Decision Surface
Structured comparison pages for framework, infrastructure, and operating-model decisions. These are built to help you choose, not to pad the archive.
Comparison
OpenAI, Anthropic, and Gemini batch APIs all discount 50% with a 24-hour window - but the request limits differ, and a rented H100 running vLLM beats them only in specific regimes. The break-even math against current GPT-5.4-family and Claude batch prices, with sources.
Comparison
Adopt Claude Code's harness as a library or own the loop yourself: what the Agent SDK ships (compaction, subagents, hooks, built-in tools), its measured ~14k-18k-token static overhead, the paused June 2026 billing split, and the 'what you still owe' matrix for the DIY path.
Comparison
Which speculator actually wins in production: independent 500-prompt benchmark numbers (EAGLE-3 2.89x, Medusa-2 2.21x, draft model 1.95x, prompt-lookup 1.38x), the ~0.5 acceptance-rate break-even where speculation turns net negative, and when to run no speculator at all.
Comparison
Choosing the isolation tier for untrusted agent-generated code: Firecracker microVMs (125 ms boots, <5 MiB overhead) vs gVisor's userspace kernel vs hardened containers - with the runc escape CVEs, gVisor's own published throughput numbers, and the map of which sandbox products run on what.
Comparison
Is graph indexing worth ~1000x the indexing cost? Microsoft's own published numbers - 70-80% win rates on global synthesis, 2.3x higher query latency, LazyGraphRAG at 0.1% of the indexing cost - and when plain pgvector-style retrieval is the right answer.
Comparison
Scheduled acyclic DAGs (Airflow 3.x) vs stateful cyclic graphs (LangGraph 1.x) for AI agent workflows - Airflow's acyclicity constraint and new 3.1 human-in-the-loop operators, LangGraph's interrupt/checkpoint model, and why most production systems end up running both.
Comparison
HKU's lightweight dual-level graph RAG vs Microsoft's community-summary pipeline: the paper's real retrieval numbers (<100 tokens and 1 API call vs ~610k tokens in its GraphRAG comparison), the archived accelerator, graphrag's new incremental update command, and when plain vector RAG beats both.
Comparison
The two OSS instrumentation standards for LLM and agent traces emit different span schemas - OTel gen_ai.* vs openinference.* - and your observability backend decides which one you can actually use. Backend-by-backend ingestion matrix, including the one vendor that explicitly rejects OpenInference.
Comparison
The gen_ai.* conventions are still experimental in July 2026 - and just moved to their own repo. When portable OTel instrumentation beats a richer vendor tracing SDK, which backends actually ingest gen_ai.* today, and the attribute renames that will break you.
Comparison
When to split prefill and decode onto separate GPU pools (NVIDIA Dynamo, SGLang P/D, vLLM, llm-d) vs running chunked prefill on one pool - with NVIDIA's own ISL:OSL thresholds, DistServe's 7.4x goodput numbers, and the honest 1.4-1.8x reality for typical workloads.
Comparison
Cache a long stable prefix at 0.1x input price, or chunk-and-retrieve per loop iteration? The break-even is a simple ratio almost nobody states: caching wins on cost only when your corpus is under ~10x your would-be retrieved context. Worked math at three corpus sizes, from current vendor prices.
Comparison
Config-driven CLI harness vs pytest-native metric framework for CI eval gates - with the fact most comparisons miss: OpenAI acquired Promptfoo in March 2026. What that means for standardizing, verified Confident AI pricing, and when to run both.
Comparison
Type-safe Python-native graphs vs the canonical state-graph runtime: the real checkpointing and durable-execution delta - pydantic-graph's FileStatePersistence and Temporal/DBOS/Prefect integrations vs LangGraph's thread-checkpointer model with time travel - verified against both projects' current docs.
Comparison
Interleaved think-act-observe vs plan-upfront-then-execute is a cost decision as much as an architecture one: ReAct's context grows every step while planners batch the expensive calls. The published numbers (LLMCompiler's 6.7x cost claim), the token math, and who should choose what.
Comparison
Which control-flow model fits your agent: cyclic state graphs (LangGraph-style), acyclic DAG pipelines (Airflow-style), or explicit finite state machines - with the documented limits of each (no cycles in DAGs, LangGraph's recursion_limit, FSM state explosion) and when a plain chain still wins.
Comparison
Score every tool call and step order, or only the final outcome? A framework support matrix (DeepEval, AgentEvals, Phoenix, LangSmith, Galileo, OpenAI Evals) plus the cost multiplier nobody publishes: step-level LLM-judge evals cost roughly 30-60x more per test case at July 2026 judge pricing (GPT-5.4 mini, Claude Haiku 4.5).
Comparison
For loop-heavy agent traffic the decisive variable is prefix-cache hit rate, not headline tokens/sec. Where SGLang's RadixAttention actually wins, where the engines are within 2-4% of each other, and when TensorRT-LLM beats both.
Comparison
AgentCore's $0.0895/vCPU-hour consumption billing vs self-hosting on E2B, Modal, or your own sandboxes - priced per agent-hour, including the idle-time billing nuance that flips the naive comparison, and what the Bedrock Agents Classic freeze means.
Comparison
Managed browser infrastructure (Browserbase) vs an MIT-licensed agent framework with cloud tiers (Browser Use) - compared on concurrency, stealth, and the uncomfortable fact that LLM tokens, not browser infra, dominate your bill.
Comparison
Subscription quota ($20 Pro / $100 Max / $200 Max 20x) vs metered API credits for Claude Code and Agent SDK workloads - where the break-even sits, and what the announced-then-paused June 15, 2026 billing split means for how you should architect.
Comparison
Lightweight memory API (Mem0) vs temporal-knowledge-graph memory backend (Zep) - real price sheets, the LOCOMO benchmark dispute with exact numbers, and when rolling your own on pgvector beats both.
Comparison
AutoGen users must pick a successor: Microsoft's Agent Framework (1.0 GA April 2, 2026) or the community AG2 fork (v0.14.0 June 2026, v1.0 beta July 2026). A migration-effort and platform-alignment comparison with real release data.
Comparison
When to leave vanilla pgvector, and whether the next step is pgvectorscale on Postgres or Pinecone - decided by vector count, p95 latency, and monthly cost, with the 50M-vector Timescale benchmark numbers and the serverless pricing math that changes the verdict.
Comparison
Which open-weight model to run locally for agent workloads in 2026, decided by VRAM budget: measured tok/s and GGUF sizes for Qwen3.5-35B-A3B and GLM-4.7-Flash on 24GB GPUs, why MiniMax-M2.5 needs ~96GB+, and when an API call beats all three.
Comparison
A practical comparison for builders choosing between LangGraph Deep Agents and the OpenAI Agents SDK for long-running, durable, production-grade agent systems.
Comparison
A practical comparison for operators deciding between OpenClaw's private-first always-on agent infrastructure and Claude Code 2.1's async subagent terminal environment.
Comparison
A practical comparison for teams deciding whether agent memory should stay inside Postgres or move into a dedicated vector database - with real latency benchmarks and hybrid search tradeoffs.