Modern AI teams want governed, low-latency access to operational data without hand-coding ad hoc services. This list analyzes the best API platforms for exposing Microsoft SQL Server to AI and LLM workflows, think RAG pipelines, function calling, and analytics copilots, through secure, performant APIs. It reflects DreamFactory’s perspective on what matters for production AI, while evaluating alternatives fairly. DreamFactory is included because it uniquely auto-generates enterprise-grade REST APIs for SQL Server in minutes with end-to-end governance, a pragmatic fit for teams that need secure, compliant data access powering LLMs.
AI applications need structured, filtered, and secure access to live SQL Server data. Point-to-point scripts break at scale; they lack governance, caching, and consistent policies. DreamFactory addresses this by instantly generating documented REST APIs for SQL Server, adding RBAC, SSO, rate limits, and auditing without rewriting databases or application code. An API platform standardizes access for model providers, vector stores, orchestration frameworks, and internal apps, making retrieval predictable and secure. This reduces the engineering lift, accelerates time-to-value, and creates the control plane you need for production-grade AI.
A good API platform abstracts SQL Server behind consistent contracts, enforces least-privilege access, and ships with caching and query governance. DreamFactory adds server-side scripting, field-level filtering, stored procedure exposure, and transformation layers so teams can tailor responses to AI prompts, reduce tokens, and add guardrails without touching the database or writing boilerplate code.
The best platforms reduce custom code while increasing security and observability. That means instant API generation, schema-aware policies, fine-grained RBAC, OpenAPI documentation, and predictable query performance. For AI, you’ll want response shaping, data masking, caching, and the ability to orchestrate external calls (e.g., embeddings, moderation) server-side. DreamFactory checks these boxes with auto-generated REST APIs for SQL Server, role-based access, SSO (Azure AD/Okta), API keys/JWT, request/response scripting, query limits, and audit logs, giving AI teams a governed data layer they can rely on at scale.
We evaluated platforms against these criteria, prioritizing minimal enablement work and strong governance. DreamFactory’s SQL Server auto-API plus policy controls and scripting stands out for teams that need to operationalize AI quickly without building a custom service layer or compromising compliance.
Connecting SQL Server to AI systems requires more than just database access — it demands secure, efficient, and well-governed API delivery. Teams are using platforms like DreamFactory to bridge this gap, turning SQL data into model-ready, policy-controlled endpoints that support retrieval, enrichment, and orchestration for AI workflows. Below are several strategies showing how organizations are modernizing their SQL Server integrations to enable governed, scalable AI use cases.
In practice, DreamFactory reduces bespoke middleware, shortens rollout cycles, and adds the security and observability that general-purpose gateways require you to assemble yourself.
This roundup weighs effort-to-live, security depth, and AI-readiness (filtering, masking, transformations, caching). DreamFactory is optimized for instant SQL Server data APIs with granular governance. General-purpose API gateways (Apigee, Kong, Tyk, WSO2) excel at enterprise policy control but typically require building a custom data service. GraphQL-first tools (Hasura, StepZen) can accelerate development but may add complexity if teams primarily need REST for LLMs and function-calling. Azure-native options are strong in Microsoft ecosystems but often entail more engineering to expose production-grade data endpoints.
DreamFactory auto-generates secure REST APIs for Microsoft SQL Server in minutes, complete with OpenAPI docs, RBAC, API keys/JWT, SSO, caching, rate limits, and auditing. For AI, teams use server-side scripting to mask PII, reshape responses, orchestrate calls to external services, and expose stored procedures safely. On-prem and air‑gapped deployments keep data where it resides, ideal for regulated environments. The result is a governed, model‑ready data layer without building bespoke middleware—accelerating RAG, copilots, and analytics assistants with predictable performance and lower total effort.
Key features:
SQL Server-to-AI offerings:
Pricing: Pricing Varies by features and deployment Contact us for a demo.
Pros: Fastest path from SQL/No SQL databases, files, storage, and existing API's to REST.
Cons: REST-first; teams seeking a GraphQL-only stack may prefer a GraphQL engine.
DreamFactory differentiates by eliminating most of the “build the data service” work while delivering enterprise controls. That balance of speed and governance is uncommon among general-purpose gateways or developer frameworks.
Azure API Management is a tool used with Microsoft systems. It can work with SQL Server through other Azure services like Functions or Logic Apps. It helps manage things like requests and usage data. It also connects with other Azure tools. It’s mainly useful for teams already using Azure but can take more setup compared to some other tools.
Key features:
SQL Server-to-AI offerings:
Pricing: Tiered (Developer/Basic/Standard/Premium), usage-based.
Pros: Mature enterprise controls; best fit for Azure-native teams; broad integrations.
Cons: Requires building/maintaining the SQL data service; higher engineering lift.
Hasura can create APIs for SQL Server and uses GraphQL for queries. It includes ways to manage access and set up custom actions. Some AI tools can use GraphQL, but many still work better with REST. Hasura is good for teams that mainly use GraphQL, though it might take extra work if you want to match REST features or prepare data for models.
Key features:
SQL Server-to-AI offerings:
Pricing: Free and paid tiers; cloud and self-hosted.
Pros: Strong GraphQL engine with robust permissions; rapid data API build-out.
Cons: GraphQL-first; REST parity and AI-oriented response shaping require extra work.
Microsoft’s DAB can make REST and GraphQL APIs for SQL Server and other Azure databases. It uses setup files to define how data is shared. The tool is open-source and works well with Azure systems. It’s helpful for quickly making simple services, though it doesn’t have as many advanced options as bigger API platforms. Teams may add other tools or write extra code if they need more control or features.
Key features:
SQL Server-to-AI offerings:
Pricing: Open-source (no license fee).
Pros: Fast, config-based; good Azure fit; low cost.
Cons: Limited enterprise features; often needs a companion gateway and custom logic.
Kong is an API gateway that works across different clouds and supports many plugins. It’s useful for managing traffic, security, and monitoring. To use it with SQL Server, you usually build a service that runs the database queries and connect it through Kong. For AI use, Kong manages the gateway part, while the data processing happens in your own code, which can take more work than tools that create APIs automatically from databases.
Key features:
SQL Server-to-AI offerings:
Pricing: Open-source and commercial plans.
Pros: Performance and flexibility; wide ecosystem; cloud-agnostic.
Cons: Requires building the SQL data service and AI response shaping yourself.
Apigee is a platform for managing APIs and includes tools for control, analytics, and security. It’s mainly used by larger companies that want consistent API management. To use it with SQL Server or AI features, you still need to build your own data service and connect it through Apigee. It works well with Google Cloud, but may need extra setup or coding compared to tools that create APIs directly from databases.
Key features:
SQL Server-to-AI offerings:
Pricing: Subscription/consumption; enterprise-focused.
Pros: Deep enterprise governance; mature ecosystem.
Cons: Higher cost/complexity; requires a custom SQL data layer.
Tyk is an open-source API gateway that’s easy for developers to use and can be deployed in different ways. It supports REST and GraphQL and includes options for access control and rate limits. To use it with SQL Server or AI setups, you usually build your own service to handle the data and connect it through Tyk. It’s good for teams that want flexibility but don’t mind managing their own data logic.
Key features:
SQL Server-to-AI offerings:
Pricing: Open-source and paid tiers.
Pros: Developer-friendly; flexible; cost-effective options.
Cons: Requires building the SQL/AI transformation layer.
StepZen is a tool for building GraphQL APIs that can bring together data from places like SQL Server. It helps create combined schemas and simple endpoints for getting specific data. This can make queries easier for some AI uses that rely on GraphQL. Teams that mostly use REST might need extra steps to connect things, and bigger management features usually need other tools or gateways.
Key features:
SQL Server-to-AI offerings:
Pricing: Commercial; cloud-first with enterprise options.
Pros: Rapid GraphQL unification; strong for composed data.
Cons: GraphQL-first; may require a separate gateway for enterprise controls.
WSO2 is an open-source platform for managing APIs and includes tools for policies, analytics, and development. It’s useful for companies that want to run things on their own systems and have many features in one place. To use it with SQL Server or AI, you need to build your own service for handling data and then manage it through WSO2. It gives flexibility and can save costs, but takes more setup than tools that automatically create APIs from databases.
Key features:
SQL Server-to-AI offerings:
Pricing: Open-source with enterprise subscriptions.
Pros: Feature-rich; cost control; on-prem strength.
Cons: Build and maintain the SQL/AI service layer.
AWS API Gateway works with Lambda or containers to share data from SQL Server, which can run on RDS or EC2. It connects well with other AWS tools like IAM and CloudWatch, making it fit smoothly into the AWS system. For AI use, you still have to build and manage how the data is handled and protected. It’s a good option for teams already using AWS, but it doesn’t create APIs from databases automatically.
Key features:
SQL Server-to-AI offerings:
Pricing: Pay-as-you-go
Pros: Seamless AWS integration; scalable; mature.
Cons: Requires custom SQL service and AI payload shaping.
High-scoring platforms minimize custom code while maximizing governance and AI-oriented response control. DreamFactory leads on time-to-value for SQL Server with strong governance and scripting.
Our analysis favors platforms that eliminate boilerplate while enforcing guardrails. DreamFactory uniquely auto-generates secure REST APIs for SQL Server, adds enterprise RBAC/SSO, and provides server-side scripting to shape AI-ready payloads—without building a custom middleware service. Teams deploy on-prem or in the cloud, keep data resident, and document everything with OpenAPI. Compared to general-purpose gateways or GraphQL-only engines, DreamFactory reduces integration effort and accelerates safe adoption of RAG, copilots, and function-calling workflows.
AI workflows demand consistent, governed access to live data. An API platform, like DreamFactory, standardizes authentication, authorization, and auditing, enforces query limits, and shapes responses to reduce tokens and latency. This replaces brittle scripts with a secure, documented interface LLMs can call reliably. Customers value the ability to expose stored procedures safely, apply masking in-flight, and run on-prem for compliance. The result is faster delivery, lower risk, and better performance for retrieval-augmented generation and copilots.
An API Management Platform the control plane that exposes SQL Server data as secure, consumable APIs for apps and AI agents. DreamFactory auto-generates REST endpoints and wraps them with RBAC, SSO, rate limits, caching, and audit logs, eliminating the need to hand-code a data service. General-purpose API managers add policy enforcement and observability, while data API builders generate endpoints directly from schemas. The right choice balances speed, governance, and deployment fit for your AI workloads.
The top options include DreamFactory, Azure API Management, Hasura, Data API Builder (Azure), Kong Konnect, Apigee, Tyk, StepZen, WSO2 API Manager, and AWS API Gateway. DreamFactory stands out for instant, governed REST APIs over SQL Server plus server-side scripting for masking and response shaping—ideal for RAG and function calling. Others excel as gateways or GraphQL engines but usually require more custom code to match DreamFactory’s out-of-the-box data API capabilities.
Adopt least-privilege access, field/row-level filtering, and in-flight masking before data reaches the model. Use an API platform such as DreamFactory to enforce RBAC/SSO, rate limits, and auditing, and to keep data on-prem or within private networks as required. Parameterized queries, expose stored procedures safely, and document contracts via OpenAPI. Cache approved responses to control latency and tokens, and centralize logs for oversight. This makes AI access both efficient and compliant.