Service level agreements
Listed terms cover requests sent through this service adapter. They do not cover direct connections to the target service or replace the cloud provider’s own SLA. Each origin-to-target pair has its own terms or an explicit pending status. See the SLA tables, measurement rules, and scaling conditions.Artifact Registry to ECR (Elastic Container Registry)
Deployment availability. These targets describe this adapter design. Confirm that your deployment supports the listed operations; a numerical target does not establish runtime availability.
What Tensor9 covers. These service levels cover the adapter between the origin API and target API. They do not replace the target provider’s SLA.
How latency is counted. Adapter work, including metadata and coordination, counts. Only separately measured permitted target waits are excluded.
Which terms apply. Your signed agreement names the covered operations, workload limits, remedies, and final service levels for your deployment.
What this adapter does
Foraws::1.0.0::ecr, container image requests use the registry distribution protocol while repository administration uses the origin cloud’s management API. Image names, authentication scopes and target repository behavior must remain coordinated. The manifest budget does not time a multi-gigabyte image transfer, and a configured vulnerability scanner does not imply a fixed scan-completion deadline. These targets describe this adapter design; confirm that your deployment supports the listed operations. A numerical target does not establish runtime availability.
How the latency targets were chosen
Bounded container manifest requests: 5 ms monthly p99.GET /v2/{name}/manifests/{reference}, HEAD /v2/{name}/manifests/{reference}, PUT /v2/{name}/manifests/{reference}. Translate repository identity, authentication scope and manifest metadata without buffering arbitrary layer bodies.
Repository administration: 20 ms monthly p99. artifactregistry.projects.locations.repositories.create, artifactregistry.projects.locations.repositories.delete. Bounded configuration validation, durable adapter metadata and response encoding; native provisioning completion and propagation are separate.
Your signed agreement sets the terms for your deployment. A target does not add an operation or option that the compatibility tables mark unavailable.
What counts toward latency
For a request-response row, measure from the agreed ingress boundary to dispatch of the complete response. A row that explicitly names a first response chunk ends at that chunk; its number does not cover the rest of the stream. A long-poll row names the intentional wait and when adapter delay starts. Include parsing, authorization, admission, translation, serialization, adapter-owned storage and coordination, retries and response handling. Subtract only separately measured target-workload waits and external network segments allowed by the measurement rules. The adapter’s own response handling and dispatch remain covered. A database used for adapter metadata is still adapter work, even if a cloud provider hosts it. For concurrent calls, exclude the union of permitted wait intervals, not the sum of overlapping spans. Calculate each request’s adapter duration first, then the monthly p99. Do not subtract one service’s p99 from another’s. Known adapter timeouts are over-budget samples; failed or incomplete requests cannot disappear to improve the percentile. Missing measurements do not become zero latency. An SDK call span alone does not prove how much of its duration can be excluded.Availability and failures
The 99.9% request target measures correct adapter handling, not the percentage of application calls that return success. Correctly forwarding a target quota or permission error is different from producing that error because the adapter sent the wrong request. Adapter-caused failures count even when the target is healthy. With 1,000,000 eligible calls in a month, a 99.9% target permits at most 1,000 adapter-attributable failures. Endpoint probes have their own denominator. Correctness defects remain actionable even when the monthly availability percentage is met.Scaling and target-service capacity
Measure active uploads/downloads and layer bytes alongside manifest requests and authentication work. Shared layers and simultaneous deployment pulls can create large bandwidth bursts with few management calls. Tensor9 scales adapter capacity within agreed byte, stream and manifest limits; the customer monitors target registry bandwidth, retention and quotas. Repository creation and image transfer have different bottlenecks. Tell Tensor9 the expected steady rate, bursts, concurrency, payload sizes and operation mix. Tensor9 sizes and scales the adapter for the agreed load; you choose and monitor the target service’s capacity with Tensor9’s help. A latency budget is not a requests-per-second rating. Larger requests and higher rates need explicit terms, not silent inheritance of a small-request SLA.Data and behavior guarantees
Preserve content digests, manifest references and supported access scope. A successful push must not point to missing layers or a different repository. Native naming, immutable-tag, token and retention limits remain those in the target profile. Scanning completion and findings are separate from request handling, and unsupported registry tasks or non-container formats do not gain coverage here.Example and diagnosis
Push a 20 KiB manifest fororders-api:v2, then pull it by digest. The manifest calls fit this row; transferring its 600 MiB of layers does not fit a 5 ms request class. Diagnose slow pulls using layer bandwidth and target throttling separately from manifest/authentication timing.
Use tensor9 explain and the documented explain headers to understand the selected adapter and its behavior. Correlate available request diagnostics with the target provider’s latency, throttling and capacity metrics. An explain report helps diagnose a request; it is not by itself a qualified SLA timing measurement. Share the operation, request shape, timestamps and request identifiers with support, with credentials and customer payloads removed.
Artifact Registry to Container Registry (ACR)
Deployment availability. These targets describe this adapter design. Confirm that your deployment supports the listed operations; a numerical target does not establish runtime availability.
What Tensor9 covers. These service levels cover the adapter between the origin API and target API. They do not replace the target provider’s SLA.
How latency is counted. Adapter work, including metadata and coordination, counts. Only separately measured permitted target waits are excluded.
Which terms apply. Your signed agreement names the covered operations, workload limits, remedies, and final service levels for your deployment.
What this adapter does
Forazure::1.0.0::container-registry, container image requests use the registry distribution protocol while repository administration uses the origin cloud’s management API. Image names, authentication scopes and target repository behavior must remain coordinated. The manifest budget does not time a multi-gigabyte image transfer, and a configured vulnerability scanner does not imply a fixed scan-completion deadline. These targets describe this adapter design; confirm that your deployment supports the listed operations. A numerical target does not establish runtime availability.
How the latency targets were chosen
Bounded container manifest requests: 5 ms monthly p99.GET /v2/{name}/manifests/{reference}, HEAD /v2/{name}/manifests/{reference}, PUT /v2/{name}/manifests/{reference}. Translate repository identity, authentication scope and manifest metadata without buffering arbitrary layer bodies.
Repository administration: 20 ms monthly p99. artifactregistry.projects.locations.repositories.create, artifactregistry.projects.locations.repositories.delete. Bounded configuration validation, durable adapter metadata and response encoding; native provisioning completion and propagation are separate.
Your signed agreement sets the terms for your deployment. A target does not add an operation or option that the compatibility tables mark unavailable.
What counts toward latency
For a request-response row, measure from the agreed ingress boundary to dispatch of the complete response. A row that explicitly names a first response chunk ends at that chunk; its number does not cover the rest of the stream. A long-poll row names the intentional wait and when adapter delay starts. Include parsing, authorization, admission, translation, serialization, adapter-owned storage and coordination, retries and response handling. Subtract only separately measured target-workload waits and external network segments allowed by the measurement rules. The adapter’s own response handling and dispatch remain covered. A database used for adapter metadata is still adapter work, even if a cloud provider hosts it. For concurrent calls, exclude the union of permitted wait intervals, not the sum of overlapping spans. Calculate each request’s adapter duration first, then the monthly p99. Do not subtract one service’s p99 from another’s. Known adapter timeouts are over-budget samples; failed or incomplete requests cannot disappear to improve the percentile. Missing measurements do not become zero latency. An SDK call span alone does not prove how much of its duration can be excluded.Availability and failures
The 99.9% request target measures correct adapter handling, not the percentage of application calls that return success. Correctly forwarding a target quota or permission error is different from producing that error because the adapter sent the wrong request. Adapter-caused failures count even when the target is healthy. With 1,000,000 eligible calls in a month, a 99.9% target permits at most 1,000 adapter-attributable failures. Endpoint probes have their own denominator. Correctness defects remain actionable even when the monthly availability percentage is met.Scaling and target-service capacity
Measure active uploads/downloads and layer bytes alongside manifest requests and authentication work. Shared layers and simultaneous deployment pulls can create large bandwidth bursts with few management calls. Tensor9 scales adapter capacity within agreed byte, stream and manifest limits; the customer monitors target registry bandwidth, retention and quotas. Repository creation and image transfer have different bottlenecks. Tell Tensor9 the expected steady rate, bursts, concurrency, payload sizes and operation mix. Tensor9 sizes and scales the adapter for the agreed load; you choose and monitor the target service’s capacity with Tensor9’s help. A latency budget is not a requests-per-second rating. Larger requests and higher rates need explicit terms, not silent inheritance of a small-request SLA.Data and behavior guarantees
Preserve content digests, manifest references and supported access scope. A successful push must not point to missing layers or a different repository. Native naming, immutable-tag, token and retention limits remain those in the target profile. Scanning completion and findings are separate from request handling, and unsupported registry tasks or non-container formats do not gain coverage here.Example and diagnosis
Push a 20 KiB manifest fororders-api:v2, then pull it by digest. The manifest calls fit this row; transferring its 600 MiB of layers does not fit a 5 ms request class. Diagnose slow pulls using layer bandwidth and target throttling separately from manifest/authentication timing.
Use tensor9 explain and the documented explain headers to understand the selected adapter and its behavior. Correlate available request diagnostics with the target provider’s latency, throttling and capacity metrics. An explain report helps diagnose a request; it is not by itself a qualified SLA timing measurement. Share the operation, request shape, timestamps and request identifiers with support, with credentials and customer payloads removed.