Compare
SGLang, Azure OpenAI Service, Amazon Bedrock vs Mistral Le Chat
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
- Private LLM
- Also
- Document Q&A · Private company ChatGPT · Internal company search
- 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 | SGLangToolSTRONG ALTERNATIVEfor self-hosted under this brief | Azure OpenAI ServiceToolCONSIDER IF…for enterprise SaaS under this brief | Amazon BedrockToolCONSIDER IF…for enterprise SaaS under this brief | Mistral Le ChatToolCONSIDER IF…for enterprise SaaS under this brief |
|---|---|---|---|---|
| Deployment modelRegistry entry: how this is delivered, and which solution class it is scored as. | Self-hosted The registry records SGLang as available on self-hosted or private cloud or on-premise enterprise; it is scored here as self-hosted. ASSESSMENT | Enterprise SaaS The registry records Azure OpenAI Service as available on vendor cloud or private cloud; it is scored here as enterprise SaaS. ASSESSMENT | Enterprise SaaS The registry records Amazon Bedrock as available on vendor cloud or private cloud; it is scored here as enterprise SaaS. ASSESSMENT | Enterprise SaaS The registry records Mistral Le Chat as available on vendor cloud or private cloud or on-premise enterprise; it is scored here as enterprise SaaS. 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 | CONSIDER IF… For this brief — confidential documents — enterprise SaaS is conditional: worth it only if the conditions in the options section hold. Change the brief and this verdict changes. RECOMMENDATION | CONSIDER IF… For this brief — confidential documents — enterprise SaaS is conditional: worth it only if the conditions in the options section hold. Change the brief and this verdict changes. RECOMMENDATION | CONSIDER IF… For this brief — confidential documents — enterprise SaaS is conditional: worth it only if the conditions in the options section hold. 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). | 60% Covers your main objective (private LLM) and 0 of 3 secondary objectives. ASSESSMENT | 100% Covers your main objective (private LLM) and 3 of 3 secondary objectives (document Q&A, private company ChatGPT, internal company search). ASSESSMENT | 100% Covers your main objective (private LLM) and 3 of 3 secondary objectives (document Q&A, private company ChatGPT, internal company search). ASSESSMENT | 87% Covers your main objective (private LLM) and 2 of 3 secondary objectives (document Q&A, private company ChatGPT). 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 SGLang, so this is read from “Private company knowledge base (self-hosted RAG)”, the best-matching self-hosted deployment for this brief. No edge in this design crosses out of the company network. ASSESSMENT | yes External data transfer: yes. No catalogue deployment names Azure OpenAI Service, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Employees → Vendor assistant (web and desktop clients) link. ASSESSMENT | yes External data transfer: yes. No catalogue deployment names Amazon Bedrock, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Employees → Vendor assistant (web and desktop clients) link. ASSESSMENT | yes External data transfer: yes. No catalogue deployment names Mistral Le Chat, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Confidential content leaves your control on the Employees → Vendor assistant (web and desktop clients) link. 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 | unknown We hold no fetched residency commitment for Azure OpenAI Service. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in the United States, which is where a transfer question starts, not where it ends. ASSESSMENT | unknown We hold no fetched residency commitment for Amazon Bedrock. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in the United States, which is where a transfer question starts, not where it ends. ASSESSMENT | unknown We hold no fetched residency commitment for Mistral Le Chat. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in European Union, which is where a transfer question starts, not where it ends. 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 | unknown We have fetched none of the four commitments for Azure OpenAI Service: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent. ASSESSMENT | unknown We have fetched none of the four commitments for Amazon Bedrock: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent. ASSESSMENT | unknown We have fetched none of the four commitments for Mistral Le Chat: data processing agreement, subprocessor list, training on customer data, zero retention option. Each is a question for the vendor, and until it is answered it is unknown rather than absent. ASSESSMENT |
| Integration effortThe FDE-day band the catalogue deployment was costed from (RECIPE_IMPLEMENTATION_DAYS). | 8–20 FDE-days 8–20 FDE-days to implement. No catalogue deployment names SGLang, so this is read from “Private company knowledge base (self-hosted RAG)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | 4–12 FDE-days 4–12 FDE-days to implement. No catalogue deployment names Azure OpenAI Service, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. ASSESSMENT | 4–12 FDE-days 4–12 FDE-days to implement. No catalogue deployment names Amazon Bedrock, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. ASSESSMENT | 4–12 FDE-days 4–12 FDE-days to implement. No catalogue deployment names Mistral Le Chat, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for 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, the identity integration and joiner/leaver process, operating-system patching and backups. Assessed difficulty 3/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | vendor-operated The vendor runs the service. Your standing work is access control, the policy staff work under, and re-reading the contract and subprocessor list when they change — not patching or capacity. ASSESSMENT | vendor-operated The vendor runs the service. Your standing work is access control, the policy staff work under, and re-reading the contract and subprocessor list when they change — not patching or capacity. ASSESSMENT | vendor-operated The vendor runs the service. Your standing work is access control, the policy staff work under, and re-reading the contract and subprocessor list when they change — not patching or capacity. 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. | 3/5 3/5. 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 1/5: a vendor product, configured rather than installed. +0.5 Single sign-on adds an identity provider, group-to-role mapping and a joiner/leaver process to the deployment. ASSESSMENT | 2/5 2/5. Starting point 1/5: a vendor product, configured rather than installed. +0.5 Single sign-on adds an identity provider, group-to-role mapping and a joiner/leaver process to the deployment. ASSESSMENT | 2/5 2/5. Starting point 1/5: a vendor product, configured rather than installed. +0.5 Single sign-on adds an identity provider, group-to-role mapping and a joiner/leaver process to the deployment. 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 10,000 – 48,000 USD 10,000 – 48,000 — one-off — for SGLang at this headcount. No catalogue deployment names SGLang, so this is read from “Private company knowledge base (self-hosted RAG)”, the best-matching self-hosted deployment for this brief. ASSESSMENT | USD 3,100 – 23,000 + licences USD 3,100 – 23,000 — one-off — for Azure OpenAI Service at this headcount. No catalogue deployment names Azure OpenAI Service, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Licences are not in that figure: no per-seat price has been read from the vendor’s own pricing page, so the software line is excluded from the total rather than guessed. Read it as implementation only. ASSESSMENT | USD 3,100 – 23,000 + licences USD 3,100 – 23,000 — one-off — for Amazon Bedrock at this headcount. No catalogue deployment names Amazon Bedrock, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Licences are not in that figure: no per-seat price has been read from the vendor’s own pricing page, so the software line is excluded from the total rather than guessed. Read it as implementation only. ASSESSMENT | USD 3,100 – 23,000 + licences USD 3,100 – 23,000 — one-off — for Mistral Le Chat at this headcount. No catalogue deployment names Mistral Le Chat, so this is read from “Enterprise SaaS assistant with governance controls”, the best-matching enterprise SaaS deployment for this brief. Licences are not in that figure: no per-seat price has been read from the vendor’s own pricing page, so the software line is excluded from the total rather than guessed. Read it as implementation only. ASSESSMENT |
| CustomisationRegistry entry: whether the source is open and where the deployment can be changed. | source available The registry files SGLang 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 Azure OpenAI Service is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. ASSESSMENT | vendor-configured Amazon Bedrock is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. ASSESSMENT | vendor-configured Mistral Le Chat is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. 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 | 8 issues · 0 cited 8 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law. ASSESSMENT | 8 issues · 0 cited 8 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law. ASSESSMENT | 8 issues · 0 cited 8 issues spotted for this brief, 0 of them legal requirements; 0 carry a quote from the instrument they rest on. Leading with: Confidentiality duties bind independently of data protection law. ASSESSMENT |
Comparing
- SGLangremove SGLang from this comparison
- Azure OpenAI Serviceremove Azure OpenAI Service from this comparison
- Amazon Bedrockremove Amazon Bedrock from this comparison
- Mistral Le Chatremove Mistral Le Chat from this comparison
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.
- Google Vertex AI
- LibreChat
- LiteLLM
- llama.cpp
- LM Studio
- LocalAI
- Ollama
- Open WebUI
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 private 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 SGLang, so its cost, effort and difficulty are read from “Private company knowledge base (self-hosted RAG)”, the closest self-hosted deployment for this brief.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names Azure OpenAI Service, so its cost, effort and difficulty are read from “Enterprise SaaS assistant with governance controls”, the closest enterprise SaaS deployment for this brief.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names Amazon Bedrock, so its cost, effort and difficulty are read from “Enterprise SaaS assistant with governance controls”, the closest enterprise SaaS deployment for this brief.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names Mistral Le Chat, so its cost, effort and difficulty are read from “Enterprise SaaS assistant with governance controls”, the closest enterprise SaaS 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.