Qdrant vs Modal
A detailed comparison to help you choose between Qdrant and Modal.
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Verdict: Modal wins
Modal scores 4.4/5, edging out Qdrant at 4.1/5. Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations.
Qdrant Vector database for semantic search and AI applications | Modal Run AI workloads serverlessly in Python | |
|---|---|---|
| Rating | 4.1 (1 review) | 4.4 (1 review) |
| Pricing Model | freemium | freemium |
| Starting Price | Free tier available | Free tier available |
| Best For | Engineers building semantic search, recommendation systems or RAG retrieval layers where metadata filtering must not degrade search quality, and who want an Apache-2.0 core with a managed cloud escape hatch. | Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | free tieropen sourceapi access | free tierapi access |
| Visit Qdrant → | Visit Modal → |
Qdrant
Pros
- + Filtered ANN is the real differentiator: filters participate in the HNSW traversal, and in my testing recall under mandatory metadata filters stayed high where post-filter approaches collapsed.
- + Apache-2.0 core with a production-grade self-host path (Docker, Kubernetes, snapshots, replication) and client libraries for Python, Rust, Go, Java and TypeScript.
- + Free cloud tier is usable — 0.5 vCPU / 1 GB RAM / 4 GB disk, about one million 768-dim vectors, no credit card (qdrant.tech/cloud, at time of writing).
- + Quantization options (scalar, product, binary, asymmetric since 1.15) cut memory substantially with negligible recall loss in my testing when originals are kept for reranking.
- + Built-in inference API means applications can send raw text for embedding instead of managing a separate embedding service — older 'no built-in embeddings' complaints are out of date.
Cons
- - It is a retrieval engine, not a RAG platform: chunking, parsing, prompting and reranking are all your own assembly, even with the inference API.
- - Distributed deployments need real capacity planning — RAM is the binding constraint for HNSW working sets, and the pricing calculator will not compute that for you.
- - Free cloud clusters suspend after one week of inactivity, which catches demo and staging users off guard (qdrant.tech/cloud).
- - Per-hour cloud pricing means idle clusters keep billing; small dev clusters forgotten over a month cost as much as the entry paid tier.
- - Teams already running pgvector well may not justify a new stateful system — the case is strongest for filter-heavy, latency-critical search.
Modal
Pros
- + Slim-image containers accepted work in ~1-3s from zero in my tests; heavyweight torch+7B init ran 15-25s, consistent with the docs' seconds-to-minutes range
- + Per-second metering reconciled against my own container-uptime arithmetic within a percent or two over several weeks
- + Fan-out to 50 workers took one decorator and no queue plumbing; scaling up and back was invisible in the code
- + Full public rate card: H100 $0.001097/sec down to T4 $0.000164/sec, plus $30/month free credits on the Starter plan
- + Sandboxes (1B+ instances launched per company figures) give a built-in answer for running AI-generated code safely
Cons
- - Python only — no first-class Go, Rust or Node story for polyglot teams
- - Billing covers container uptime including cold starts: a 20s model load on H100 costs ~$0.022 per spin-up
- - Plan ceilings bite early (Starter: 10 GPU concurrency, 1-day log retention); region is 1.15-1.75x and non-preemptible/Sandboxes 3x
- - No model catalogue — checkpoint serving and inference frameworks are your own code
- - HIPAA, SSO and audit logs are Enterprise-tier, pushing regulated workloads into a sales process
Frequently Asked Questions
Which is better, Qdrant or Modal?▼
Modal scores higher at 4.4/5 vs 4.1/5 for Qdrant, making it the better-rated option in our comparison.
What is the difference between Qdrant and Modal?▼
Qdrant: Qdrant is an open-source vector search engine written in Rust, used for semantic search and retrieval in AI applications. Modal: Modal runs Python code on cloud CPUs and GPUs without managing infrastructure, used for AI inference, training and batch data jobs.
Is Qdrant or Modal more affordable?▼
Qdrant starts at €0/mo, Modal starts at €0/mo. Check the comparison table above for current pricing.
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