Compare
Together AI, Anthropic API vs Baseten
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
- Agent workflow
- Also
- Choosing a model · Local LLM
- 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 | Together AIToolSTRONG ALTERNATIVEfor a model vendor's API under this brief | Anthropic APIToolSTRONG ALTERNATIVEfor a model vendor's API under this brief | BasetenToolSTRONG ALTERNATIVEfor a model vendor's API under this brief |
|---|---|---|---|
| Deployment modelRegistry entry: how this is delivered, and which solution class it is scored as. | Model API The registry records Together AI as available only on vendor_api; it is scored here as a model vendor's API. ASSESSMENT | Model API The registry records Anthropic API as available only on vendor_api; it is scored here as a model vendor's API. ASSESSMENT | Model API The registry records Baseten as available on vendor_api or private cloud; it is scored here as a model vendor's API. 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 — a model vendor's API is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION | STRONG ALTERNATIVE For this brief — confidential documents — a model vendor's API is a strong alternative. Change the brief and this verdict changes. RECOMMENDATION | STRONG ALTERNATIVE For this brief — confidential documents — a model vendor's API 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). | 100% Covers your main objective (agent workflow) and 2 of 2 secondary objectives (choosing a model, local LLM). ASSESSMENT | 80% Covers your main objective (agent workflow) and 1 of 2 secondary objectives (choosing a model). ASSESSMENT | 100% Covers your main objective (agent workflow) and 2 of 2 secondary objectives (choosing a model, local LLM). 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. You did not name a cloud you already run on, so no design is favoured on that basis. ASSESSMENT | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. You did not name a cloud you already run on, so no design is favoured on that basis. ASSESSMENT | 74% You stated no hosting preference, so no option is favoured on that basis. No hard restriction on where processing happens was stated. You did not name a cloud you already run on, so no design is favoured on that basis. ASSESSMENT |
| Data controlExternal transfer computed from the architecture graph of the deployment (computeExternalTransfer) — what actually crosses out of your network. | yes External data transfer: yes. No catalogue deployment names Together AI, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. Confidential content leaves your control on the Tools + retrieval (your APIs and data) → Vendor model API link. ASSESSMENT | yes External data transfer: yes. Computed from “Agent workflow on a model vendor’s API”, the catalogue deployment that names Anthropic API and best matches this brief. Confidential content leaves your control on the Tools + retrieval (your APIs and data) → Vendor model API link. ASSESSMENT | yes External data transfer: yes. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. Confidential content leaves your control on the Tools + retrieval (your APIs and data) → Vendor model API link. ASSESSMENT |
| Data residencyVendor documents we have fetched. A residency commitment we have not read is “unknown” — never assumed (CONTENT-RULES §5). | unknown We hold no fetched residency commitment for Together AI. That is a question to put to the vendor, not an assumption to make about them. ASSESSMENT | unknown We hold no fetched residency commitment for Anthropic API. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in United States, which is where a transfer question starts, not where it ends. ASSESSMENT | unknown We hold no fetched residency commitment for Baseten. That is a question to put to the vendor, not an assumption to make about them. The vendor is headquartered in United States, 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. | unknown We have fetched none of the four commitments for Together AI: 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 Anthropic API: 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 Baseten: 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). | 8–22 FDE-days 8–22 FDE-days to implement. No catalogue deployment names Together AI, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. ASSESSMENT | 8–22 FDE-days 8–22 FDE-days to implement. Computed from “Agent workflow on a model vendor’s API”, the catalogue deployment that names Anthropic API and best matches this brief. ASSESSMENT | 8–22 FDE-days 8–22 FDE-days to implement. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API 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 the identity integration and joiner/leaver process, the connectors and their credentials, operating-system patching and backups. Assessed difficulty 3/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | you operate it You own the identity integration and joiner/leaver process, the connectors and their credentials, operating-system patching and backups. Assessed difficulty 3/5 is the shape of that work; someone has to hold it after go-live. ASSESSMENT | you operate it You own the identity integration and joiner/leaver process, the connectors and their credentials, operating-system patching and backups. Assessed difficulty 3/5 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. | 3/5 3/5. Starting point 2/5: software you install, run and keep running on your own machines. -0.5 The model runs on the vendor’s infrastructure: there is no capacity planning, no GPU driver, no model upgrade window and no failover for you to design. ASSESSMENT | 3/5 3/5. Starting point 2/5: software you install, run and keep running on your own machines. -0.5 The model runs on the vendor’s infrastructure: there is no capacity planning, no GPU driver, no model upgrade window and no failover for you to design. ASSESSMENT | 3/5 3/5. Starting point 2/5: software you install, run and keep running on your own machines. -0.5 The model runs on the vendor’s infrastructure: there is no capacity planning, no GPU driver, no model upgrade window and no failover for you to design. 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 6,100 – 43,000 USD 6,100 – 43,000 — one-off — for Together AI at this headcount. No catalogue deployment names Together AI, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. ASSESSMENT | USD 6,100 – 43,000 USD 6,100 – 43,000 — one-off — for Anthropic API at this headcount. Computed from “Agent workflow on a model vendor’s API”, the catalogue deployment that names Anthropic API and best matches this brief. ASSESSMENT | USD 6,100 – 43,000 USD 6,100 – 43,000 — one-off — for Baseten at this headcount. No catalogue deployment names Baseten, so this is read from “Agent workflow on a model vendor’s API”, the best-matching a model vendor's API deployment for this brief. ASSESSMENT |
| CustomisationRegistry entry: whether the source is open and where the deployment can be changed. | vendor-configured Together AI is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. ASSESSMENT | vendor-configured Anthropic API is a vendor product: you configure what the vendor exposes — policies, connectors, retention settings — and nothing below that line. ASSESSMENT | vendor-configured Baseten 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. | 9 issues · 0 cited 9 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 | 9 issues · 0 cited 9 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 | 9 issues · 0 cited 9 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
- Together AIremove Together AI from this comparison
- Anthropic APIremove Anthropic API from this comparison
- Basetenremove Baseten from this comparison
Add one that serves the same objective
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 agent workflow — 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 Together AI, so its cost, effort and difficulty are read from “Agent workflow on a model vendor’s API”, the closest a model vendor's API deployment for this brief.
- MINORCompare recipe substitutedcompare_recipe_substituted
No catalogue deployment names Baseten, so its cost, effort and difficulty are read from “Agent workflow on a model vendor’s API”, the closest a model vendor's API 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.