Explore model providers, data infrastructure, evaluation, orchestration, and governance. See the providers named in covered disclosures and the layers those sources leave unknown.
Initial coverage sample · reviewed (UTC)
Real public-source records, manually reviewed. This is a dated snapshot, not a continuous live feed or a representative market survey.
Explore each layer
Counts describe covered companies, not feature usage or a complete architecture.
Model providers
Providers named for AI processing; model version and actual usage remain unknown.
OpenAI3 observed companies
Anthropic3 observed companies
Cohere1 observed companies
Fireworks AI1 observed companies
Public-source observations. Observation dates are review dates, not adoption dates.
The company’s published subprocessor disclosure names this provider for the stated purpose.
Observed AI adoption, month by month
Reviewed 2026-09-10 · 3 distinct companies in the selected model-provider sample.
Each company counts once per provider. Shares can total more than 100% because companies can disclose multiple providers.
Monthly series
Month
Covered companies
Comparability
September 2026
3
Initial reviewed snapshot
Earlier months
Unavailable
No comparable company panel
One point does not establish a trend. Company-size values are not provided by these model-provider disclosures.
Delivery route: not established
The records name providers, but do not establish direct API use versus Azure OpenAI, Bedrock, or Vertex. All 8 records remain unclassified by delivery route; no route split is inferred.
Public-source observations. Observation dates are review dates, not adoption dates.
The company’s published subprocessor disclosure names this provider for the stated purpose.
Layers in a generative AI tech stack
Model providers supply AI processing. Retrieval and data layers help make relevant information available. Orchestration connects steps, evaluation tests behavior, and governance defines controls and accountability. A disclosure can name a service without revealing the whole architecture.
Observed enterprise LLM providers
Provider counts describe companies naming AI providers in our reviewed sources. The model-provider layer does not identify every model version, routing decision, or workload. Multiple providers can be listed for the same company.
What the map cannot reconstruct
A vendor name alone rarely identifies the internal orchestration framework or governance process. Empty layers are coverage gaps, not a claim that those functions are absent. The monthly chart currently contains one coverage snapshot and does not establish a growth trend.
A public job board supports an ATS observation. A subprocessor disclosure identifies a provider that may process data for particular services. A vendor customer story describes a published relationship. These sources have different scopes and are labeled separately.
Counts cover the records on these pages, not the whole market. No observation in our covered sources does not mean a company does not use a vendor. Source recency and unresolved conflicts affect confidence; observations do not establish spend, renewal timing, or customer churn.