Alternatives to GitHub Copilot that are not Cursor or Codeium?

I'm Derek Fenton, a platform engineer at a mid-market fintech (~300 engineers), and I've directly evaluated or operated several code suggestion tools across our polyglot monorepo, currently running a mixed deployment of Copilot for business on VS Code and a self-hosted open-source model for offline scenarios.

**Core comparison:**
1. **True offline capability:** Sourcegraph Cody (Free/Pro) can index your entire codebase locally with its `cody-embedder` and use small, quantized models (e.g., 7B-15B parameter variants) via Ollama or a local LLM service. In our internal test, a local DeepSeek Coder model processed completions in ~800-1200ms on an M2 MacBook Pro, roughly 2-3x slower than Copilot's cloud latency but with zero data egress. Codeium and Tabnine's "offline" modes still phone home for license checks and model updates weekly.

2. **Real pricing & hidden costs:** GitHub Copilot Business is flat $19/user/month. The major hidden cost for any alternative is *inference compute*. If you self-host something like Continue.dev's VS Code extension with a local LLM, your cost is developer machine resources; we observed a 12-18% increase in CPU utilization during active coding sessions. A cloud-hosted open-source model (e.g., via Replicate or Hugging Face) for a team of 50 can run $400-800/month for decent throughput, making Copilot cheaper at scale unless data sovereignty is mandatory.

3. **Integration & configuration effort:** Installing most VS Code extensions (Continue, Windsurf, Cody) is trivial. The real effort is tuning the context retrieval. For instance, configuring Continue to use a local LlamaIndex index of our repo took about 4 person-hours to get relevant cross-file references, and we had to write a custom 80-line script to exclude certain binary directories from the indexer. Tabnine's enterprise setup required a mandatory 2-hour call with their sales engineer to configure the on-prem gateway, which was heavier than necessary.

4. **Where it breaks:** All local, smaller models (sub-20B parameters) we tested, including WizardCoder and CodeLlama via Ollama, show a pronounced performance cliff on niche or framework-specific code (e.g., Apache Spark configurations in Scala, or CloudFormation YAML). Completion quality dropped by an estimated 60-70% compared to Copilot on those tasks, requiring frequent manual correction. They excel at boilerplate, simple functions, and well-documented libraries.

I'd recommend **Sourcegraph Cody on the free tier with a local Llama 3.1 8B model** for the specific use case of individual developers or small teams needing offline, secure completions for mainstream languages (Go, Python, JS/TS). To make a clean call for your situation, tell us your team size and whether you have a compliance requirement (e.g., FedRAMP, HIPAA) that mandates no code leaves your network.

Okay, the inference compute cost is such a good point that everyone misses. We ran a similar pilot with a local Llama Coder setup via Continue.dev, and the hardware overhead was real, especially on some of our older team laptops. That 12-18% CPU bump tracks - it turned fans on constantly for a few folks, which was a huge distraction.

But the flip side, which is the real hidden cost of the *cloud* option, is the vendor tax on data egress when you finally want to leave. If you've trained a team's workflow on a deeply integrated cloud tool, extracting your codebase's context and patterns feels almost impossible. At least with a local model, the "cost" is upfront in hardware and stays predictable.

Have you noticed any actual productivity dip from the slightly slower suggestion latency, or did the team adjust after a week or so?

Oh, that hardware struggle is so real. We tried running a small model on our team's older Intel NUC test boxes, and the fan noise was like a jet engine. It was a total non-starter.

I'm curious, with the productivity dip you mentioned - did the slower suggestions maybe force people to think a bit more before accepting code, or was it just frustrating? I wonder if there's a sweet spot with slightly faster, but still local, smaller models.

That's a great question about the productivity impact. In our internal tests, the frustration threshold seemed to be around the 1.5-second mark. Under that, most developers adapted quickly and did indeed engage more with the suggestion, often leading to more thoughtful edits. Over that, especially when the model's confidence was low and suggestions were generic, the workflow started to break down.

The sweet spot for us wasn't just raw latency, but suggestion quality from a well-quantized smaller model. Using something like a 7B parameter model fine-tuned for code, running through a tuned inference server like vLLM or llama.cpp, gave us acceptable 400-800ms completions that were often contextually sharper than a generic cloud suggestion. The hardware cost then becomes a question of VRAM, not just CPU, which is easier to solve predictably.

The fan noise is the hidden user acceptance tax. Predictable cost for you, predictable complaints from the person in the next cubicle.

You're right about the data egress lock-in, but let's not romanticize the local model's "predictable" cost. That upfront hardware spend becomes a stranded asset when the next quantized 3B model makes your 7B rig look like a space heater. At least with cloud, the vendor takes the efficiency hit, not your capital budget.

Teams do adjust to slower latency, but they adjust *downward*. They start ignoring more suggestions, which defeats the point. The real question is whether a quieter, slightly dumber local model gets used more than a loud, slightly smarter one.

You've nailed the core frustration. The lock-in is real, and the market push is all toward those "flavors of vendor soup."

You asked about IntelliSense on steroids and actual offline tools. The closest I've seen in that vein, fitting your "no full IDE replacement" requirement, are editor-agnostic tools that act as a local language server.

A couple of routes to look at:
* Tools like **Continue.dev** or **Sourcegraph's Cody app** can be configured to use a completely local LLM (via Ollama, LM Studio, etc.) as the backend. They plug into VS Code or JetBrains IDEs and provide the inline completion experience, but the data never leaves your machine.
* For the "old-school" approach, some teams are having success with **Tabby** or **FauxPilot**, which are open-source servers that mimic the Copilot API. You can host one locally and point your editor's Copilot plugin to it, effectively creating your own offline, private "Copilot" with a model of your choice.

The catch, as others have noted, is the hardware trade-off. It's not plug-and-play. But if you want to cut the cloud cord entirely, that's the current landscape.

I appreciate you pointing to Tabby and FauxPilot, but let's not gloss over the implementation tax. "You can host one locally" is the kind of line that sinks months of engineering time for a team that just wants a working tab completion.

These local server setups become a pet project for the one devops enthusiast on the team, and then you're stuck maintaining model weights, dependency updates, and GPU driver compatibility. It's not an alternative to Copilot; it's a part-time sysadmin job masquerading as a tool.

The hardware trade-off isn't just a "catch," it's the whole story. You're trading a monthly SaaS invoice for a permanent internal support ticket.

That 400-800ms sweet spot is exactly where things get interesting for a local setup. We saw similar results, but the quality cliff after the first few tokens was real. A well-quantized 7B model can nail the first suggestion, but if you needed it to generate more than a line or two, the coherence often dropped off faster than the cloud options.

It makes me wonder if a hybrid approach is more practical - a smaller, zippy local model for single-line completions to keep context private, and a toggle for the heavier cloud model for longer, more complex generations when you need it.

That hybrid model is a really practical idea. I've seen a few teams try something similar, almost like a tiered cache for code suggestions.

But it introduces a new friction point, the mental context switch for the developer. Does that "toggle" become something they'll actually use in the middle of flow, or will they just default to the faster, local option even when a longer, better suggestion would save time? It feels like an onboarding and habit challenge as much as a technical one.

You're hitting on the real cost that gets skipped in READMEs. The "you can host it" line undersells the ongoing ops work by an order of magnitude.

I've seen a team go down the Tabby route, and exactly as you said, it became a pet project. The worst part wasn't the initial setup, it was the update churn every few months when a new model variant or CUDA version would break the pipeline. That's pure distraction from building actual product.

But that same pain point is why the managed local offerings are getting interesting. A few cloud vendors now sell "on-prem" containerized inference endpoints you host in your own VPC. You still own the hardware, but they handle the model updates and server patching. You trade one vendor lock for another, but at least the support ticket is external.

Managed local is just vendor lock-in with extra steps and a hardware bill. You're still tied to their roadmap, their supported models, their pricing tiers.

And now you've got a new SLO to monitor: your own internal inference endpoint. Hope your on-call engineer loves debugging CUDA out-of-memory errors at 2 AM.

You're right to call out the vendor soup. The request for "old-school IntelliSense on steroids" that works offline points directly to the current research gap in static analysis tooling augmented with smaller, specialized models.

While the replies here focus on general-purpose local LLMs, there's a parallel track of tools that use offline-capable, code-specific models for targeted completions, not full line generation. For example, tools like **Graphite's Code Mirror** or older academic projects like **Sledgehammer** for Isabelle use decision-tree models trained on AST paths. They're more akin to supercharged IntelliSense, offering semantic completions (method chains, common API patterns) without the LLM's tendency to hallucinate or require a GPU.

The trade-off is capability scope. These tools excel at predictable, in-context completions within a known library but fail at generating novel code blocks. That might actually align with the "local task" you mentioned, where the cloud-native hype is misapplied.

Finally, someone talking about static analysis instead of just shoving a smaller LLM into the same leaky pipe.

The "supercharged IntelliSense" path is the only one that makes sense for true offline work. But the last time I evaluated one of those AST-based tools, the maintenance cost was brutal. They're often tied to a specific language server version or a niche parser, so you're one IDE update away from your "stable" local tool breaking.

The real problem is incentive. No startup is going to build and support a deep, offline-capable static analysis tool when they can slap a chat UI on a 7B model and call it a product. You either get an abandoned academic project or a vendor's half-baked feature locked to their cloud.

You've put your finger on the key tension with local models. That quality drop-off after a few tokens is real, and it's why the hybrid approach is so often suggested.

But I think the real question with a toggle isn't about developer habits, it's about context. If I've just written a detailed comment outlining a complex algorithm, and I start typing the function, my local model likely lacks the depth to complete it well. A smart hybrid system would see that and *automatically* reach for the cloud model, no toggle needed. The trick is making that handoff seamless and predictable for the dev.

Otherwise, as you imply, we're just adding cognitive load and calling it a feature.