Dakera vs Mem0: your memory, your bill, your servers
Mem0 is a managed memory platform with an open-source library and a self-hosted server behind it. Dakera is one self-hosted binary that runs its models on-device. The real questions are who holds the memories, who charges per request, and what you have to run.
Reviewed October 2026
Estimate your monthly bill
Mem0 Platform bills by tier, set by how many memory adds and retrievals you make each month. A self-hosted Dakera costs what your server costs. Enter your own numbers.
Worked examples (turn on JavaScript for the interactive version). The Dakera column uses an example server at $40/month.
| Scenario | Adds / retrievals per month | Mem0 Platform tier | Dakera (your server) |
|---|---|---|---|
| Prototype | 5,000 / 500 | Hobby, $0 | $40 flat |
| Small product | 40,000 / 4,000 | Starter, $19 | $40 flat |
| Growing product | 200,000 / 20,000 | Pro, $249 | $40 flat |
| Busy agent fleet | 2,000,000 / 200,000 | Enterprise, custom | $40 flat |
Example only: $40/mo for 4 vCPU / 8 GiB. Replace it with what you pay.
Mem0 tiers and quotas are from mem0.ai/pricing (Hobby: 10k adds and 1k retrievals, free; Starter: $19 at 50k and 5k; Pro: $249 at 500k and 50k; Enterprise: custom). Mem0 also supports usage-based pricing for traffic that does not fit a tier. Dakera has no usage fees; the server figure is yours and is not a Dakera price. The estimate leaves out your own time to operate a server.
Where your data goes
Where memories are stored, and which services see their text, differs across the Mem0 options and Dakera.
The same two calls, side by side
Both are a few lines of Python. Mem0 takes conversation messages and processes them into memories; Dakera stores the content you give it and ranks it on-device.
from mem0 import MemoryClient
client = MemoryClient(api_key=KEY)
client.add(
[{"role": "user",
"content": "Vegetarian, no nuts"}],
user_id="user123",
)
results = client.search(
"Dietary restrictions?",
filters={"user_id": "user123"},
)
print(results)from dakera import DakeraClient
client = DakeraClient(
"http://localhost:3000",
api_key="my-dev-key",
)
client.store_memory(
agent_id="user123",
content="Vegetarian, no nuts",
importance=0.8,
)
response = client.recall(
agent_id="user123",
query="Dietary restrictions?",
top_k=5,
)
for m in response.memories:
print(m.content, m.score)Dakera: pip install dakera; see the Python SDK and Quickstart. Mem0: pip install mem0ai.
Capabilities at a glance
| Dakera | Mem0 | |
|---|---|---|
| Hosting | Self-hosted: Docker, Kubernetes, systemd | Managed platform; open-source library and self-hosted server |
| Retrieval | HNSW vector + BM25, RRF fusion, cross-encoder reranker | Semantic, BM25 keyword and entity matching, optional reranker |
| Graph and decay | On-device entity graph (5 edge types, BFS) and 6 decay curves, in the server | Graph memory and decay are Platform features, not in open source |
| MCP | 14 core tools (100+ via profiles) | Hosted MCP server, 11 tools |
| SDKs and licence | Python, TypeScript, Go, Rust (MIT); server is proprietary with no usage fees | Python, TypeScript; Apache 2.0 |
| LoCoMo | 88.2% Recall@20: does retrieval surface the answer (1,536 questions). Method | 92.5 as reported by Mem0: QA accuracy, does an LLM answer correctly. Different measurements |
What Dakera v0.12.0 adds
- Multilingual: bge-m3 embeddings across 100+ languages, full-text stemming in 18, and a per-request
lang. See Multilingual. - Multimodal: file attachments, on-device speech to text and visual recall over document pages. See Multimodal.
- Retrieval you can inspect: every response carries a
rerank_report, and the dashboard shows why each memory ranked where it did. - Operations:
dakera downgradeconverts data back to v0.11.108, with health probes,dakera --check-configand encrypted backups. Every v0.12.0 feature is opt-in.
When to choose Mem0, and when Dakera
Choose Mem0 if
- You want memory running today with an API key and no servers; its Platform operates the vector store, LLM and embedder.
- Your volume sits inside the Hobby or Starter quotas, where a tier costs less than a server.
- You want Platform-only ranking features: graph memory, decay and temporal reasoning.
- Your team is on Python or TypeScript and is comfortable sending text to an LLM provider.
Choose Dakera if
- Memories must stay on infrastructure you control.
- Request volume is high or unpredictable and you want a flat bill.
- You need multilingual or multimodal memory, or Go and Rust SDKs.
- You want ranking you can inspect and tune, with reranking, decay and a dashboard built in.
Prefer managed? Dakera Cloud is coming. Join the waitlist.
Frequently asked questions
Can I run Mem0 on my own servers too?
Yes. Mem0 has an open-source library and a self-hosted server (Docker Compose with Postgres and pgvector). It needs an LLM provider key; OpenAI is the default. Platform-only features such as graph memory and decay are not part of the open-source version. See Dakera vs Mem0 self-hosting.
How do I move from Mem0 to Dakera?
Export your memories from Mem0, start Dakera with the Quickstart command, and import them with the batch memory endpoint. Dakera embeds on-device, so you do not carry vectors across.
What server do I need?
The default models use about 0.65 GB of container memory after reranked recalls, measured in a 4 CPU / 8 GiB container on v0.12.0. Stored memories add to that as they grow.
How are the LoCoMo numbers different?
Dakera reports Recall@20, whether the answer is among the top 20 retrieved memories. Mem0 reports QA accuracy, whether an LLM answers correctly from what was retrieved. They answer different questions.
Run Dakera on your own infrastructure.
One binary with its models baked in and no per-call fees. Start it with one Docker command and see it for yourself.
Prefer managed hosting? Join the Dakera Cloud waitlist →