Sie vs DBOS
Side-by-side comparison of features, pricing, and ratings
Sie or DBOS: which should you choose?
| Dimension | Sie | DBOS |
|---|---|---|
| What it is | Self-hosted Kubernetes inference cluster for small agent models | Durable execution library running inside your Postgres |
| Infra you must run | Kubernetes (EKS/GKE/AKS) plus GPUs | A Postgres database you already own |
| Core value | 89% GPU efficiency benchmark vs 51% for worker-local queues by batching mixed request sizes | Checkpoint + replay of workflows so crashes and redeploys don't destroy work |
| Languages / models | 138 supported models; SGLang, vLLM, TensorRT-LLM, TEI, PyTorch, Candle backends | TypeScript, Python, Go, Java; Rust first look announced 2026-06-15 |
| Bad fit when | You have no Kubernetes/GPU experience or your workload is bursty and low-volume | You're not on Postgres or need multi-region active-active out of the box |
SIE serves small open models for embeddings, reranking, OCR, and extraction on your GPUs or its API
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DBOS adds durable workflow orchestration to your app or AI agent using the Postgres database you already run.
Visit WebsiteWho should pick Sie, and who should pick DBOS?
- Agent engineer whose runs die mid-flightPick: DBOS
Checkpointing and replay from last completed step, plus durable queues with concurrency limits, fix lost work without adding a queue or orchestrator.
- Backend team already running PostgresPick: DBOS
Annotate TypeScript, Python, Go or Java functions in place and consolidate job queues, schedulers and orchestration into the database you already operate.
- Developer shipping approval-gated workflowsPick: DBOS
Durable send/recv and durable sleep let a human-in-the-loop step pause for days or weeks across restarts and redeploys.
- Search/RAG team with steady per-token billsPick: Sie
Self-hosted embeddings and cross-encoder reranking on shared GPUs removes per-token pricing for high-volume pipelines; bursty low volume should stay hosted.
- Regulated team needing air-gapped document processingPick: Sie
OCR, extraction and summarization run on your own EKS/GKE/AKS or air-gapped cluster, so prompts and documents never leave your cloud.
Frequently Asked Questions
Could I run DBOS and Sie together?
Yes, and it's a common shape for agent platforms — DBOS keeps the orchestration durable while Sie serves the embeddings, rerankers and OCR behind it. They occupy different layers rather than overlapping.
Which one should I trial first?
Trial whichever matches your current pain, not the other product. If runs lose work on crashes, stand up DBOS against an existing Postgres this week. If the bill or a data-residency rule is the blocker, scope a Sie cluster on Kubernetes.
Does either offer a fully managed option today?
Not in a self-serve sense. DBOS Cloud is contact-sales only, and Managed SIE is still a waitlist — plan for self-hosting on both sides.
What's the biggest hidden cost on each side?
For DBOS it's the $99/mo Pro to $499/mo Teams jump once you need more seats or apps. For Sie it's GPUs and Kubernetes expertise — a real hire if your team doesn't already run clusters.
Can I avoid GPUs entirely with Sie?
No. Sie runs models on your own hardware, which is the point; if you want to avoid infrastructure, hosted per-token APIs remain the alternative Superlinked itself points bursty, low-volume users toward.
How current are these products?
Both ship fast. DBOS added a Rust first look on 2026-06-15 and has a July 2026 product recap; Sie published FlashNorm inference work and fresh Qwen3 embedding/reranker benchmarks in 2026, including 0.6B vs 4B and ColQwen variants.
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