BacklogHQ vs LLM Gateway: Which is Better in 2026?
A comprehensive comparison of BacklogHQ and LLM Gateway covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose BacklogHQ if:
- →You need agentdb — embedded ai-first database with runtime-discoverable persisted schemas or hnsw vector search, blob storage and real-time subscriptions exposed as mcp tools
Choose LLM Gateway if:
- →You want more affordable paid plans (from $0.01/mo)
- →You need a broader feature set (6 features vs 5)
- →You need one openai-compatible endpoint for 200+ models across 40+ providers or real-time cost tracking and spend controls across every provider
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BacklogHQ vs LLM Gateway: At a Glance
Pricing Comparison: BacklogHQ vs LLM Gateway
Understanding the pricing differences between BacklogHQ and LLM Gateway is crucial for making the right choice. Here's how their plans compare side by side.
BacklogHQ Pricing
LLM Gateway Pricing
💡 Pricing takeaway: Both BacklogHQ and LLM Gateway offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from BacklogHQ and LLM Gateway stacks up.
What Makes Each Tool Unique
🔵 Unique to BacklogHQ
Features available in BacklogHQ but not in LLM Gateway:
- ✓AgentDB — embedded AI-first database with runtime-discoverable persisted schemas
- ✓HNSW vector search, blob storage and real-time subscriptions exposed as MCP tools
- ✓Backlog — task management whose tasks survive across agent sessions
- ✓Ships as both MCP tools and Claude Code skills
- ✓Pure TypeScript, zero native dependencies, append-only WAL with immutable snapshots
🟣 Unique to LLM Gateway
Features available in LLM Gateway but not in BacklogHQ:
- ✓One OpenAI-compatible endpoint for 200+ models across 40+ providers
- ✓Real-time cost tracking and spend controls across every provider
- ✓Automatic provider routing plus custom routing rules
- ✓Prompt caching, guardrails and reliability failover
- ✓Bring-your-own-keys on the free tier
- ✓MCP server, AI SDK provider and a token cost calculator
Use Case Recommendations
Best for: BacklogHQ
BacklogHQ is a small suite of pure-TypeScript, zero-native-dependency components built to be used by AI agents as infrastructure rather than by humans through a UI. Two projects sit under it. AgentDB is an embedded database designed for LLM agents: schemas are persisted so an agent can discover them at runtime instead of being told about them in a prompt, and it ships HNSW vector search, blob storage and real-time subscriptions, all exposed as MCP tools. Backlog is persistent task management for Claude Code, where the point is that tasks outlive a single session — work started by one agent can be picked up later by another, and the whole thing is surfaced both as MCP tools and as Claude Code skills. The stated design principles explain the shape of both: agent-first, meaning the MCP servers and declarative schemas were written for how agents actually behave rather than retrofitted from human-facing libraries; zero native dependencies, so it runs anywhere Node.js does with no native binaries and no external database to stand up; and crash-safe by default, via an append-only write-ahead log, immutable snapshots and undo. For anyone building long-running agent workflows, the session-survival property is the differentiator — most agent memory disappears when the process does.
Ideal use cases:
- •Teams or individuals who need agentdb — embedded ai-first database with runtime-discoverable persisted schemas
- •Teams or individuals who need hnsw vector search, blob storage and real-time subscriptions exposed as mcp tools
- •Teams or individuals who need backlog — task management whose tasks survive across agent sessions
- •Teams or individuals who need ships as both mcp tools and claude code skills
- •Anyone focused on mcp workflows
- •Anyone focused on typescript workflows
Best for: LLM Gateway
LLM Gateway is an open-source, OpenAI-compatible routing layer that puts one API in front of more than 200 models across 40-plus providers, including OpenAI, Anthropic and Google. The problem it solves is operational rather than intellectual: teams that use several providers end up juggling separate keys, separate dashboards, separate billing and separate SDK quirks, and switching provider for a given model means a code change. Pointing an existing OpenAI-compatible client at the gateway collapses that into one endpoint with real-time cost tracking across all of it. Around the core routing sit the features that make a gateway worth running in production — automatic provider selection and custom routing rules, budgets and spend controls, prompt caching, guardrails, reliability failover, and activity logs with exportable data. The commercial model is unusual and worth reading carefully: the free tier is free forever with all 200+ models available and bring-your-own-keys included, and the platform fee is 5% on credit usage rather than a monthly subscription, so cost scales with spend rather than seats. Metadata retention is free while full payload storage is billed at $0.01 per million tokens. Supporting surfaces include an MCP server, an AI SDK provider, a token cost calculator, model comparison and rankings pages, and the vendor states it is SOC 2 Type II certified.
Ideal use cases:
- •Teams or individuals who need one openai-compatible endpoint for 200+ models across 40+ providers
- •Teams or individuals who need real-time cost tracking and spend controls across every provider
- •Teams or individuals who need automatic provider routing plus custom routing rules
- •Teams or individuals who need prompt caching, guardrails and reliability failover
- •Anyone focused on llm-gateway workflows
- •Anyone focused on api workflows
🤖 Other AI Agent Infrastructure Tools to Consider
BacklogHQ and LLM Gateway aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is BacklogHQ better than LLM Gateway?
It depends on your needs. BacklogHQ offers 5 key features including AgentDB — embedded AI-first database with runtime-discoverable persisted schemas and HNSW vector search, blob storage and real-time subscriptions exposed as MCP tools, while LLM Gateway provides 6 features including One OpenAI-compatible endpoint for 200+ models across 40+ providers and Real-time cost tracking and spend controls across every provider. BacklogHQ uses a free model with a free tier, while LLM Gateway is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is BacklogHQ cheaper than LLM Gateway?
BacklogHQ doesn't have standard paid plans, while LLM Gateway starts at $0.01/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use BacklogHQ and LLM Gateway together?
Yes, many users combine BacklogHQ and LLM Gateway in their workflow. BacklogHQ excels at agentdb — embedded ai-first database with runtime-discoverable persisted schemas, while LLM Gateway shines with one openai-compatible endpoint for 200+ models across 40+ providers. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between BacklogHQ and LLM Gateway?
While both are ai agent infrastructure tools, BacklogHQ emphasizes agentdb — embedded ai-first database with runtime-discoverable persisted schemas, whereas LLM Gateway is known for one openai-compatible endpoint for 200+ models across 40+ providers. The best choice depends on your specific workflow and feature priorities.
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