企业AI模型战略：如何选对模型、实现业务落地+AI+Model+Strategy+for+Enterprises+Choosing+the+Right+Model+and+Making+It+Work

AIMODELSTRATEGY

FORENTERPRISES

Choosingtherightmodelandmakingitwork

October2026

ImplicationsofAIModelChoicesfortheEnterprises2

TABLEOFCONTENTS

Foreword 3

Insightsbyte 4

Thechallenge 4

AImodellandscape 5

Comparisonofmodelapproaches 6

Capabilitiesofdifferentmodeltypes 7

Enterpriserequirementsfordeploymentandoperation 9

Costcomponents 11

Matchingmodelstoenterprisecontexts 15

Implicationsforindia 18

TheBottomLine 19

ImplicationsofAIModelChoicesfortheEnterprises3

FOREWORD

EnterpriseAIadoptionisenteringadecisivephase.Thefocushasshiftedfromexperimentationtodeterminingwhichmodelsanddeploymentapproachesbestserveorganizationalneeds.TheeconomicsandrisksofAIdeploymentnowgofarbeyondmodelpricing.Theyincludedataintegration,infrastructure,security,governance,operationalcomplexityandthecapabilitiesrequiredtomanagethem.

Inthisenvironment,leadersmustcraftstrategiesthatbalancetrade-offsacrosscapability,control,customisation,security,infrastructureandcost,whileorchestratingmodelstofitspecificworkloads.Thechallengeisnolongerjustselectingamodel,butensuringitisdeployedeffectively.

ForIndia,thisenvironmentpresentsauniqueopportunity.Byleveragingitsscale,linguisticdiversity,digitalpublicinfrastructureandtechnologycapabilities,thecountrycancreateanIndiaAIStack–aninteroperablelayerofmodels,infrastructureandservicesthatbringstogetherIndia-specificdatasets,sharedcompute,evaluationinfrastructureandopeninterfaces.Suchastackwouldenableaffordable,multilingualAIdeploymentacrossindustriesandpositionIndiaasaprominentplayerinenterpriseinnovation.

AtNasscom,webelievethatAIsuccesswillrestontwofundamentals:theabilitytomaketherightmodelchoicesandthecapacitytoadaptthemcontinuouslyastechnology,economicsandbusinessneedsevolve.Together,theseelementsarecrucialtobuildinggloballycompetitive,trustedandeconomicallysustainableAIenterprisesfromIndia.

ThisreportoffersaframeworktoguidetechnologyandbusinessleadersinnavigatingthesechoicesanddesigningAIarchitecturesnotonlyfortoday’smodels,butfortherapidlychangingecosystemahead.

ImplicationsofAIModelChoicesfortheEnterprises4

INSIGHTSBYTE

TheproliferationofAImodelshascreatedanewchallengeforenterprises:identifyingwhichmodeltypeisfitforpurpose,andunderstandingwhatittakestodeployandoperateeffectively.

Thereisnosingle‘best’AImodel.Eachchoiceinvolvestrade-offsbetweenperformance,control,customisation,security,deploymentcomplexityandcost.Formostenterprises,thepreferredchoicewilllikelybeamulti-modelportfolioapproach:frontiermodelsforgeneral-purposeandhigh-endreasoningworkloads;open-weightmodelswherecontrol,customisationoreconomicsjustify;andsmallerspecialisedmodelsforhigh-volumeoredgeworkloads.Increasingly,thisportfolioshouldsitbehindamodel-independentgateway/routerthatcanselectorswitchmodelsasneeded.

Modeleconomicsextendsbeyondheadlinepricing.Thetotalcostofdeploymentandoperatingdependsonmodeltype,workloadcharacteristicsandanenterprise’stechnologyandoperatingcapabilities.Itshouldbeassessedas:model/licence+inference+compute+data+integration+securityandgovernance+operations+talent.Theabilitytooptimisethisstack,notsimplyminimisemodelprice,willdeterminethereturnoninvestment(ROI)ofAI.

Fromanationalperspective,India’sopportunityliesincreatinganIndiaAIStack:areusableAIlayerthatcombinesindigenousmodels,IndiaStack,data,computeandenterprisecapabilities.Suchanecosystemcanscaledomesticallyandevolveintoanexportableadvantageforthecountry.

THECHALLENGE

Forenterprisestoday,AIadoptionhasmovedbeyondthequestionofwhethertouseit,tothechallengeofwhichmodelapproachbestfitsthetaskathand.Performance,capability,infrastructuredemands,cost,governanceandriskalldependonthischoice.

Thedecisionisnolongerasimpleopen-versus-closeddebate.Organizationsmustnownavigateaspectrum:powerfulclosedfrontiermodels,flexibleopen-weightmodels,compactspecialisedmodels,andhybridportfoliosthatcombinethem.Eachoptionbringsdistinctoperationalrequirementsandeconomictrade-offs.

Thisreportprovidesapracticalguidetohelpenterprisesalignmodelswithworkloads,designeffectivedeploymentstrategiesandmanageAIoperationswithconfidence.ItaimstoequipleaderstomovebeyondexperimentationandbuildAIarchitecturesthatareresilient,efficientandalignedwithbusinesspriorities.

ImplicationsofAIModelChoicesfortheEnterprises5

1.AIMODELLANDSCAPE

WORKINGDEFINITIONS

Closedmodel

Modelweightsandcoredevelopmentartefactsarenotmadeavailabletousers–accessistypicallyprovidedthroughanAPIormanagedservice.

Open-weightmodel

Modelweightsaremadeavailablefordownloadanddeployment–accesstosourcecode,trainingdata,trainingmethodologyandotherartefactsvariesacrossimplementations.

Open-sourcemodel

Abroaderconceptthatinvolvestheavailabilityofsourcecodeandlicensingtermspermittingspecifiedformsofuse,modificationandredistribution–thereisnouniversallyaccepteddefinitionofwhatconstitutesafullyopenmodel.

Proprietarymodel

Ownedandcontrolledbyacompanyunderits

intellectualpropertyandlicensingterms–suchmodelscanbeofferedthroughdifferentaccessmodels,

includingAPIsand,insomecases,downloadableweights.

TheterminologyusedtodescribeAImodelsis

ofteninconsistent.Termssuchas‘open’,

‘open-source’,‘open-weight’,‘closed’and

‘proprietary’arefrequentlyused

interchangeably,althoughtheyreferto

distinctcharacteristics.

Inenterprisecontexts,thesedescriptorsare

bestunderstoodasindependentdimensions

ratherthanmutuallyexclusivecategories.

Relevantdimensionsincludetheavailability

ofweightsandartefacts,thescopeoflicence

rights,themodeofdeployment(e.g.,

applicationprogramminginterface(API),

managedinference,private-hostingorself-

hosting),theproviderorownershipmodel,

andthesizeorspecialisationofthemodel.

Proprietarydescribesownershipandlicensing

arrangements,whilecloseddescribes

restrictionsonaccess.Thetermopendoesnot

representabinaryclassification;asingle

providermayofferbothclosedandopen-weightmodels.Fromanenterpriseperspective,themainconsiderationistheextenttowhichamodelcanbeaccessed,controlled,modifiedanddeployedindependently.

Practicaltaxonomyofmodelapproaches

Model

approach

Model

weights

access

Sourcecodeortrainingdataaccess

Self-host

Illustrativeexamples

Typicalmodeofenterprise

access

Closed

No

No

Generallyno

Claude(Anthropic),GPT

API/frontiermodels

(OpenAI),Gemini(Google)

API/managedservice

Yes

Varies

Yes,

subjecttolicence

Llama(Meta),Mistral

(MistralAI),Qwen(Alibaba),DeepSeek(DeekSeek),gpt-oss-120b/gpt-oss-20b

(OpenAI),selectedGLM(

ZhipuAI/Z.ai

),Kimi

(MoonshotAI),MiniMaxmodels,Sarvammodels,Param(BharatGen)

SelectNemotronmodels(NVIDIA)offeradditionalartefactavailability

Self-host,

managedcloudorAPI

Small/

specialisedmodels

Varies

Varies

Oftenyes

Domain-/task-specificmodels

API,cloudor

localdeployment

Source:NasscomResearch

Note:Opennessvariesacrossmodelcomponents.Theexamplesareillustrativeandshouldbetreatedasadatedsnapshot.Availabilityofweightsandlicencingtermsmustbeverifiedforeachmodelatthetimeofenterpriseselection

ImplicationsofAIModelChoicesfortheEnterprises6

2.COMPARISONOFMODELAPPROACHES

Thekeydriversofenterprisedecision-makinginvolvetrade-offsamongperformance,control,flexibility,deploymentcomplexityandeconomics.

ImplicationsofAIModelChoicesfortheEnterprises7

Detailedcomparisonofmodeltypes

Dimension

Closed

Open-weight

Modelaccess

API/managedservice

Weights+API/cloudoptions

Deploymentcontrol

Low

Relativelyhigh;deploymentdependent

Self-hosting

Generallyunavailable

Possible,subjecttolicenceterms

Customisation

Prompting/RAG/provider-supportedfine-tuning

Fine-tuning+deploymentcontrol

Datacontrol

Dependsonprovider/deployment

Potentiallyhigh

Infrastructureburden

Low

Medium-high,dependingondeployment

Vendordependencyandlock-in

Highatmodellayer;canbe

reducedthroughprovider-neutralgatewayiandotherportability

mechanisms

Relativelyloweratmodellevel;

dependencycanremainon

managedservices,servingstackii,cloudandacceleratoriiichoices

Timetodeploy

Fast

Mediumforself-hosted;fast-mediumformanaged

EnterpriseAIcapabilityrequired

Lower

Relativelyhigher

Costmodel

Primarilyusage/tokens

Variesbydeployment

-Self-hosted:compute+

engineeringiv+operationsv

-Managed:managed-servicepricing

Bestsuitedfor

Speed,experimentation,general-purposeworkloads

Control,customisation,scale

Source:NasscomResearch

Note:iProvider_neutralgatewaysofferacommoninterfaceacrossmodelsandproviders,enablingenterprisestoswitch

betweenthemwithoutmateriallyalteringapplications;iiServingstackreferstothesoftwareandinfrastructureusedtorunandservemodels;iiiacceleratorsreferprimarilytoGPUsandotherspecialisedAIcomputehardware;ivEngineering:

integration,customisation,servinganddataconnectivity;vOperations:monitoring,security,updates,scalingandongoingoptimisation

3.CAPABILITIESOFDIFFERENTMODELTYPES

Modelsshouldbeassessedagainstthecapabilitiesmostrelevanttoenterpriseworkloads.Nosinglemodelconsistentlyleadsacrossalldimensions;performancevariesbyworkload,size,deploymentenvironmentandcost.Enterprisesshouldthereforeassessmodelsbasedonworkloadratherthanoverallrankings.Keyevaluationcriteriaincludetasksuccessandquality,latency,throughput,costpersuccessfultask,tool-usereliability,safety,contexthandlingandoperationalavailability.Publicbenchmarkscanserveasscreeningsignalsbuttheyshouldnotbetheprimarybasisforenterprisedecision.

ImplicationsofAIModelChoicesfortheEnterprises8

Consequently,enterprisesshouldadoptaportfolioapproach–not‘oneenterprise,onemodel’,butamixofmodelsanddeploymentmodestailoredtodifferentworkloads,eachwithdistinctdeploymenteconomics.Forexample,alargeenterprisemaydeployafrontierclosedmodelforcomplexreasoning,anopen-weightmodelinaprivateenvironmentforsensitiveworkloads,andsmallerspecialisedmodelsforhigh-volumeoredgeapplications.

Illustrativeuse-casepositioning

Enterpriseworkload

Closed

frontier

models*

Open-

weight

models*

Smaller/specialisedmodels*

Rationale

Generalknowledgework

●●●

●●

(●●●in

select

cases)

●

Traditionally,frontiermodelsofferedstrongergeneral-purposereasoning,languageandmultimodal

capabilities,supportedbylarge-scaletraining,makingthemeffective

acrossvaried,unstructured

enterprisetasks.Recent

developmentsshowthatfrontier-classcapabilityisincreasingly

availableinleadingopen-weightmodels.

Suitabilityshouldthereforebe

validatedagainstenterprise-specificevaluationsforreasoning,language,multimodality,latencyandcost

ratherthanassumedsolelyfrommodelopenness.

Complexreasoning

●●●

●●●

●

Frontierandleadingopen-weight

modelsoffergreaterreasoningdepthandcontext-handlingcapability,

whichenablesmulti-stepanalysisandcomplexproblem-solving;

smallermodelstypicallytrade-offreasoningdepthforefficiency.

Coding

●●●

●●●

●●

Frontierandleadingopen-weightmodelsaretrainedextensivelyoncodeandcanhandlecomplex

softwaretasks;smallermodelscanbeeffectiveforrepetitive,well-

definedcoding,testingandcode-assistancetasksatlowerinferencecost.

Sensitive

enterprisedata

●●

●●●

●●●

Open-weightandsmallermodelscantypicallybedeployedincontrolled

environments,providinggreater

flexibilityoverdataresidencyandaccess,andpotentiallyenabling

privateinference.Closedmanagedmodelscanalsooperatewithin

approvedtrustboundaries.

ImplicationsofAIModelChoicesfortheEnterprises9

Enterprise

workload

Closed

frontier

models*

Open-

weight

models*

Smaller/specialisedmodels*

Rationale

Domain-specificworkflows

●●●

●●●

●●●

Allthreemodeltypescanbe

effectivewhenadaptedtowell-

definedworkflows.Open-weightandsmallermodelsoffergreater

customisationandoptimisation,

whileclosedmodelsprovidestrongout-of-the-boxcapability.

Overall,productionandoutcome

successdependsonaligningthe

chosenmodelwithdataquality,

domainexpertise,workflow

integrationandgovernancecontrols.

Scientific/technicalR&D

●●●

●●●

●●

Frontierandleadingopen-weight

modelsenablestrongmathematical,scientificandtechnicalreasoning

andtheabilitytoprocesscomplex

technicalcontext.Smallermodelsarebettersuitedtonarrower,well-

definedtasks;somecanoutperformlargermodelsonspecificscientificortechnicalbenchmarks.

High-volume,low-costtasks

●●

●●●

●●●

Open-weightandsmallermodels

oftendeliverbettereconomics,astheirlowerinferencecostsmake

themmorewell-suitedtorepetitive,predictableandlessreasoning-

intensiveworkloads.

Edge/offline

applications

—

●●

●●●

Smallermodelshavelowermemoryandcomputerequirements,which

makesiteasiertorunthemlocallyondevicesoredgeinfrastructure,

reducinglatencyanddependenceonnetworkconnectivity.

Source:NasscomResearch

*●●●:strongfit;●●:goodfitwithtrade-offs;●:limitedfit;—:generallyunsuitable

4.ENTERPRISEREQUIREMENTSFORDEPLOYMENTANDOPERATION

Choosingamodelisonlythefirststep.Enterprisesalsohavetobuildthesurroundingtechnologyandoperatingenvironmenttoconvertmodelcapabilityintoproductionapplications

1

.

Themaindifferencebetweenmodelapproachesliesinwhereresponsibilityresides.Closedmodelsusuallyreduceinfrastructureandoperationalburden,whileopen-weightmodelsprovidegreatercontrolbutshiftmoreengineering,andoperationalresponsibilitytotheenterprise.Thisisnotonlyaclosed-versus-open-weightissue:deploymentmodemattersasmuchasmodeltype.Managed

1Aproduction-gradeAIenvironmentalsorequirescapabilitiesbeyondthemodelitself,includingdataandcontextintegration,modelservingandrouting,securityandgovernance,toolandagentintegration,evaluationandmonitoring,andongoingperformanceandcostoptimisation.Theseaspectsarenotcoveredinthisnote.

ImplicationsofAIModelChoicesfortheEnterprises10

inferenceretainsmuchoftheprovider-operatedinfrastructure,whileself-hostingtransferssubstantiallymoreresponsibilitytotheenterprise.

EnterpriseAIdeploymentstack

Layer

Whatanenterpriseneeds

Closed

Open-weight

Modelserving

API/licence,modelserving,fine-tuning/customisation

API/managedservice,provider-dependent

forcustomisation

Greatercontrol,enterpriseormanagedmodelserving

Data

integration

Enterprisedataaccess,pipelines,RAG,data

governance

Enterprise

responsibility

Enterpriseresponsibility

Compute

CloudGPU,dedicated/ownedGPU,on-premise

infrastructure,inferenceoptimisation

GPUinfrastructurenotdirectlyrequiredfor

APIaccess

GPUinfrastructurerequiredifself-hosted

Enterprise

Integration

APIs,enterpriseapplications,identityandaccess

management,workflowintegration,

agent/orchestration

Enterprise

responsibility

Enterpriseresponsibility

Securityandgovernance

Dataprotection,modelsecurity,evaluation,

guardrails,compliance,provenance,patchability,misusecontrols

Sharedresponsibilitywithprovider;dependsondeploymentmode,trustboundaryand

contractualcontrols

Greaterenterprise

responsibility;control

benefitsmustbebalancedwithgreaterresponsibilityforprovenance,patching,accesscontrolsandmisuseprevention

Operations

LLMOps/MLOps,monitoringandobservability,model

updates,performanceandcostoptimisation

Loweroperationalburden

Higheroperationalburden

Model

optimisation

Inferenceoptimisation,modelselection,

performance/costtuning

Relativelylimited

Greaterflexibilityandresponsibility

Infrastructure

management

Provisioning,scaling,

availability,maintenance

Low

High,ifself-hosted

Model

gatewayandrouting

Providerabstraction,routingpolicy,fallback/failover,

versionpinning,modeleligibilitybydata

classification

Enterprise

responsibility,providerplatformsmayoffer

routingcapabilities

Enterpriseresponsibility,canrouteacrossmanagedandself-hostedmodels

ImplicationsofAIModelChoicesfortheEnterprises11

Layer

Whatanenterpriseneeds

Closed

Open-weight

Context,

evaluation

andpolicy

Prompt/context

management,workloadevaluations,safety/policyengine,regressiongates,auditevidence

Sharedresponsibilitybetweenproviderandenterprise

Greaterenterprise

responsibility,particularlyformodelevaluation,

policycontrolsandauditability

Agentandtoolruntime

Toolpermissions,agentidentity/secrets,

orchestration,circuitbreaking,trace-levelobservability

Enterprise

responsibility

Enterpriseresponsibility

Source:NasscomResearch

Graphicsprocessingunit(GPU)accessdoesnotnecessarilymeanGPUownership.Enterprisesusingopen-weightmodelsgenerallyhavefouroptions,makingcomputestrategyanimportantpartofmodelselection.

GPUoptionsforenterprises

Approach

Whatitmeans

Trade-off

OwnGPUs

Dedicatedon-premises/privateinfrastructure

Maximumcontrol,higherupfrontinvestmentandutilisationrisk

RentcloudGPUs

Need-basedGPUcapacityconsumption

Greaterflexibility,variableoperatingcost

Managedinference

Provideroperatedcomputeandservinglayer

Lowerinfrastructureburden,lesscontrol

Hybrid

Combinedowned/privateandrented/managedcompute

Balancebetweencontrolandflexibility,greaterarchitecturalcomplexity

Source:NasscomResearch

5.COSTCOMPONENTS

Amodel’slistedpriceisonlyoneelementofenterpriseAIeconomics.Thetotalcostofdeployingandoperatingamodeldependsonanenterprise’smodelapproach,workloadcharacteristics,

andtechnologyandoperatingcapabilities.Themajorcostheadsareasfollows.

ImplicationsofAIModelChoicesfortheEnterprises12

EnterpriseAIcoststack

Costhead

Closedmodel

Open-weightmodel

Modelaccess/licence

Usage/subscriptionfee

Typicallynomodelaccessfee,butlicencetermsvary

Inference

Token/usagecharges

Majorcost

Self-hosted:computerequiredforinference

Managed:token/capacitypricing

GPU/compute

Notdirectlyrequired,embeddedinproviderpricing

Majorcost

Self-hosted:directcost

Managed:embeddedinmanagedpricing

Storage

Low

Self-hosted:higher(modelweights,dataandinfrastructuremayrequireadditionalstorage)

Managed:couldbesimilartoclosedmodels

Dataengineering

Similar,enterpriseresponsibility

Similar,enterpriseresponsibility

Integration

Similar,enterpriseresponsibility

Similar,enterpriseresponsibility

Fine-

tuning/customisation

Provider-dependent,mayincuradditionalusageortraining

costs

Greaterflexibility,associatedcomputeandengineeringcosts

Securityand

governance

Medium,sharedwithprovider

Self-hosted:higher

Managed:couldbesimilartoclosedmodels

LLMOps/MLOps

Loweroperationalburden

Higheroperationalburden,dependson

deploymentmode(self-hostedormanaged)

AItalent

Relatively,lowtechnical

capability:lowerinfrastructureandmodeloperations

requirement

Relativelyhigherinfrastructureandengineeringcapability,dependsondeploymentmode

(higherforself-hostedorcustomisationvis-à-visormanageddeployment)

Ongoing

optimisation

Medium

Higher,dependsondeploymentmode(self-hostedormanaged)

Source:NasscomResearch

ImplicationsofAIModelChoicesfortheEnterprises13

Sixprimaryvariablesdeterminingeconomics

Therelativeimportanceofcostheadsdependsonworkload,deploymentmodelandoperatingenvironment:

Scaleofusage:Workloadvolume,frequencyand

predictabilitycan

significantlyaffecttheeconomicsofdifferentdeploymentmodels.

1

Performancerequirements:

Real-time,high-

availability/mission-criticalapplicationsmayrequire

additionalcapacityandredundancy,whichcanincreasecosts.

3

Modelandtaskcomplexity:

Largerandmorecapablemodelsgenerallyrequire

greaterinferenceresourcesandaremoreexpensive.

Longerinputs,complexreasoningandmulti-stepagentictaskscanalso

increaseprocessing

2

Qualityandefficiency:Thecostofretries,errors,humaninterventionandrepeated

processingcansignificantlyincreasetheeffectivecost,

whilecaching,model

selectionandroutingcan

lowerexpenses.Therelevantmeasureisthereforenotcost

4

Enterprisecapabilities:Thecostofengineering,

integration,securityand

modeloperationscanriseasenterprisesassumegreaterresponsibilityforthe

technologystack.

6

Controlanddeployment

requirements:Data

sensitivity,security,

residencyandcontrol

requirementscanincreaseinfrastructureandoperatingcosts.

5

Themodelwiththelowestlistedpriceisnotnecessarilythelowest-costenterprisesolution.Aclosedmodelwithahigherper-tokenpricecanbemoreeconomicalthananominally‘free’open-weightmodelifthelatterrequiresdedicatedGPUs,engineeringteamsandcontinuousoperations.

Deploymentmodealsoaffectseconomics.Athighandpredictablevolumes,self-hostingcanbecomeattractivebecauseinfrastructurecostsareamortisedandinferenceoptimised.Bycontrast,managedinferenceorprovisionedservicesmayremainmoreeconomicalwhereutilisationisunevenorthecostofoperatinginfrastructureissignificant.

Decisionsonmodelselectionanddeploymentmodeshouldbeguidedbytotalcostofownershipandworkloadprofile,ratherthanbymodelpricealone.

Totalcostofownership(TCO)shouldthereforebeassessedas:

Model/licence+inference+compute+data+integration+security&governance+operations+talent

Modeleconomicsbyapproach

a.Closedmodels:Usage-basedeconomics

Forclosedmodels,themosttransparentcostisgenerallyAPIinferencepricing,typicallyexpressedpermillioninputandoutputtokens.Pricingmechanicschangerapidlyandvarysignificantlybymodelandprovider.Enterprisesshouldthereforerelyonadatedproviderpricingsnapshotandaccountforinput/outputmix,usagepatterns,caching,discountsandnegotiatedcommercialterms,ratherthanafixedgenericpricerange.

ImplicationsofAIModelChoicesfortheEnterprises14

Therelevantenterprisemetricisnotheadlinecostpertoken,butcostpersuccessful,policycompliantcompletedtaskorworkflow.Effectivecostdependsontokenvolumes,outputlength,cachingorbatch-processingoptions,reasoningeffort,toolcalls,retriesandrework,humanescalationandsupportingservicessuchasembeddings,retrievalandtoolcalls.Forexample,inagenticsystems,alowtokenpricecanbeoutweighedbylongeragenttrajectories,repeatedtoolcallsorpoorfirst-passsuccess.

IllustrativeAPIcostframework

Costdriver

Illustrativeapproach

APIusage/inference

Usage-basedpricing,typicallychargedpermillioninputandoutputtokens,withoutputtokenspriceshigherthaninputtokens

Caching/batchprocessing

Canreduceeffectivecostwheresupported

Additionalservices

Embeddings,retrieval,toolcalls,storage,etc.

Enterpriselayer

Integration,security,governanceandmonitoring

Source:NasscomResearch

Themodelbillmayberelativelylowcomparedwithsurroundingenterprisecosts(suchasdata,integration,securityandgovernance),particularlyinlow-volumeusecases.Atscaledproductionvolumesoragenticworkloads,however,inferencecanbecomeamajoroperatingcost.Modeleconomicsshouldthereforebeassessedattheworkloadlevel:costpersuccessfulquery,document,transaction,agenttaskorbusinessprocess(includingretry,escalationandcontrolcosts),ratherthansimplycostpertoken.

b.Open-weightmodels:Deploymentmodedeterminescost

Open-weightdoesnotimplyasinglecoststructure.Self-hostingshiftstheeconomicstowardscompute,modelservingandongoingoperations,whilemanagedorserverlessopen-weightinferencecanremainusage-orcapacity-priced.Enterprisesshouldthereforeassesseconomicsbasedondeploymentmoderatherthanmodelopennessalone.

EnterprisescaneitherownGPUinfrastructureorrentcomputefromcloudproviders,GPU-as-a-Serviceplatformsorotherinfrastructurevendors.Theappropriatemodeldependsonworkloadvolume,utilisation,latency,data-residencyrequirementsandtherequiredlevelofcontrol.

However,GPUhoursareonlyonepartofthecost.EnterpriseAITCOalsoincludescostssuchas:

Hostinfrastructure,memoryandstorage

Networkinganddatatransfer

Modelservingandinferenceoptimisation

Securityandsoftware

EngineeringandLLMOps/MLOps

Redundancyandavailability

Infrastructurerequirementsvarybymodelsize,quantisation,inferencearchitecture,concurrencyandlatency.Asaresult,theeconomicsofAIareshiftingfromthenarrowquestionof‘whatdoesthemodelcost?’tothebroaderquestionof‘whatdoesitcosttoreliablyrunthisworkloadattherequiredscale,performanceandlevelofcontrol?’

ImplicationsofAIModelChoicesfortheEnterprises15

6.MATCHINGMODELSTOENTERPRISECONTEXTS

a.Practicaldecisionframework

Enterpriseneed

Likelymodelapproach

Rationale

Rapid

experimentation/pilots

Closed

Fastdeployment,minimalinfrastructure

Generalenterpriseproductivity

Closed/open-weight

Strongcapability

Highlysensitive/regulateddata

Open-weightself-

hosted/privatedeployment,approvedmanagedmodel

Modelopennessaloneisnotdeterminative,controloverdataresidency,retention,

isolation,auditability,security

High-volume,predicta