--- title: "API Versioning & Deprecation Policy | Aguasto" description: "Aguasto API compatibility, major version changes, migration notices and at least 90 days notice before retiring an operation. No current deprecations." canonical: "https://www.aguasto.com/developers/versioning" last-updated: "2026-10-27" --- # API Versioning & Deprecation Policy | Aguasto Canonical HTML: [API versioning policy](https://www.aguasto.com/developers/versioning) > Aguasto API compatibility, major version changes, migration notices and at least 90 days notice before retiring an operation. No current deprecations. Aguasto / API contract ## Versioning and deprecation policy. Compatibility rules for the public research and educational calculation APIs. Effective October 27, 2026. No operation is currently deprecated or scheduled for shutdown. ### Current versions REST catalog, batch, sandbox and calculation operations use /api/v1. Search at /ask follows NLWeb 0.55. MCP protocol versions are negotiated independently at initialization. ### Compatible changes Optional response fields may be added within v1; clients should ignore unknown fields. Breaking request or response changes use a new major REST URL version. The v1 calculator formula contract preserves deterministic results for the same canonical inputs. ### Deprecation and Sunset notices Before retiring an operation, Aguasto will publish its replacement, migration instructions and removal date at least 90 days before removal. A deprecated operation will return a Deprecation header and a Sunset header with its announced removal date. Current operations have no retirement date and emit neither header. Retirement notices will appear here and in the [Markdown policy](https://www.aguasto.com/versioning.md) . API responses link that policy with Link: rel="deprecation". The [OpenAPI description](https://www.aguasto.com/openapi.json) identifies current operations and links this policy. ### Retries and support Follow typed errors, honor Retry-After on HTTP 429 and use the documented per-instance limits. Calculator retry keys bind to canonical inputs; keys and inputs are not stored. See the [retry contract](https://www.aguasto.com/developers#idempotency) and [synthetic integration sandbox](https://www.aguasto.com/sandbox) . Questions can be sent to contact@aguasto.com using your own email application. Website inquiry delivery is disabled. This policy introduces no GPU provisioning, inference, accounts or billing service. ## Company navigation - [API versioning policy](https://www.aguasto.com/developers/versioning) - [Developer sandbox](https://www.aguasto.com/sandbox) - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Terms of use](https://www.aguasto.com/terms) - [Research topics](https://www.aguasto.com/topics) - [GPU systems & local clusters](https://www.aguasto.com/topics/gpu-systems) - [Model serving & inference](https://www.aguasto.com/topics/model-serving) - [Modular infrastructure & resources](https://www.aguasto.com/topics/modular-infrastructure) - [Compute economics & evidence](https://www.aguasto.com/topics/compute-economics) - 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[Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center) - [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) - [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) - [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power) - [What Is KV Cache? Capacity, Reuse and Isolation](https://www.aguasto.com/learn/kv-cache-capacity-and-policy) - [Continuous Batching: Throughput Without Hiding Latency](https://www.aguasto.com/learn/continuous-batching-and-latency) - [Applied Digital: Reading the Polaris Forge 1 Delivery Milestones](https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones) - [Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans](https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion) - [Data Center Cooling: Choosing Air, Liquid, Dry and Hybrid Architectures](https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid) - [Can Data Centers Reduce Water Consumption? Define the Water Boundary First](https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal) - [Measuring PUE, WUE and AI Compute Efficiency at the Right Boundary](https://www.aguasto.com/learn/measuring-pue-wue-and-ai-compute-efficiency) - [The Environmental Trade-offs of High-Density AI Infrastructure](https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse) - [Selecting GPU Systems for Enterprise Inference](https://www.aguasto.com/learn/selecting-enterprise-inference-gpu-systems) - [Networking Rack-Scale AI Systems: Define the Communication Boundary](https://www.aguasto.com/learn/rack-scale-ai-networking-boundaries) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.