Top 10 API Management Platforms for linking SQL Server and AI Applications in 2025 | DreamFactory

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.

Why use an API platform to connect SQL Server to AI and LLM workflows?

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.

What problems typically arise without an API platform for SQL Server-to-AI?

• Brittle, one-off integrations and duplicated business logic
• Inconsistent authentication, authorization, and auditing
• Unbounded queries that inflate token usage and latency
• Complex joins, stored procedure access, and transformations
• Compliance gaps for PII and regulated data

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.

What to look for in an API platform for SQL Server-to-AI

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.

Which capabilities matter most, and how does DreamFactory align?

• Instant, schema-driven REST API generation for SQL Server
• Role-based access control, SSO, and API keys/JWT
• Field/row-level filtering, stored procedure endpoints, parameterized queries
• Request/response transformation, server-side scripting, and caching
• OpenAPI/Swagger docs for rapid consumption and testing

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.

How are teams connecting SQL Server to AI using API platforms?

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.

• Strategy 1: Governed RAG retrieval
  • Use DreamFactory’s filtered REST endpoints to serve model-ready records.
• Strategy 2: Prompt enrichment and masking
  • Apply server-side scripts to redact PII and shape payloads for LLMs.
• Strategy 3: Stored procedure orchestration
  • Expose SQL Server stored procedures as endpoints with RBAC.
• Strategy 4: Cost and latency control
  • Enforce query limits, cache common responses, and compress payloads.
• Strategy 5: Unified data access plane
  • Document with OpenAPI, standardize auth, and centralize auditing.
• Strategy 6: Hybrid/on‑prem deployments
  • Run DreamFactory where data resides to meet compliance and residency.

In practice, DreamFactory reduces bespoke middleware, shortens rollout cycles, and adds the security and observability that general-purpose gateways require you to assemble yourself.

Competitor comparison: Which platforms best fit SQL Server-to-AI?

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.

Best API platforms to connect SQL Server to AI and LLM workflows in 2025

1. DreamFactory

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:

• No‑code REST API generation for SQL Server with OpenAPI
• Role-based access, SSO (Azure AD/Okta), API keys/JWT
• Stored procedures as endpoints, parameterized queries, filtering
• Request/response transformation, server-side scripting, caching
• Rate limiting, auditing, and logging for compliance

SQL Server-to-AI offerings:

• Governed endpoints for RAG/function calling
• PII redaction and payload shaping in-flight
• On‑prem/air‑gapped deployment for data residency

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.

2. Azure API Management

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:

• Enterprise gateway, policy engine, and portal
• Deep Azure integration and VNET support
• Advanced throttling, auth, and observability

SQL Server-to-AI offerings:

• Secure exposure of custom data services to LLMs
• Azure OpenAI and Key Vault integration workflows
• Managed identities and private networking

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.

3. Hasura

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:

• Auto GraphQL over SQL Server
• Role-based permissions and row-level policies
• Event triggers and remote schemas

SQL Server-to-AI offerings:

• Fine-grained GraphQL querying for retrieval
• Actions for custom orchestration
• Subscriptions for real-time updates

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.

4. Data API Builder for Azure Databases (DAB)

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:

• Config-driven REST/GraphQL generation
• Role-based authorization with claims mapping
• Azure-friendly deployment and tooling

SQL Server-to-AI offerings:

• Quick entity exposure for retrieval tasks
• Lightweight policy guardrails
• Works alongside Azure API Management

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.

5. Kong Konnect

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:

• High-throughput gateway with plugins
• OIDC/OAuth, rate limiting, analytics
• Hybrid and multi-cloud support

SQL Server-to-AI offerings:

• Policy enforcement for custom data services
• Plugin-driven auth and traffic shaping
• Scalable, low-latency routing

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.

6. Apigee (Google Cloud)

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:

• Full lifecycle API management and analytics
• Powerful policy engine and monetization
• Developer portal and CI/CD integrations

SQL Server-to-AI offerings:

• Policy-governed access to custom data APIs
• Traffic shaping and advanced analytics
• GCP ecosystem alignment

Pricing: Subscription/consumption; enterprise-focused.
Pros: Deep enterprise governance; mature ecosystem.
Cons: Higher cost/complexity; requires a custom SQL data layer.

7. Tyk

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:

• Open-source gateway with GraphQL federation
• Quotas, rate limits, and auth policies
• Self-hosted and cloud options

SQL Server-to-AI offerings:

• Governed access to custom data services
• GraphQL at the edge if needed
• Plugin and middleware extensibility

Pricing: Open-source and paid tiers.
Pros: Developer-friendly; flexible; cost-effective options.
Cons: Requires building the SQL/AI transformation layer.

8. StepZen (IBM)

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:

• Declarative GraphQL over databases and services
• Schema stitching and composition
• Cloud-first developer experience

SQL Server-to-AI offerings:

• Targeted GraphQL retrieval for prompts/RAG
• Composition across multiple data sources
• Declarative transformations

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.

9. WSO2 API Manager

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:

• Full-featured open-source API management
• Analytics, throttling, and identity integrations
• On-prem and cloud deployment

SQL Server-to-AI offerings:

• Governed access to custom data services
• Extensible policies and mediation
• Developer portal and lifecycle tools

Pricing: Open-source with enterprise subscriptions.
Pros: Feature-rich; cost control; on-prem strength.
Cons: Build and maintain the SQL/AI service layer.

10. AWS API Gateway

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:

• Managed REST/WebSocket APIs
• Tight IAM/CloudWatch integration
• Lambda/containers for backend logic

SQL Server-to-AI offerings:

• Secure, scalable endpoints for custom services
• Native fit with AWS AI services
• Global distribution via CloudFront

Pricing: Pay-as-you-go
Pros: Seamless AWS integration; scalable; mature.
Cons: Requires custom SQL service and AI payload shaping.

Evaluation rubric for SQL Server-to-AI API platforms

• Time-to-first-API (20%): Minutes vs. weeks to expose governed SQL endpoints
• Governance and security (20%): RBAC, SSO, scopes, auditing, rate limits
• AI readiness (20%): Filtering, masking, response shaping, caching
• Performance and scale (15%): Latency, throughput, resource efficiency
• Deployment fit (10%): On-prem/air‑gapped, cloud, hybrid options
• Developer experience (10%): OpenAPI docs, SDK/workflow compatibility
• TCO and maintainability (5%): Ongoing ops burden and custom code required

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.

Choosing the best API Management Platform for connecting SQL Server to AI

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.

FAQs 

Why do teams need an API platform to connect SQL Server to AI apps?

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.

What is an “API management platform”?

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.

What are the best API management platforms for linking SQL Server with AI?

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.

How do enterprises ensure compliance when exposing data to LLMs?

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.