self-hosted; it is scored here as self-hosted. ASSESSMENT | Self-hosted The registry records Tabby as available on self-hosted or private cloud or on-premise enterprise; it is scored here as self-hosted. ASSESSMENT | Self-hosted The registry records Open WebUI as available on self-hosted or private cloud or on-premise enterprise; it is scored here as self-hosted. ASSESSMENT |
| Verdict for this briefSolution-class analysis (buildOptions) over this brief. The verdict is on the class of answer, not on the product. | STRONG ALTERNATIVE For this brief — confidential documents — self-hosted is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION | STRONG ALTERNATIVE For this brief — confidential documents — self-hosted is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION | STRONG ALTERNATIVE For this brief — confidential documents — self-hosted is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION | STRONG ALTERNATIVE For this brief — confidential documents — self-hosted is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION |
| Functional fitOntology overlap between the objective in this brief and the use cases the registry records for the subject (matchRecipes). | 80% Covers your main objective (local LLM) and 1 of 2 secondary objectives (private LLM). ASSESSMENT | 100% Covers your main objective (local LLM) and 2 of 2 secondary objectives (private LLM, document Q&A). ASSESSMENT | 60% Covers your main objective (local LLM) and 0 of 2 secondary objectives. ASSESSMENT | 100% Covers your main objective (local LLM) and 2 of 2 secondary objectives (private LLM, document Q&A). ASSESSMENT |
| Deployment fitHosting preference, data egress, headcount band and technical capability, weighted 0.30 / 0.35 / 0.15 / 0.20 (matchRecipes). | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. Headcount was not stated, so the size band could not be checked. ASSESSMENT | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. Headcount was not stated, so the size band could not be checked. ASSESSMENT | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. Headcount was not stated, so the size band could not be checked. ASSESSMENT | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. Headcount was not stated, so the size band could not be checked. ASSESSMENT |
| Data controlExternal transfer computed from the architecture graph of the deployment (computeExternalTransfer) — what actually crosses out of your network. | none External data transfer: none. No catalogue deployment names LocalAI, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. No edge in this design crosses out of the company network. ASSESSMENT | none External data transfer: none. No catalogue deployment names LM Studio, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. No edge in this design crosses out of the company network. ASSESSMENT | none External data transfer: none. Computed from “AI coding assistant with private code”, the catalogue deployment that names Tabby and best matches this brief. No edge in this design crosses out of the company network. ASSESSMENT | none External data transfer: none. Computed from “Private ChatGPT on your own server”, the catalogue deployment that names Open WebUI and best matches this brief. No edge in this design crosses out of the company network. ASSESSMENT |
| Data residencyVendor documents we have fetched. A residency commitment we have not read is “unknown” — never assumed (CONTENT-RULES §5). | you decide Self-hosted: the content stays wherever you run the server, so residency is a property of your own infrastructure rather than a vendor commitment. ASSESSMENT | you decide Self-hosted: the content stays wherever you run the server, so residency is a property of your own infrastructure rather than a vendor commitment. ASSESSMENT | you decide Self-hosted: the content stays wherever you run the server, so residency is a property of your own infrastructure rather than a vendor commitment. ASSESSMENT | you decide Self-hosted: the content stays wherever you run the server, so residency is a property of your own infrastructure rather than a vendor commitment. ASSESSMENT |
| Governance controlsThe four commitments a processor is asked for — DPA, subprocessor list, no training on your content, zero retention — counted against fetched vendor documents. | not applicable Run on your own infrastructure, so the vendor questions apply only to whatever you still buy. We hold none of the four commitments for this subject, and for a self-hosted deployment none of them is required. ASSESSMENT | not applicable Run on your own infrastructure, so the vendor questions apply only to whatever you still buy. We hold none of the four commitments for this subject, and for a self-hosted deployment none of them is required. ASSESSMENT | not applicable Run on your own infrastructure, so the vendor questions apply only to whatever you still buy. We hold none of the four commitments for this subject, and for a self-hosted deployment none of them is required. ASSESSMENT | not applicable Run on your own infrastructure, so the vendor questions apply only to whatever you still buy. We hold none of the four commitments for this subject, and for a self-hosted deployment none of them is required. ASSESSMENT |
| Integration effortThe FDE-day band the catalogue deployment was costed from (RECIPE_IMPLEMENTATION_DAYS). | 2–6 FDE-days 2–6 FDE-days to implement. No catalogue deployment names LocalAI, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | 2–6 FDE-days 2–6 FDE-days to implement. No catalogue deployment names LM Studio, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | 5–12 FDE-days 5–12 FDE-days to implement. Computed from “AI coding assistant with private code”, the catalogue deployment that names Tabby and best matches this brief. ASSESSMENT | 3–8 FDE-days 3–8 FDE-days to implement. Computed from “Private ChatGPT on your own server”, the catalogue deployment that names Open WebUI and best matches this brief. ASSESSMENT |
| Maintenance burdenWho operates the result, read from the solution class and the difficulty factors the deployment carries. | you operate it You own GPU drivers and the model server, operating-system patching and backups. Assessed difficulty 2/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | you operate it You own GPU drivers and the model server, operating-system patching and backups. Assessed difficulty 2/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | you operate it You own GPU drivers and the model server, operating-system patching and backups. Assessed difficulty 2/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | you operate it You own GPU drivers and the model server, operating-system patching and backups. Assessed difficulty 2/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT |
| Deployment difficultyDifficulty 1–5 for the deployment and for this team (scoreDifficulty): the same stack scores lower for an organisation with its own engineers. | 2/5 2/5. Starting point 2/5: software you install, run and keep running on your own machines. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own. ASSESSMENT | 2/5 2/5. Starting point 2/5: software you install, run and keep running on your own machines. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own. ASSESSMENT | 2/5 2/5. Starting point 2/5: software you install, run and keep running on your own machines. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own. ASSESSMENT | 2/5 2/5. Starting point 2/5: software you install, run and keep running on your own machines. +0.5 You run the model server yourself: GPU drivers, quantisation choice, memory headroom and restarts are all yours to own. ASSESSMENT |
| Estimated costIndicative cost for this headcount (estimateCost), plus any per-token price we have actually fetched. A price we have not read is not printed. | USD 5,500 – 21,000 USD 5,500 – 21,000 — one-off — for LocalAI at this headcount. No catalogue deployment names LocalAI, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | USD 5,500 – 21,000 USD 5,500 – 21,000 — one-off — for LM Studio at this headcount. No catalogue deployment names LM Studio, so this is read from “Local LLM inference server (Ollama / vLLM)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | USD 16,000 – 45,000 USD 16,000 – 45,000 — one-off — for Tabby at this headcount. Computed from “AI coding assistant with private code”, the catalogue deployment that names Tabby and best matches this brief. ASSESSMENT | USD 6,300 – 24,000 USD 6,300 – 24,000 — one-off — for Open WebUI at this headcount. Computed from “Private ChatGPT on your own server”, the catalogue deployment that names Open WebUI and best matches this brief. ASSESSMENT |
| CustomisationRegistry entry: whether the source is open and where the deployment can be changed. | source available The registry files LocalAI as open source, so the interface, retrieval behaviour and the model behind it can be changed — subject to the licence, which is a fetched fact on its own page. ASSESSMENT | vendor-configured LM Studio is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. ASSESSMENT | source available The registry files Tabby as open source, so the interface, retrieval behaviour and the model behind it can be changed — subject to the licence, which is a fetched fact on its own page. ASSESSMENT | source available The registry files Open WebUI as open source, so the interface, retrieval behaviour and the model behind it can be changed — subject to the licence, which is a fetched fact on its own page. ASSESSMENT |
| Relevant compliance evidenceThe compliance engine over this brief and this subject (assessCompliance), with each issue cited to the instrument it quotes where the page was fetched. | 7 issues · 0 cited 7 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Self-hosting moves the security obligation to you. ASSESSMENT | 7 issues · 0 cited 7 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Self-hosting moves the security obligation to you. ASSESSMENT | 7 issues · 0 cited 7 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Self-hosting moves the security obligation to you. ASSESSMENT | 7 issues · 0 cited 7 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Self-hosting moves the security obligation to you. ASSESSMENT |
Comparing
Add one that serves the same objective
This table is full at 4 subjects. Remove one to add another — beyond four, the columns stop being readable and the comparison stops being one.
- AnythingLLM
- Continue
- LiteLLM
- llama.cpp
- Ollama
- SGLang
- vLLM
03What this does not tell you
- WARNINGCompare no jurisdictioncompare_no_jurisdiction
No jurisdiction was supplied, so only the cross-cutting rules ran. A comparison for a regulated deployment should name one.
- MINORCompare objective derivedcompare_objective_derived
No objective was given, so functional fit is measured against local LLM — the use case most of these subjects share. Add one to the link to measure a different job.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names LocalAI, so its cost, effort and difficulty are read from “Local LLM inference server (Ollama / vLLM)”, the closest self-hosted deployment for this brief.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names LM Studio, so its cost, effort and difficulty are read from “Local LLM inference server (Ollama / vLLM)”, the closest self-hosted deployment for this brief.
Structured issue-spotting to support your own review — not legal advice. Verify against the cited primary sources and your counsel.
04Evidence
No sources were recorded for this answer. Nothing on this page should be treated as verified.