sizeless vs TrueFoundry AI Gateway: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of sizeless and TrueFoundry AI Gateway — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
sizeless
sizeless
Turns a smartphone video of an open trench into a centimetre-accurate 3D point cloud, CAD as-built plan and GIS-ready digital twin of buried utilities.
Key features
- Smartphone capture: Field crews record an open trench with a standard iPhone Pro — no specialist scanning hardware and no separate surveying appointment
- Centimetre-accurate point clouds: Reconstruction algorithms developed at ETH Zurich build a high-resolution 3D point cloud of the excavation from the video alone
- Standards-compliant CAD output: Generates as-built plans in DWG and DXF, with couplings and pipe runs identified and measurements simplified
- 3D digital twin and GIS export: Produces a model of the pipe route including building entries that drops into existing GIS systems
- Works without GPS: Captures basement sections and building entry points where GNSS-based surveying fails
- Immediate backfilling: Because capture takes minutes, trenches close right after filming instead of waiting on a survey crew
- Documentation in about 72 hours: Complete records arrive weeks earlier than conventional surveying, enabling prompt connection billing
- Third-party utility capture: Records crossing utilities and as-laid geometry as unbroken 3D evidence, replacing hand sketches
Best for
- A utility network operator documenting residential service connections without booking a surveyor for every site
- A contractor closing a trench the same day instead of leaving it open pending a survey appointment
- Capturing a building entry point in a basement where GPS-based surveying cannot get a fix
- A district heating project producing as-built DWG plans for regulatory sign-off
- Spotting a laying error in the 3D point cloud before backfilling, while the fix is still cheap
- Feeding as-built pipe geometry into a GIS system for long-term network maintenance planning
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