ABrush vs TrueFoundry AI Gateway: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ABrush and TrueFoundry AI Gateway — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ABrush
ABrush
AI image generation and editing studio that runs as a panel inside Adobe Photoshop, with 23+ models, ControlNet, LoRA styles and layer-native output.
Key features
- Photoshop-native panel: Generation, editing and upscaling happen on the open document and land on real layers, with no export-import round trip
- 23+ models in one panel: Switch between Stable Diffusion, Flux, Qwen Image and others per stage of a piece rather than committing to one provider
- Targeted editing: Inpaint or regenerate only the region that needs changing, keeping the rest of the composition untouched
- Pro conditioning controls: ControlNet support plus IP-Adapter and reference images for pose, composition and style control
- Custom LoRA styles: Load your own LoRA or style models to keep generations consistent with an established look
- Generation history: Every generation is saved and recoverable, so artists can return to an earlier variation without regenerating
- Shareable presets: Save prompts and settings as presets and share them across a team to reproduce a house style
- Commercial-safe data policy: Generated images belong to the user and customer images are not used for model training
Best for
- A concept artist generating multiple variations of a character directly in the working file and painting over the strongest one
- A retoucher fixing a single element of a composite with inpainting rather than regenerating the whole image
- A studio distributing a shared preset pack so several artists produce work in a consistent house style
- A freelance illustrator using a custom LoRA to keep generated assets on-style with a client's brand
- A designer upscaling and cleaning up a low-resolution asset without leaving Photoshop
- An agency handling commercial client work that needs assurance the images aren't used for model training
TrueFoundry AI Gateway
TrueFoundry
A gateway for deploying, routing, governing and monitoring GenAI workloads with unified access, cost controls and observability.
Key features
- Unified Access Control: Centralized authentication and role-based policy enforcement for model access and API usage across teams and environments, enabling consistent governance.
- Cost-aware DevOps and Budgeting: Per-user and per-team budgeting, usage tracking and cost allocation tools to enforce spend limits and surface cost anomalies for GenAI workloads.
- Provider-agnostic Model Routing: Route requests to multiple model providers or on-prem models via a single gateway layer, with configurable routing rules and fallback strategies.
- Observability and Telemetry: Request-level logging, metrics, traces and dashboards that capture latency, token usage, error rates and model performance for troubleshooting and optimization.
- Developer APIs and UI: RESTful APIs and an interface to integrate coding assistants, RAG pipelines and applications easily while exposing governance and telemetry controls.
- Auditing and Compliance: Persistent audit logs of requests, model choices and policy decisions to support compliance, review and post-hoc analysis.
- Request Orchestration and Enrichment: Support for common RAG workflows where inputs are embedded, retrievers queried, and final answers composed through the gateway with optional enrichment of metadata.
- Unified access control and routing for model and assistant requests
- Developer-friendly REST APIs and web UI for management and governance
- Observability: request logging, metrics, tracing and feedback capture
- Cost-aware DevOps: budgeting, usage tracking and cost controls per user/team
- Integrations with RAG frameworks and retrieval workflows (embeddings, vector DBs)
- Plugs into agentic deployments and MCP/FastAPI servers for production agents
- Infrastructure automation support via Terraform and Kubernetes (EKS) modules
- Documentation and example integrations (Cline, Cognita, Prisma AIRS guides)
Best for
- Routing requests from coding assistants (e.g., in-editor tools) through a centralized gateway to apply access controls, budgeting and observability for developer-facing AI features.
- Running RAG pipelines where user queries are embedded, vector DB retrievers are invoked and LLMs are called via the gateway to capture logs, metrics and feedback.
- Enforcing enterprise governance and compliance by centralizing policy enforcement, audit trails and model selection across multiple teams and environments.
- Cost control and chargeback for GenAI experiments by applying per-team budgets, usage limits and visibility into token/compute consumption.
- Provider-agnostic deployment where applications can switch between cloud-hosted models and on-premise models without code changes by updating gateway routing.
- Integrating security and policy scanning (e.g., Prisma AIRS) into AI workflows to enforce runtime checks and threat detection at the gateway layer.
- Observability-driven optimization: analyze gateway telemetry to reduce latency, detect failing model providers and implement caching or fallback strategies.
- Routing and governing LLM requests from coding assistants (e.g., Cline) with per-user budgeting and observability
- Production RAG pipelines where embeddings/retrievers fetch documents and LLM calls are routed through a monitored gateway
- Deploying and scaling agentic AI services behind a gateway with centralized access control and logging
- Integrating security and policy enforcement into AI workflows via third-party integrations (e.g., Prisma AIRS)
- Embedding TrueFoundry Gateway into microservices stacks using Python SDKs, FastAPI endpoints, or MCP servers